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Applied Sciences
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Machine Fault Diagnostics and Prognostics
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Machine Fault Diagnostics and Prognostics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (15 March 2020) | Viewed by 125330

Special Issue Editors

Prof. Dr. Jong-Myon Kim

E-Mail Website
Guest Editor
School of IT Convergence, University of Ulsan, Ulsan 4402, Republic of Korea
Interests: machine learning; signal processing; image processing; machine fault diagnosis and health prognosis; condition monitoring; deep learning; embedded systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cheol Hong Kim

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Soongsil University, Seoul 06978, Korea
Interests: fault diagnostics; health prognosis; mobile system design; machine learning; edge computing; embedded system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are currently living through the fourth Industrial revolution, which is riding on the wave of cutting-edge technologies in computing, artificial intelligence, and communications. The past decade has witnessed incredible advances in the field of artificial intelligence (AI) and has seen massive proliferation of cloud computing technologies. These technological advances have further fueled the integration of the cyber and the physical worlds, with intelligence and autonomy as its key hallmarks, which would lead to more reliable, productive, and efficient industries and businesses in the future.

Machines and mechanical structures in industries undergo inevitable degradation and loss of performance during operation. The timely diagnosis of symptoms of their degradation and a reliable estimate of their future health condition are essential for Industrial productivity and reliability. Models constructed from historical measurement data using AI techniques have shown great promise in fault diagnosis and prognosis of industrial equipment. AI-based techniques are poised to gain even more significance in the future as huge amounts of measurement data are to be available for decision making due to the deployment of the internet-of-things and cloud-based technologies for condition-based maintenance (CBM).

This Special Issue will focus on the topic of fault diagnosis and prognosis of industrial equipment and mechanical structures. We invite researchers and practicing engineers to contribute original research articles that discuss issues related but not limited to condition-based monitoring, fault diagnosis and prognosis of industrial machines and mechanical structures, diagnostic and prognostic techniques based on AI, such as deep learning, transfer learning, and neuro-fuzzy inference techniques, AI-based solutions that are explainable, solutions utilizing the Internet. of Things, cloud computing, cyber physical systems, and machine-to-machine interfaces and paradigms for fault diagnosis and prognosis in the context of Industry 4.0. We would also welcome review articles that capture the current state-of-the art and outline future areas of research in the fields relevant to this Special Issue.

Prof. Dr. Jong-Myon Kim
Prof. Dr. Cheol Hong Kim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Condition monitoring
  • Fault diagnosis
  • Health prognosis
  • Remaining useful life
  • Deep learning
  • Artificial intelligence
  • Condition-based maintenance
  • Cyber physical systems

Published Papers (33 papers)

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21 pages, 9525 KiB  
Article
A System-Level Failure Propagation Detectability Using ANFIS for an Aircraft Electrical Power System
by Cordelia Mattuvarkuzhali Ezhilarasu and Ian K Jennions
Appl. Sci. 2020, 10(8), 2854; https://doi.org/10.3390/app10082854 - 20 Apr 2020
Cited by 15 | Viewed by 4128
Abstract
The Electrical Power System (EPS) in an aircraft is designed to interact extensively with other systems. With a growing trend towards more electric aircraft, the complexity of interactions between the EPS and other systems has grown. This has resulted in an increased necessity [...] Read more.
The Electrical Power System (EPS) in an aircraft is designed to interact extensively with other systems. With a growing trend towards more electric aircraft, the complexity of interactions between the EPS and other systems has grown. This has resulted in an increased necessity of implementing health monitoring methods like diagnosis and prognosis of the EPS at the systems level. This paper focuses on developing a diagnostic algorithm for the EPS to detect and isolate faults and their root causes that occur at the Line Replaceable Units (LRUs) connecting with aircraft systems like the engine and the fuel system. This paper aims to achieve this in two steps: (i) developing an EPS digital twin and presenting the simulation results for both healthy and fault scenarios, (ii) developing an Adaptive Neuro-Fuzzy Inference System (ANFIS) monitor to detect faults in the EPS. The results from the ANFIS monitor are processed in two methods: (i) a crisp boundary approach, and (ii) a fuzzy boundary approach. The former approach has a poor misclassification rate; hence the latter method is chosen to combine with causal reasoning for isolating root causes of these interacting faults. The results from both these methods are presented through examples in this paper. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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10 pages, 2788 KiB  
Article
Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor
by Md Junayed Hasan, Jaeyoung Kim, Cheol Hong Kim and Jong-Myon Kim
Appl. Sci. 2020, 10(7), 2525; https://doi.org/10.3390/app10072525 - 06 Apr 2020
Cited by 13 | Viewed by 2526
Abstract
Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical [...] Read more.
Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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18 pages, 1340 KiB  
Article
Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model
by Jingli Yang, Yongqi Chang, Tianyu Gao and Jianfeng Wang
Appl. Sci. 2020, 10(6), 2056; https://doi.org/10.3390/app10062056 - 18 Mar 2020
Cited by 21 | Viewed by 2027
Abstract
A novel failure prediction method of the rotating machinery is presented in this paper. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to decompose the vibration signals of the rotating machinery into a number of intrinsic mode functions [...] Read more.
A novel failure prediction method of the rotating machinery is presented in this paper. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to decompose the vibration signals of the rotating machinery into a number of intrinsic mode functions (IMFs) and a residual (Res), and the metric of maximal information coefficient (MIC) is used to select eligible IMFs to reconstruct signals. Then, the approximate entropy (ApEn)-weighted energy value of the reconstructed signals are calculated to track the degradation process of the rotating machinery. Furthermore, the Chebyshev inequality is introduced to determine the prediction starting time (PST). Finally, the auto regress (AR) model and unscented Kalman filter (UKF) algorithm are used to predict the remaining useful life (RUL) of the rotating machinery. The method is fully evaluated in a test-to-failure experiment. The obtained results show that the proposed method outperforms its counterparts on failure prediction of the rotating machinery. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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12 pages, 6331 KiB  
Article
Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals
by JaeYoung Kim and Jong-Myon Kim
Appl. Sci. 2020, 10(6), 2050; https://doi.org/10.3390/app10062050 - 18 Mar 2020
Cited by 47 | Viewed by 5162
Abstract
Bearing failure generates impulses when the rolling elements pass the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect bearing failures operated in low-rotating speeds. However, since the high sampling rates of the AE [...] Read more.
