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Applied Sciences
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Machine Fault Diagnostics and Prognostics Volume III
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Machine Fault Diagnostics and Prognostics Volume III

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 10157

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
Dr. Farzin Piltan

E-Mail Website
Guest Editor
Ulsan Industrial Artificial Intelligence (UIAI) Lab, Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan, Korea
Interests: fault diagnosis; prognosis; control; machine learning; deep learning
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 a 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 made 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; cyberphysical 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
Dr. Farzin Piltan
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 (7 papers)

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Research

24 pages, 7289 KiB  
Article
Diagnosing Cracks in the Injector Nozzles of Marine Internal Combustion Engines during Operation Using Vibration Symptoms
by Jan Monieta
Appl. Sci. 2023, 13(17), 9599; https://doi.org/10.3390/app13179599 - 24 Aug 2023
Cited by 2 | Viewed by 893
Abstract
In the operation of internal combustion engines, despite technical state monitoring, some cracks that develop in metal components go undetected, leading to secondary, critical, or degradation damage. The diagnostic systems used in floating objects mainly use quasi-static thermodynamic signals, which alert operators too [...] Read more.
In the operation of internal combustion engines, despite technical state monitoring, some cracks that develop in metal components go undetected, leading to secondary, critical, or degradation damage. The diagnostic systems used in floating objects mainly use quasi-static thermodynamic signals, which alert operators too late about emerging damage. Although various methods have been developed to detect cracks in internal combustion engine components, the effectiveness and implementation of the proposed methods are not satisfactory. Therefore, this article presents the use of selected vibration and in-cylinder pressure signals to diagnose the development of damage in some components of marine diesel engines. The investigations were conducted under the natural conditions of the operation of sea-going vessels during port-handling operations. During these investigations, it was possible to observe clear changes in the values of diagnostic symptoms, which corresponded to the development of damage. The developing damage detected in the study involved cracks in injector nozzles manufactured from alloy steel. Despite advances in design, materials, and manufacturing technology, injector nozzle cracks still occur. The diagnostic symptoms used to detect damage development were the amplitude and spectral and wavelet measurements of vibration acceleration signals. This work aimed to search for crack-oriented methods of signal analysis, for example, computer visualization and the recording of diagnostic parameters in various domains. Decimation, windowed, time, amplitude, and time-frequency domain analyses; wavelet statistics; color analysis; and machine learning were used for classification using artificial neural networks. Experimental investigations showed the possibility of diagnosing the development processes of damage to marine diesel engines. The advanced signal processing methods used made it possible to obtain many signal measurements, from which the most useful diagnostic symptoms were selected. The new symptoms found with decimation, time-domain windowed analysis, and Haar wavelet statistics were more useful than the existing ones. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics Volume III)
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17 pages, 3363 KiB  
Article
Composite Fault Diagnosis of Rolling Bearings: A Feature Selection Approach Based on the Causal Feature Network
by Kuo Gao, Zongning Wu, Chongchong Yu, Mengxiong Li and Sihan Liu
Appl. Sci. 2023, 13(16), 9089; https://doi.org/10.3390/app13169089 - 09 Aug 2023
Cited by 1 | Viewed by 729
Abstract
A rolling bearing is a complex system consisting of the inner race, outer race, rolling element, etc. The interaction of components may lead to composite faults. Selecting the features that can accurately identify the fault type from the composite fault features with causality [...] Read more.
A rolling bearing is a complex system consisting of the inner race, outer race, rolling element, etc. The interaction of components may lead to composite faults. Selecting the features that can accurately identify the fault type from the composite fault features with causality among components is key to composite fault diagnosis. To tackle this issue, we propose a feature selection approach for composite fault diagnosis based on the causal feature network. Based on the incremental association Markov blanket discovery, we first use the algorithm to mine the causal relationships between composite fault features and construct the causal feature network. Then, we draw upon the nodes’ centrality indicators in the complex network to quantify the importance of composite fault features. We also propose the criteria for threshold selection to determine the number of features in the optimal feature subset. Experimental results on the standard dataset for composite fault diagnosis show that our approach of using the causal relationship between features and the nodes’ centrality indicators of complex network can effectively identify the key features in composite fault signals and improve the accuracy of composite fault diagnosis. Experimental results thus verify our approach’s effectiveness. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics Volume III)
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25 pages, 8238 KiB  
Article
Computation and Statistical Analysis of Bearings’ Time- and Frequency-Domain Features Enhanced Using Cepstrum Pre-Whitening: A ML- and DL-Based Classification
by David Cascales-Fulgencio, Eduardo Quiles-Cucarella and Emilio García-Moreno
Appl. Sci. 2022, 12(21), 10882; https://doi.org/10.3390/app122110882 - 27 Oct 2022
Cited by 4 | Viewed by 1827
Abstract
Vibration signals captured with an accelerometer carry essential information about Rolling Element Bearings (REBs) faults in rotating machinery, and the envelope spectrum has proven to be a robust tool for their diagnosis at an early stage of development. In this paper, Cepstrum Pre-Whitening [...] Read more.
Vibration signals captured with an accelerometer carry essential information about Rolling Element Bearings (REBs) faults in rotating machinery, and the envelope spectrum has proven to be a robust tool for their diagnosis at an early stage of development. In this paper, Cepstrum Pre-Whitening (CPW) has been applied to REBs’ signals to enhance and extract health-state condition indicators from the preprocessed signals’ envelope spectra. These features are used to train some of the state-of-the-art Machine Learning (ML) algorithms, combined with time-domain features such as basic statistics, high-order statistics and impulsive metrics. Before training, these features were ranked according to statistical techniques such as one-way ANOVA and the Kruskal–Wallis test. A Convolutional Neural Network (CNN) has been designed to implement the classification of REBs’ signals from a Deep Learning (DL) point of view, receiving raw time signals’ greyscale images as inputs. The different ML models have yielded validation accuracies of up to 87.6%, while the CNN yielded accuracy of up to 77.61%, for the entire dataset. In addition, the same models have yielded validation accuracies of up to 97.8%, while the CNN, 90.67%, where signals from REBs with faulty balls have been removed from the dataset, highlighting the difficulty of classifying such faults. Furthermore, from the results of the different ML algorithms compared to those of the CNN, frequency-domain features have proven to be highly relevant condition indicators combined with some time-domain features. These models can be potentially helpful in applications that require early diagnosis of REBs faults, such as wind turbines, vehicle transmissions and industrial machinery. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics Volume III)
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18 pages, 6211 KiB  
Article
Experimental Analysis of the Current Sensor Fault Detection Mechanism Based on Cri Markers in the PMSM Drive System
by Kamila Jankowska and Mateusz Dybkowski
Appl. Sci. 2022, 12(19), 9405; https://doi.org/10.3390/app12199405 - 20 Sep 2022
Cited by 3 | Viewed by 1517
Abstract
In this paper the current sensor fault detector for the permanent-magnet synchronous motor drive system has been presented. The solution is a known method used for induction motor drive systems, tested by authors in simulation for the PMSM drive system. The application is [...] Read more.
In this paper the current sensor fault detector for the permanent-magnet synchronous motor drive system has been presented. The solution is a known method used for induction motor drive systems, tested by authors in simulation for the PMSM drive system. The application is based on the current markers, which enable not only failure detection, but also the location of said failures. Detector operation is based only on the analysis of measurements from current sensors and does not require additional information about other state variables. The aim of the work is to present simulation and experimental studies in field-oriented control (FOC) for the tested current sensor fault detector for various operating conditions of the drive system—variable speed and load. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics Volume III)
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30 pages, 10583 KiB  
Article
Improved Variational Mode Decomposition and One-Dimensional CNN Network with Parametric Rectified Linear Unit (PReLU) Approach for Rolling Bearing Fault Diagnosis
by Xiaofeng Wang, Xiuyan Liu, Jinlong Wang, Xiaoyun Xiong, Suhuan Bi and Zhaopeng Deng
Appl. Sci. 2022, 12(18), 9324; https://doi.org/10.3390/app12189324 - 17 Sep 2022
Cited by 7 | Viewed by 1398
Abstract
As a critical component of rotating machinery, rolling bearings are essential for the safe and efficient operation of machinery. Sudden faults of rolling bearings can lead to unscheduled downtime and substantial economic costs. Therefore, diagnosing and identifying the equipment status is essential for [...] Read more.