Bearing failure generates impulses when the rolling elements pass the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect bearing failures operated in low-rotating speeds. However, since the high sampling rates of the AE signals make it difficult to design and extract discriminative fault features, deep neural network-based approaches have been proposed in several recent studies. This paper proposes a convolutional neural network (CNN)-based bearing fault diagnosis technique. In this work, the normalized bearing characteristic component (NBCC) is used as the input of CNN, which is an effective form of representing bearing failure symptoms. In addition, importance-weight is extracted using gradient-weighted class activation mapping (Grad-CAM) for visual explanation of CNN. In the experiment result, the proposed approach achieves high classification accuracy with reasonable visualization, which shows that CNN successfully learned the components of bearing characteristic frequency for each type of bearing failure. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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18 pages, 6395 KiB  
Article
Inter-turn Short Circuit Diagnosis Using New D-Q Synchronous Min–Max Coordinate System and Linear Discriminant Analysis
by Yeong-Jin Goh and Kyoung-Min Kim
Appl. Sci. 2020, 10(6), 1996; https://doi.org/10.3390/app10061996 - 14 Mar 2020
Cited by 2 | Viewed by 1880
Abstract
In this paper, a direct-quadrature (D-Q) synchronous min–max coordinate system is proposed (as a new method) for diagnosing the occurrence of inter-turn short circuits (ITSC) of three-phase induction motors, and it was found that this method can linearly diagnose such short circuits using [...] Read more.
In this paper, a direct-quadrature (D-Q) synchronous min–max coordinate system is proposed (as a new method) for diagnosing the occurrence of inter-turn short circuits (ITSC) of three-phase induction motors, and it was found that this method can linearly diagnose such short circuits using only the maximum value of the d-axis current component from the heavy load to the full load. In the diagnosis of ITSC, a method to perform linear discriminant analysis (LDA) efficiently was applied owing to the difficulty of linear separation under light load conditions. In the aforementioned method, time burden is generated because operations are performed for the entire data and between classes. However, the proposed method is useful even when it is applied to the entire load with only the LDA eigenvector of the minimum light load. This is proved by the graphical evaluation of the interaction between the false acceptance rate (FAR) and false recognition rate (FRR), and the results demonstrate that the proposed method is more efficient than existing LDA application methods. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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11 pages, 3902 KiB  
Article
Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline
by Bach Phi Duong, JaeYoung Kim, Inkyu Jeong, Cheol Hong Kim and Jong-Myon Kim
Appl. Sci. 2020, 10(6), 1933; https://doi.org/10.3390/app10061933 - 12 Mar 2020
Cited by 10 | Viewed by 2878
Abstract
Acoustic emission bursts are signal waveforms that include a number of consecutive imbrication transients with variable strengths and contain crucial information on the leakage phenomenon in a pipeline system. Detection and isolation of a burst against the background signal increases the ability of [...] Read more.
Acoustic emission bursts are signal waveforms that include a number of consecutive imbrication transients with variable strengths and contain crucial information on the leakage phenomenon in a pipeline system. Detection and isolation of a burst against the background signal increases the ability of a pipe’s fault diagnosis system. This paper proposes a methodology using the Enhanced Constant Fault Alarm Rate (ECFAR) to detect bursts and exploit the burst phenomenon in acoustic emission. The extracted information from the burst waveform is used to distinguish several levels of leakage in a laboratory leak-off experimental testbed. The multi-class support vector machine in the one-against-all method is established as the classifier. The results are compared with those of the wavelet threshold-based method, another algorithm utilized for impulse and burst detection, which indicates that the ECFAR method gives an ameliorative classification result with an accuracy of 93% for different levels of leakage. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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19 pages, 4284 KiB  
Article
The Rational Spline Interpolation Based-LOD Method and Its Application to Rotating Machinery Fault Diagnosis
by Xiaorui Niu, Kang Zhang, Chao Wan, Xiangmin Chen, Lida Liao and Zeyu Tian
Appl. Sci. 2020, 10(4), 1259; https://doi.org/10.3390/app10041259 - 13 Feb 2020
Viewed by 1972
Abstract
Local oscillatory-characteristic decomposition (LOD) is a relatively new self-adaptive time-frequency analysis methodology. The method, based on local oscillatory characteristics of the signal itself uses three mathematical operations such as differential, coordinate domain transform, and piecewise linear transform to decompose the multi-component signal into [...] Read more.
Local oscillatory-characteristic decomposition (LOD) is a relatively new self-adaptive time-frequency analysis methodology. The method, based on local oscillatory characteristics of the signal itself uses three mathematical operations such as differential, coordinate domain transform, and piecewise linear transform to decompose the multi-component signal into a series of mono-oscillation components (MOCs), which is very suitable for processing multi-component signals. However, in the LOD method, the computational efficiency and real-time processing performance of the algorithm can be significantly improved by the use of piecewise linear transformation, but the MOC component lacks smoothness, resulting in distortion. In order to overcome the disadvantages mentioned above, the rational spline function that spline shape can be adjusted and controlled is introduced into the LOD method instead of the piecewise linear transformation, and the rational spline-local oscillatory-characteristic decomposition (RS-LOD) method is proposed in this paper. Based on the detailed illustration of the principle of RS-LOD method, the RS-LOD, LOD, and empirical mode decomposition (EMD) are compared and analyzed by simulation signals. The results show that the RS-LOD method can significantly improve the problem of poor smoothness of the MOC component in the original LOD method. Moreover, the RS-LOD method is applied to the fault feature extraction of rotating machinery for the multi-component modulation characteristics of rotating machinery fault vibration signals. The analysis results of the rolling bearing and fan gearbox fault vibration signals show that the RS-LOD method can effectively extract the fault feature of the rotating mechanical vibration signals. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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17 pages, 4979 KiB  
Article
Fault Diagnosis of Diesel Engine Valve Clearance Based on Variational Mode Decomposition and Random Forest
by Nanyang Zhao, Zhiwei Mao, Donghai Wei, Haipeng Zhao, Jinjie Zhang and Zhinong Jiang
Appl. Sci. 2020, 10(3), 1124; https://doi.org/10.3390/app10031124 - 07 Feb 2020
Cited by 17 | Viewed by 8788
Abstract
Diesel engines, as power equipment, are widely used in the fields of the automobile industry, ship industry, and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in [...] Read more.
Diesel engines, as power equipment, are widely used in the fields of the automobile industry, ship industry, and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in the time domain and angular domain, on which the current diagnosis methods are based, are easily affected by working conditions or are hard to extract accurate enough from, as the diesel engine keeps running in transient and non-stationary processes. This work aimed at diagnosing this fault mainly based on frequency band features, which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively, a decomposition technique based on improved variational mode decomposition was investigated in this work. As the connection between the features and the fault was fuzzy, the random forest algorithm was used to analyze the correspondence between features and faults. In addition, the feature dimension was reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition showed that the method based on variational mode decomposition and random forest was capable to detect the valve clearance fault effectively. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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16 pages, 5552 KiB  
Article
Improved Empirical Wavelet Transform for Compound Weak Bearing Fault Diagnosis with Acoustic Signals
by Chaoren Qin, Dongdong Wang, Zhi Xu and Gang Tang
Appl. Sci. 2020, 10(2), 682; https://doi.org/10.3390/app10020682 - 18 Jan 2020
Cited by 29 | Viewed by 2623
Abstract
Most of the current research on the diagnosis of rolling bearing faults is based on vibration signals. However, the location and number of sensors are often limited in some special cases. Thus, a small number of non-contact microphone sensors are a suboptimal choice, [...] Read more.
Most of the current research on the diagnosis of rolling bearing faults is based on vibration signals. However, the location and number of sensors are often limited in some special cases. Thus, a small number of non-contact microphone sensors are a suboptimal choice, but it will result in some problems, e.g., underdetermined compound fault detection from a low signal-to-noise ratio (SNR) acoustic signal. Empirical wavelet transform (EWT) is a signal processing algorithm that has a dimension-increasing characteristic, and is beneficial for solving the underdetermined problem with few microphone sensors. However, there remain some critical problems to be solved for EWT, especially the determination of signal mode numbers, high-frequency modulation and boundary detection. To solve these problems, this paper proposes an improved empirical wavelet transform strategy for compound weak bearing fault diagnosis with acoustic signals. First, a novel envelope demodulation-based EWT (DEWT) is developed to overcome the high frequency modulation, based on which a source number estimation method with singular value decomposition (SVD) is then presented for the extraction of the correct boundary from a low SNR acoustic signal. Finally, the new fault diagnosis scheme that utilizes DEWT and SVD is compared with traditional methods, and the advantages of the proposed method in weak bearing compound fault diagnosis with a single-channel, low SNR, variable speed acoustic signal, are verified. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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16 pages, 3230 KiB  
Article
A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching
by Jiancheng Yin, Huailiang Zheng, Yuantao Yang and Minqiang Xu
Appl. Sci. 2020, 10(2), 673; https://doi.org/10.3390/app10020673 - 18 Jan 2020
Cited by 1 | Viewed by 1969
Abstract
The life prediction is crucial to guarantee the reliability and safety of the mechanical system. The current prediction methods predict the life only based on the historical usage pattern of the mechanical system, and do not consider the mission profile of the future [...] Read more.
The life prediction is crucial to guarantee the reliability and safety of the mechanical system. The current prediction methods predict the life only based on the historical usage pattern of the mechanical system, and do not consider the mission profile of the future working process. To realize the life prediction with considering the switching of mission profile, which is composed of different operating conditions, this paper proposes a new prediction scheme on the base of the similarity trajectory method (STM). Two main improvements are employed. First, the reference degradation models are constructed according to the predicted trend of each constant operating condition obtained by the relevance vector machine (RVM). Secondly, the life under specific mission profile is calculated through weighted aggregating the life of each constant operating condition. The proposed method is validated by a turbofan engine simulation data. The results show that the proposed method achieves an excellent predicted result, in which the predicted result is close to the actual result. In addition, the proposed method can deal with the problem of mission profile switching. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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9 pages, 919 KiB  
Article
Adaptive State Observer for Robot Manipulators Diagnostics and Health Degree Assessment
by Sanlei Dang, Zhengmin Kong, Long Peng, Yilin Ji and Yongwang Zhang
Appl. Sci. 2020, 10(2), 514; https://doi.org/10.3390/app10020514 - 10 Jan 2020
Cited by 5 | Viewed by 2064
Abstract
To avoid serious damages caused by the dynamic environment, fault detection and health assessment are essential for an integrated robotic system. In this paper, we propose a fault detection algorithm and a health degree assessment approach for a robot manipulator system. Both the [...] Read more.