As a critical component of rotating machinery, rolling bearings are essential for the safe and efficient operation of machinery. Sudden faults of rolling bearings can lead to unscheduled downtime and substantial economic costs. Therefore, diagnosing and identifying the equipment status is essential for ensuring the operation and decreasing the additional maintenance costs of the machines. However, extracting the features from the early bearing fault signals is challenging under background noise interference. With the purpose of solving the above problem, we propose an integrated rolling bearing fault diagnosis model based on the improved grey wolf optimized variational modal decomposition (IGVMD) and an improved 1DCNN with a parametric rectified linear unit (PReLU). Firstly, an improved grey wolf optimizer (IGWO) with the fitness function, the minimum envelope entropy, is designed for adaptively searching the optimal parameter values of the VMD model. The performance of the basic grey wolf optimizer (GWO) algorithm by introducing three improvement strategies, the non-linear convergence factor adjustment strategy, the grey wolf adaptive position update strategy, and the Levy flight strategy in the IGWO algorithm, is improved. Then, an improved 1DCNN model with the PReLU activation function is proposed, which extracts the bearing fault features, and a grid search to optimize the model parameters of the 1DCNN is introduced. Finally, the effectiveness of the proposed model is demonstrated well by employing two experimental datasets. The preliminary comparative results of the average identification accuracy in the proposed method in two datasets are 99.98% and 99.50%, respectively, suggesting that this proposed method has a relatively higher recognition accuracy and application values. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics Volume III)
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24 pages, 6801 KiB  
Article
Bearing Crack Diagnosis Using a Smooth Sliding Digital Twin to Overcome Fluctuations Arising in Unknown Conditions
by Farzin Piltan, Cheol-Hong Kim and Jong-Myon Kim
Appl. Sci. 2022, 12(13), 6770; https://doi.org/10.3390/app12136770 - 04 Jul 2022
Cited by 5 | Viewed by 1544
Abstract
Bearings cause the most breakdowns in induction motors, which can result in significant economic losses. If faults in the bearings are not detected in time, they can cause the whole system to fail. System failures can lead to unexpected breakdowns, threats to worker [...] Read more.
Bearings cause the most breakdowns in induction motors, which can result in significant economic losses. If faults in the bearings are not detected in time, they can cause the whole system to fail. System failures can lead to unexpected breakdowns, threats to worker safety, and huge economic losses. In this investigation, a new approach is proposed for fault diagnosis of bearings under variable low-speed conditions using a smooth sliding digital twin analysis of indirect acoustic emission (AE) signals. The proposed smooth sliding digital twin is designed based on the combination of the proposed autoregressive fuzzy Gauss–Laguerre bearing modeling approach and the proposed smooth sliding fuzzy observer. The proposed approach has four steps. The AE signals are resampled and the root mean square (RMS) feature is extracted from the AE signal in the first step. To estimate the resampled RMS bearing signal, a new smooth sliding digital twin is proposed in the second step. After that, the resampled RMS bearing residual signal is generated using the difference between the original and estimated signals. Next, a support vector machine (SVM) is proposed for crack detection and crack size identification. The effectiveness of this new approach is evaluated by AE signals provided by our lab’s bearing dataset, where the benchmark dataset consists of one normal and seven abnormal conditions: ball, outer, inner, outer-ball, inner-ball, inner-outer, and inner-outer-ball. The results demonstrated that the average accuracies of the anomaly diagnosis and crack size identification of AE signals for the bearings used in this new smooth sliding digital twin are 97.75% and 97.78%, respectively. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics Volume III)
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16 pages, 1821 KiB  
Article
Identification of Key Components of CNC Lathe Based on Dynamic Influence of Fault Propagation
by Lan Luan, Guixiang Shen, Yingzhi Zhang and Guiming Guo
Appl. Sci. 2022, 12(12), 6187; https://doi.org/10.3390/app12126187 - 17 Jun 2022
Cited by 3 | Viewed by 1435
Abstract
Identifying the key components of CNC lathe and analyzing the fault propagation behavior is a powerful guarantee for the fault diagnosis and health maintenance of CNC lathe. The traditional key component identification studies are mostly based on the feature parameter evaluation of the [...] Read more.
Identifying the key components of CNC lathe and analyzing the fault propagation behavior is a powerful guarantee for the fault diagnosis and health maintenance of CNC lathe. The traditional key component identification studies are mostly based on the feature parameter evaluation of the fault propagation model, disregarding the dynamics and influence of fault propagation. Therefore, this paper proposes a key component identification method based on the dynamic influence of fault propagation. Based on the CNC lathe architecture and fault data, the cascaded faults are analyzed. The improved Floyd algorithm is used to iterate and transform the direct correlation matrix expressing the cascaded fault information, and the fault propagation structure model of each component is constructed. The coupling degree function is introduced to calculate the dynamic impact degree between components, and the dynamic fault propagation rate of each component is calculated with the dynamic fault rate model. Based on this, the dynamic influence value of fault propagation is obtained by using the improved ASP algorithm. The key components of the system are identified by synthesizing the fault propagation structure model and the dynamic influence value of fault propagation. Taking a certain type of CNC lathe as an example, the proposed method is verified to be scientific and effective by comparing with the traditional identification method of key components based on fault propagation intensity. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics Volume III)
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