To avoid serious damages caused by the dynamic environment, fault detection and health assessment are essential for an integrated robotic system. In this paper, we propose a fault detection algorithm and a health degree assessment approach for a robot manipulator system. Both the internal disturbance and the output measurement disturbance are considered in the proposed method. In addition, an adaptive observer is utilized to reconstruct the real system of robot manipulators. Under the proposed observer, the real system is estimated to detect the fault and obtain the health degree of the robot manipulator. The feasibility and reliability of the proposed fault detection algorithm and health degree assessment index for robot manipulator systems are proved by simulation experiments. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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16 pages, 2835 KiB  
Article
A Novel Intelligent Method for Bearing Fault Diagnosis Based on EEMD Permutation Entropy and GG Clustering
by Jingbao Hou, Yunxin Wu, Hai Gong, A. S. Ahmad and Lei Liu
Appl. Sci. 2020, 10(1), 386; https://doi.org/10.3390/app10010386 - 04 Jan 2020
Cited by 39 | Viewed by 2708
Abstract
For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the fault category. A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), and the Gath–Geva [...] Read more.
For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the fault category. A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), and the Gath–Geva (GG) clustering algorithm is proposed. Firstly, we decompose the vibration signal using EEMD, and several inherent modal components are obtained. Then, the permutation entropy values of each modal component are calculated to get the entropy feature vector, and the entropy feature vector is reduced by the LDA method to be used as the input of the clustering algorithm. The data experiments show that the proposed fault diagnosis method can obtain satisfactory clustering indicators. It implies that compared with other mode combination methods, the fault identification method proposed in this study has the advantage of better intra-class compactness of clustering results. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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20 pages, 2306 KiB  
Article
A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction
by Hung-Cuong Trinh and Yung-Keun Kwon
Appl. Sci. 2020, 10(1), 368; https://doi.org/10.3390/app10010368 - 03 Jan 2020
Cited by 14 | Viewed by 3601
Abstract
Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and [...] Read more.
Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine learning. In general, the best performing algorithm and the optimal hyper-parameters suitable for each subtask are varied across the characteristics of datasets. Therefore, it is challenging to develop a general diagnostic/prognostic framework that can automatically identify the best subtask algorithms and the optimal involved parameters for a given dataset. To resolve this problem, we propose a new framework based on an ensemble of genetic algorithms (GAs) that can be used for both the fault-type classification and RUL prediction. Our GA is combined with a specific machine-learning method and then tries to select the best algorithm and optimize the involved parameter values in each subtask. In addition, our method constructs an ensemble of various prediction models found by the GAs. Our method was compared to a traditional grid-search over three benchmark datasets of the fault-type classification and the RUL prediction problems and showed a significantly better performance than the latter. Taken together, our framework can be an effective approach for the fault-type and RUL prediction of various machinery systems. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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20 pages, 4069 KiB  
Article
Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period
by Haixu Jiang, Ke Zhang, Jingyu Wang, Xianyu Wang and Pengfei Huang
Appl. Sci. 2020, 10(1), 103; https://doi.org/10.3390/app10010103 - 20 Dec 2019
Cited by 13 | Viewed by 4237
Abstract
To effectively detect and identify the anomaly data in massive satellite telemetry data sets, the novel detection and identification method based on the pseudo-period was proposed in this paper. First, the raw data were compressed by extracting the shape salient points. Second, the [...] Read more.
To effectively detect and identify the anomaly data in massive satellite telemetry data sets, the novel detection and identification method based on the pseudo-period was proposed in this paper. First, the raw data were compressed by extracting the shape salient points. Second, the compressed data were symbolized by the tilt angle of the adjacent data points. Based on this symbolization, the pseudo-period of the data was extracted. Third, the phase-plane trajectories corresponding to the pseudo-period data were obtained by using the pseudo-period as the basic analytical unit, and then, the phase-plane was divided into statistical regions. Finally, anomaly detection and identification of the raw data were achieved by analyzing the statistical values of the phase-plane trajectory points in each partition region. This method was verified by a simulation test that used the measured data of the satellite momentum wheel rotation. The simulation results showed that the proposed method could achieve the pseudo-period extraction of the measured data and the detection and identification of the anomalous telemetry data. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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22 pages, 3043 KiB  
Article
Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer
by Farzin Piltan, Alexander E. Prosvirin, Inkyu Jeong, Kichang Im and Jong-Myon Kim
Appl. Sci. 2019, 9(24), 5404; https://doi.org/10.3390/app9245404 - 10 Dec 2019
Cited by 35 | Viewed by 3683
Abstract
Rotating machines represent a class of nonlinear, uncertain, and multiple-degrees-of-freedom systems that are used in various applications. The complexity of the system’s dynamic behavior and uncertainty result in substantial challenges for fault estimation, detection, and identification in rotating machines. To address the aforementioned [...] Read more.
Rotating machines represent a class of nonlinear, uncertain, and multiple-degrees-of-freedom systems that are used in various applications. The complexity of the system’s dynamic behavior and uncertainty result in substantial challenges for fault estimation, detection, and identification in rotating machines. To address the aforementioned challenges, this paper proposes a novel technique for fault diagnosis of a rolling-element bearing (REB), founded on a machine-learning-based advanced fuzzy sliding mode observer. First, an ARX-Laguerre algorithm is presented to model the bearing in the presence of noise and uncertainty. In addition, a fuzzy algorithm is applied to the ARX-Laguerre technique to increase the system’s modeling accuracy. Next, the conventional sliding mode observer is applied to resolve the problems of fault estimation in a complex system with a high degree of uncertainty, such as rotating machinery. To address the problem of chattering that is inherent in the conventional sliding mode observer, the higher-order super-twisting (advanced) technique is introduced in this study. In addition, the fuzzy method is applied to the advanced sliding mode observer to improve the accuracy of fault estimation in uncertain conditions. As a result, the advanced fuzzy sliding mode observer adaptively improves the reliability, robustness, and estimation accuracy of rolling-element bearing fault estimation. Then, the residual signal delivered by the proposed methodology is split in the windows and each window is characterized by a numerical parameter. Finally, a machine learning technique, called a decision tree, adaptively derives the threshold values that are used for problems of fault detection and fault identification in this study. The effectiveness of the proposed algorithm is validated using a publicly available vibration dataset of Case Western Reverse University. The experimental results show that the machine learning-based advanced fuzzy sliding mode observation methodology significantly improves the reliability and accuracy of the fault estimation, detection, and identification of rolling element bearing faults under variable crack sizes and load conditions. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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15 pages, 3240 KiB  
Article
Deep Learning Object-Impulse Detection for Enhancing Leakage Detection of a Boiler Tube Using Acoustic Emission Signal
by Bach Phi Duong, Jaeyoung Kim, Cheol-Hong Kim and Jong-Myon Kim
Appl. Sci. 2019, 9(20), 4368; https://doi.org/10.3390/app9204368 - 16 Oct 2019
Cited by 8 | Viewed by 3108
Abstract
Advances in technology have enhanced the ability to detect leakages in boiler tube components in thermal power plants. As a specific issue, the interaction between the coal fuel stream and the boiler tube membrane generates random and high-amplitude impulses, which negatively affect the [...] Read more.
Advances in technology have enhanced the ability to detect leakages in boiler tube components in thermal power plants. As a specific issue, the interaction between the coal fuel stream and the boiler tube membrane generates random and high-amplitude impulses, which negatively affect the measured acoustic emission (AE) signal from leakages. It is essential to detect and practically handle these kinds of impulses. Based on the object detection concept, this paper proposes an impulse detection methodology that employs deep learning flexible boundary regression (DLFBR). First, the shape extraction (SE) preprocessing technique is implemented to yield the shape signal, which contains intrinsic information about the impulse from the raw AE signal. Then, DLFBR extracts and generates both the feature map and the confidence mask from the shape signal to regress a boundary box, which specifies the position of the impulse. For illustration purposes, the proposed algorithm is applied to an experimental leakage detection dataset recorded from a subcritical boiler unit with a tube membrane. Experimental results show that the proposed method is effective for detecting impulses of leakage in a boiler tube testbed, providing 99.8% average classification accuracy. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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13 pages, 4512 KiB  
Article
Labeling Confidence Values for Wafer-Handling Robot Arm Performance Using a Feature-Based General Regression Neural Network and Genetic Algorithm
by Yi-Cheng Huang, Zi-Sheng Yang and Hsien-Shu Liao
Appl. Sci. 2019, 9(20), 4241; https://doi.org/10.3390/app9204241 - 11 Oct 2019
Cited by 4 | Viewed by 2055
Abstract
The prognosis and management of machine health statuses are emerging research topics. In this study, the performance degradation of a wafer-handling robot arm (WHRA) was predicted using the proposed machine-learning approach. This method considers the eccentric vertical and planar position deviations from a [...] Read more.
The prognosis and management of machine health statuses are emerging research topics. In this study, the performance degradation of a wafer-handling robot arm (WHRA) was predicted using the proposed machine-learning approach. This method considers the eccentric vertical and planar position deviations from a wafer mark using a charge-coupled device (CCD) camera. Synthesized position signals were defined using the square root of x- and y-axes deviations in the horizontal view and the square of the wafer mark diameter in the vertical view. A feature extraction method was used to determine the position status on the basis of these displacements and the area of a wafer mark in a CCD image. The root mean square error and mean, maximum, and minimum of the synthesized position signals were extracted through feature extraction and used for data mining by a general regression neural network (GRNN) and logistic regression (LR) models. The lifetime assessment by confidence value of the WHRA’s remaining useful life (RUL) by the genetic algorithm/GRNN exhibited nearly the same trend as that predicted through a run-to-failure LR model. The experimental results indicated that the proposed methodology can be used for proactive assessments of the RUL of WHRAs. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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18 pages, 1295 KiB  
Article
Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics
by Zhengmin Kong, Yande Cui, Zhou Xia and He Lv
Appl. Sci. 2019, 9(19), 4156; https://doi.org/10.3390/app9194156 - 03 Oct 2019
Cited by 89 | Viewed by 5389
Abstract
Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven [...] Read more.
Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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26 pages, 7655 KiB  
Article
An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient
by Xincheng Cao, Binqiang Chen, Bin Yao and Shiqiang Zhuang
Appl. Sci. 2019, 9(18), 3912; https://doi.org/10.3390/app9183912 - 18 Sep 2019
Cited by 31 | Viewed by 3814
Abstract
Tool wear and breakage are inevitable due to the severe stress and high temperature in the cutting zone. A highly reliable tool condition monitoring system is necessary to increase productivity and quality, reduce tool costs and equipment downtime. Although many studies have been [...] Read more.
Tool wear and breakage are inevitable due to the severe stress and high temperature in the cutting zone. A highly reliable tool condition monitoring system is necessary to increase productivity and quality, reduce tool costs and equipment downtime. Although many studies have been conducted, most of them focused on single-step process or continuous cutting. In this paper, a high robust milling tool wear monitoring methodology based on 2-D convolutional neural network (CNN) and derived wavelet frames (DWFs) is presented. The frequency band of high signal-to-noise ratio is extracted via derived wavelet frames, and the spectrum is further folded into a 2-D matrix to train 2-D CNN. The feature extraction ability of the 2-D CNN is fully utilized, bypassing the complex and low-portability feature engineering. The full life test of the end mill was carried out with S45C steel work piece and multiple sets of cutting conditions. The recognition accuracy of the proposed methodology reaches 98.5%, and the performance of 1-D CNN as well as the beneficial effects of the DWFs are verified. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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15 pages, 5203 KiB  
Article
An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors
by Dong Zhen, Zuolu Wang, Haiyang Li, Hao Zhang, Jie Yang and Fengshou Gu
Appl. Sci. 2019, 9(18), 3902; https://doi.org/10.3390/app9183902 - 17 Sep 2019
Cited by 22 | Viewed by 2509
Abstract
Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the [...] Read more.
Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency αmax that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist’s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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25 pages, 7629 KiB  
Article
Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD
by Xiaolong Wang, Xiaoli Yan and Yuling He
Appl. Sci. 2019, 9(18), 3706; https://doi.org/10.3390/app9183706 - 06 Sep 2019
Cited by 17 | Viewed by 2207
Abstract
The fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward [...] Read more.
The fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward to extract and enhance the fault feature effectively. In this diagnosis method, the fast spectral coherence is fused with the equal step size search strategy for the cyclic frequency parameter and the filter length parameter optimization, and a new frequency weighted energy entropy (FWEE) indicator which combining the advantages of the frequency weighted energy operator (FWEO) and the Shannon entropy, is developed for deconvolution signal evaluation during parameter optimization process. In addition, a novel singular value order determination approach based on fitting error minimum principle is utilized by SVDD to enhance the fault feature. During the process of defect identification, OCYCBD with the optimal parameters is firstly used to recover the informative source from the collected vibration signal. FWEO is further utilized to highlight the potential impulsive characteristics, and the instantaneous energy signal of deconvolution result can be acquired. The whole interferences contained in the instantaneous energy signal can’t be removed due to the weak fault signature and the severe background noise. Then, SVDD is applied to purify the instantaneous energy signal of deconvolution signal, by which the residual interference component is eliminated and the fault feature is strengthened immensely. Finally, frequency domain analysis is performed on the denoised instantaneous energy signal, and the defect location identification of wind turbine bearing can be achieved through analyzing the obvious spectral lines in the obtained enhanced energy spectrum. The collected signals from the experimental platform and the engineering field are both utilized to verify the feasibility of proposed method, and its superiority is further demonstrated through comparing with several well known diagnosis methods. The results indicate this novel method has distinct advantage on bearing weak feature extraction and enhancement. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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26 pages, 13105 KiB  
Article
Feature Extraction of Impulse Faults for Vibration Signals Based on Sparse Non-Negative Tensor Factorization
by Lin Liang, Haobin Wen, Fei Liu, Guang Li and Maolin Li
Appl. Sci. 2019, 9(18), 3642; https://doi.org/10.3390/app9183642 - 04 Sep 2019
Cited by 8 | Viewed by 2690
Abstract
The incipient damages of mechanical equipment excite weak impulse vibration, which is hidden, almost unobservable, in the collected signal, making fault detection and failure prevention at the inchoate stage rather challenging. Traditional feature extraction techniques, such as bandpass filtering and time-frequency analysis, are [...] Read more.
The incipient damages of mechanical equipment excite weak impulse vibration, which is hidden, almost unobservable, in the collected signal, making fault detection and failure prevention at the inchoate stage rather challenging. Traditional feature extraction techniques, such as bandpass filtering and time-frequency analysis, are suitable for matrix processing but challenged by the higher-order data. To tackle these problems, a novel method of impulse feature extraction for vibration signals, based on sparse non-negative tensor factorization is presented in this paper. Primarily, the phase space reconstruction and the short time Fourier transform are successively employed to convert the original signal into time-frequency distributions, which are further arranged into a three-way tensor to obtain a time-frequency multi-aspect array. The tensor is decomposed by sparse non-negative tensor factorization via hierarchical alternating least squares algorithm, after which the latent components are reconstructed from the factors by the inverse short time Fourier transform and eventually help extract the impulse feature through envelope analysis. For performance verification, the experimental analysis on the bearing datasets and the swashplate piston pump has confirmed the effectiveness of the proposed method. Comparisons to the traditional methods, including maximum correlated kurtosis deconvolution, singular value decomposition, and maximum spectrum kurtosis, also suggest its better performance of feature extraction. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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11 pages, 4186 KiB  
Article
Matching Quality Detection System of Synchronizer Ring and Cone
by Wanfu Li, Yong Chen, Xueru Li and Siyuan Liang
Appl. Sci. 2019, 9(17), 3622; https://doi.org/10.3390/app9173622 - 03 Sep 2019
Cited by 3 | Viewed by 7728
Abstract
A synchronizer is a key component in automotive transmission. It is necessary to detect the matching quality between a synchronizer ring and cone. For this purpose, a friction torque based detection system of matching quality between a synchronizer ring and cone is designed [...] Read more.
A synchronizer is a key component in automotive transmission. It is necessary to detect the matching quality between a synchronizer ring and cone. For this purpose, a friction torque based detection system of matching quality between a synchronizer ring and cone is designed in this paper. In the system, the acceptance criteria are established by the residual sum of squares (RSS), and the quality of the synchronizer is determined by measuring the friction torque and backup gap. This synchronizer ring and cone matching quality detection system has been implemented. The system is mainly used for quality detection of the synchronizer ring and cone in the automobile gearbox before packing. It improves the consistency of the synchronizer ring and synchronizer cone, which makes the synchronizer lighter and more reliable during shifting of the gearbox. According to market research, the system designed and implemented in this paper is advanced and original. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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18 pages, 6247 KiB  
Article
Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault Diagnosis
by Changqing Shen, Jiaqi Xie, Dong Wang, Xingxing Jiang, Juanjuan Shi and Zhongkui Zhu
Appl. Sci. 2019, 9(16), 3374; https://doi.org/10.3390/app9163374 - 16 Aug 2019
Cited by 30 | Viewed by 3225
Abstract
Rotating machinery plays a vital role in modern mechanical systems. Effective state monitoring of a rotary machine is important to guarantee its safe operation and prevent accidents. Traditional bearing fault diagnosis techniques rely on manual feature extraction, which in turn relies on complex [...] Read more.
Rotating machinery plays a vital role in modern mechanical systems. Effective state monitoring of a rotary machine is important to guarantee its safe operation and prevent accidents. Traditional bearing fault diagnosis techniques rely on manual feature extraction, which in turn relies on complex signal processing and rich professional experience. The collected bearing signals are invariably complicated and unstable. Deep learning can voluntarily learn representative features without a large amount of prior knowledge, thus becoming a significant breakthrough in mechanical fault diagnosis. A new method for bearing fault diagnosis, called improved hierarchical adaptive deep belief network (DBN), which is optimized by Nesterov momentum (NM), is presented in this research. The frequency spectrum is used as inputs for feature learning. Then, a learning rate adjustment strategy is applied to adaptively select the descending step length during gradient updating, combined with NM. The developed method is validated by bearing vibration signals. In comparison to support vector machine and the conventional DBN, the raised approach exhibits a more satisfactory performance in bearing fault type and degree diagnosis. It can steadily and effectively improve convergence during model training and enhance the generalizability of DBN. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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14 pages, 2335 KiB  
Article
Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
by Donggil Kim and Dongik Lee
Appl. Sci. 2019, 9(16), 3329; https://doi.org/10.3390/app9163329 - 13 Aug 2019
Cited by 11 | Viewed by 2165
Abstract
Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation [...] Read more.
Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation of fault parameters is reformulated as the state estimation problem by augmenting the parameters as an additional state. The novelty of the proposed method lies in the use of an adaptive fuzzy fading algorithm for the adaptive Kalman filter so that the convergence property during the estimation of fault parameter can be improved. The performance of the proposed method is evaluated through a set of numerical simulations with both linear and non-linear models. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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14 pages, 5529 KiB  
Article
Spring Failure Analysis of Mining Vibrating Screens: Numerical and Experimental Studies
by Yue Liu, Guoying Meng, Shuangfu Suo, Dong Li, Aiming Wang, Xiaohan Cheng and Jie Yang
Appl. Sci. 2019, 9(16), 3224; https://doi.org/10.3390/app9163224 - 07 Aug 2019
Cited by 9 | Viewed by 4042
Abstract
Spring failure is one of the critical causes of the structural damage and low screening efficiency of mining vibrating screens. Therefore, spring failure diagnosis is necessary to prompt maintenance for the safety and reliability of mining vibrating screens. In this paper, a spring [...] Read more.
Spring failure is one of the critical causes of the structural damage and low screening efficiency of mining vibrating screens. Therefore, spring failure diagnosis is necessary to prompt maintenance for the safety and reliability of mining vibrating screens. In this paper, a spring failure diagnosis approach is developed. A finite element model of mining vibrating screens is established. Simulations are carried out and the spring failure influence rules of spring failure on the dynamic characteristics of mining vibrating screens are obtained. These influence rules indicate that the amplitude variation coefficients (AVCs) of the four spring seats in the x, y, and z directions can reveal two kinds of single spring failure and four kinds of double spring failure, which are useful for diagnosing spring failure. Furthermore, experiments are conducted. Comparison analyses of the experimental results and simulation results indicate that the proposed approach is capable of revealing various kinds of spring failure. Therefore, this approach provides useful information for diagnosing spring failure and guiding technical staff to routinely maintain mining vibrating screens. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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13 pages, 7400 KiB  
Article
Effect of Multiple Factors on Identification and Diagnosis of Skidding Damage in Rolling Bearings under Time-Varying Slip Conditions
by Junning Li, Wuge Chen, Jiafan Xue, Ka Han and Qian Wang
Appl. Sci. 2019, 9(15), 3033; https://doi.org/10.3390/app9153033 - 27 Jul 2019
Cited by 6 | Viewed by 2818
Abstract
Skidding damage mechanism of rolling bearings is not clear, due to the influence of various coupling factors. To solve this problem, it is important to identify and diagnose skidding damage and study the vibration characteristics in rolling bearings. Based on Fast Fourier Transform [...] Read more.
Skidding damage mechanism of rolling bearings is not clear, due to the influence of various coupling factors. To solve this problem, it is important to identify and diagnose skidding damage and study the vibration characteristics in rolling bearings. Based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT), vibration signals of rolling bearings are extracted and analyzed, and then the skidding damage of rolling bearings from multiple signals perspectives is identified. The relationship between the variation in the radial load, temperature, slip and the skidding damage of rolling bearings under time-varying slip conditions is analyzed comprehensively, and then the influence of different factors on bearing skidding damage is studied. The integrated analysis of the vibration, load, temperature, slip rate and other multivariate signals information shows the starting time of skidding damage. This research can be conducive to reduce vibration and prolong the life of rolling bearings. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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21 pages, 4534 KiB  
Article
CNN-Based Fault Localization Method Using Memory-Updated Patterns for Integration Test in an HiL Environment
by Ki-Yong Choi and Jung-Won Lee
Appl. Sci. 2019, 9(14), 2799; https://doi.org/10.3390/app9142799 - 12 Jul 2019
Viewed by 2928
Abstract
Automotive electronic components are tested via hardware-in-the-loop (HiL) testing at the unit and integration test stages, according to ISO 26262. It is difficult to obtain debugging information from the HiL test because the simulator runs a black-box test automatically, depending on the scenario [...] Read more.
Automotive electronic components are tested via hardware-in-the-loop (HiL) testing at the unit and integration test stages, according to ISO 26262. It is difficult to obtain debugging information from the HiL test because the simulator runs a black-box test automatically, depending on the scenario in the test script. At this time, debugging information can be obtained in HiL tests, using memory-updated information, without the source code or the debugging tool. However, this method does not know when the fault occurred, and it is difficult to select the starting point of debugging if the execution flow of the software is not known. In this paper, we propose a fault-localization method using a pattern in which each memory address is updated in the HiL test. Via a sequential pattern-mining algorithm in the memory-updated information of the transferred unit tests, memory-updated patterns are extracted, and the system learns using a convolutional neural network. Applying the learned pattern in the memory-updated information of the integration test can determine the fault point from the normal pattern. The point of departure from the normal pattern is highlighted as a fault-occurrence time, and updated addresses are presented as fault candidates. We applied the proposed method to an HiL test of an OSEK/VDX-based electronic control unit. Through fault-injection testing, we could find the cause of faults by checking the average memory address of 3.28%, and we could present the point of fault occurrence with an average accuracy of 80%. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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26 pages, 12803 KiB  
Article
Adaptive Fuzzy-Based Fault-Tolerant Control of a Continuum Robotic System for Maxillary Sinus Surgery
by Farzin Piltan, Cheol-Hong Kim and Jong-Myon Kim
Appl. Sci. 2019, 9(12), 2490; https://doi.org/10.3390/app9122490 - 19 Jun 2019
Cited by 8 | Viewed by 6716
Abstract
Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that are biologically inspired. Because of their flexibility and accuracy, these robots can be used in maxillary sinus surgery. The design of an effective procedure with high accuracy, reliability, robust fault diagnosis, and [...] Read more.
Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that are biologically inspired. Because of their flexibility and accuracy, these robots can be used in maxillary sinus surgery. The design of an effective procedure with high accuracy, reliability, robust fault diagnosis, and fault-tolerant control for a surgical robot for the sinus is necessary to maintain the high performance and safety necessary for surgery on the maxillary sinus. Thus, a robust adaptive hybrid observation method using an adaptive, fuzzy auto regressive with exogenous input (ARX) Laguerre Takagi–Sugeno (T–S) fuzzy robust feedback linearization observer for a surgical robot is presented. To address the issues of system modeling, the fuzzy ARX-Laguerre technique is represented. In addition, a T–S fuzzy robust feedback linearization observer is applied to a fuzzy ARX-Laguerre to improve the accuracy of fault estimation, reliability, and robustness for the surgical robot in the presence of uncertainties. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy observation-based feedback linearization technique is presented. The effectiveness of the proposed algorithm is tested with simulations. Experimental results show that the proposed method reduces the average position error from 35 mm to 2.45 mm in the presence of faults. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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12 pages, 3077 KiB  
Article
Data Driven Leakage Detection and Classification of a Boiler Tube
by Muhammad Sohaib and Jong-Myon Kim
Appl. Sci. 2019, 9(12), 2450; https://doi.org/10.3390/app9122450 - 15 Jun 2019
Cited by 20 | Viewed by 3681
Abstract
Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. [...] Read more.
Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. In this study, a boiler tube leak detection and classification mechanism was designed using wavelet packet transform (WPT) analysis of the acoustic emission (AE) signals acquired from the boiler tube and a fully connected deep neural network (FC-DNN). WPT analysis of the AE signals enabled the extraction of features associated with the different conditions of the boiler tube, that is, normal and leak conditions. The deep neural network (DNN) effectively explores the salient information from the wavelet packet features through a deep architecture instead of considering shallow networks, such as k-nearest neighbors (k-NN) and support vector machines (SVM). This enhances the classification performance of the leak identification and classification model developed. The proposed model yielded a 99.2 % average classification accuracy when tested with AE signals from the boiler tube. The experimental results prove the efficacy of the proposed model for boiler tube leak detection and classification. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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23 pages, 2048 KiB  
Article
A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
by Yinsheng Chen, Tinghao Zhang, Zhongming Luo and Kun Sun
Appl. Sci. 2019, 9(11), 2356; https://doi.org/10.3390/app9112356 - 08 Jun 2019
Cited by 47 | Viewed by 3834
Abstract
To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is [...] Read more.
To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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Review

Jump to: Research

24 pages, 930 KiB  
Review
Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review
by Arantxa Contreras-Valdes, Juan P. Amezquita-Sanchez, David Granados-Lieberman and Martin Valtierra-Rodriguez
Appl. Sci. 2020, 10(3), 950; https://doi.org/10.3390/app10030950 - 01 Feb 2020
Cited by 20 | Viewed by 4874
Abstract
Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as [...] Read more.
Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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32 pages, 5174 KiB  
Review
On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
by Moussa Hamadache, Saikat Dutta, Osama Olaby, Ramakrishnan Ambur, Edward Stewart and Roger Dixon
Appl. Sci. 2019, 9(23), 5129; https://doi.org/10.3390/app9235129 - 27 Nov 2019
Cited by 60 | Viewed by 9808
Abstract
Railway switch and crossing (S&C) systems have a very complex structure that requires not only a large number of components (such as rails, check rails, switches, crossings, turnout bearers, slide chair, etc.) but also different types of components and technologies (mechanical devices to [...] Read more.
Railway switch and crossing (S&C) systems have a very complex structure that requires not only a large number of components (such as rails, check rails, switches, crossings, turnout bearers, slide chair, etc.) but also different types of components and technologies (mechanical devices to operate switches, electrical and/or electronic devices for control, etc.). This complexity of railway S&C systems makes them vulnerable to failures and malfunctions that can ultimately cause delays and even fatal accidents. Thus, it is crucial to develop suitable condition monitoring techniques to deal with fault detection and diagnosis (FDD) in railway S&C systems. The main contribution of this paper is to present a comprehensive review of the existing FDD techniques for railway S&C systems. The aim is to overview the state of the art in rail S&C and in doing so to provide a platform for researchers, railway operators, and experts to research, develop and adopt the best methods for their applications; thereby helping ensure the rapid evolution of monitoring and fault detection in the railway industry at a time of the increased interest in condition based maintenance and the use of high-speed trains on the rail network. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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