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Information Theory and Its Application in Machine Condition Monitoring

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 25291

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Special Issue Editors

Dr. Yongbo Li

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Guest Editor

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: information fusion; digital twin technology; structural health monitoring; fault diagnosis and prognosis; system reliability analysis; dynamic modelling of mechanical systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Fengshou Gu

E-Mail Website
Guest Editor

Centre for Efficiency and Performance Engineering (CEPE), Department of Engineering and Technology, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: vibro-impact modelling; machine modelling and fault simulation; neural network modelling; time–frequency and time–scale analysis; modulation and demodulation analysis; complex vibro-acoustic source identification; acoustic condition monitoring; intelligent monitoring system; powerless and wireless data sensing and transfer; tribological dynamics
Special Issues, Collections and Topics in MDPI journals

Dr. Xihui (Larry) Liang

E-Mail Website
Guest Editor

Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Interests: nonlinear dynamics; condition monitoring; predictive maintenance; intelligent manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Condition monitoring (CM) techniques have been rapidly advancing in recent years for promoting the productivity and reliability of large-scale engineering systems. This advancement is greatly impacted by the progress in information theory and computing technologies, evidenced by many published works in CM fields, such as Shannon entropy, Lempel-ziv complexity, and permutation entropy. As a statistical measure, information theory can be used to quantify complexity and detect dynamic change by taking into account the nonlinear behavior of time series. The information theory can be served as a promising tool to extract the dynamic characteristics of machines, which is useful in developing effective condition monitoring techniques.

The last decade has witnessed an increasingly growing research interest in information theory. This Special Issue aims to provide a platform to present high-quality original research as well as review articles on the latest developments of information theory and its application in machine condition monitoring.

Dr. Yongbo Li
Prof. Dr. Fengshou Gu
Dr. Xihui (Larry) Liang
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

information theory
condition monitoring
complexity measure
symbolic dynamic analysis
fault detection, diagnosis and prognosis
dynamic change detection
machine learning
physics driven digital modeling
multi-objective optimizations
intelligent maintenance

Published Papers (11 papers)

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Editorial

Jump to: Research

3 pages, 157 KiB

Open AccessEditorial

Information Theory and Its Application in Machine Condition Monitoring

by Yongbo Li, Fengshou Gu and Xihui Liang

Entropy 2022, 24(2), 206; https://doi.org/10.3390/e24020206 - 28 Jan 2022

Viewed by 1649

Abstract

Rotating machinery is part and parcel of modern industrial applications [...] Full article

(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring)

Research

Jump to: Editorial

14 pages, 3078 KiB

Open AccessArticle

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

by Gang Mao, Zhongzheng Zhang, Bin Qiao and Yongbo Li

Entropy 2022, 24(1), 119; https://doi.org/10.3390/e24010119 - 13 Jan 2022

Cited by 22 | Viewed by 2454

Abstract

The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods. Full article

(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring)

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13 pages, 4367 KiB

Open AccessArticle

A Study on Railway Surface Defects Detection Based on Machine Vision

by Tangbo Bai, Jialin Gao, Jianwei Yang and Dechen Yao

Entropy 2021, 23(11), 1437; https://doi.org/10.3390/e23111437 - 30 Oct 2021

Cited by 24 | Viewed by 3065

Abstract

The detection of rail surface defects is an important tool to ensure the safe operation of rail transit. Due to the complex diversity of track surface defect features and the small size of the defect area, it is difficult to obtain satisfying detection results by traditional machine vision methods. The existing deep learning-based methods have the problems of large model sizes, excessive parameters, low accuracy and slow speed. Therefore, this paper proposes a new method based on an improved YOLOv4 (You Only Look Once, YOLO) for railway surface defect detection. In this method, MobileNetv3 is used as the backbone network of YOLOv4 to extract image features, and at the same time, deep separable convolution is applied on the PANet layer in YOLOv4, which realizes the lightweight network and real-time detection of the railway surface. The test results show that, compared with YOLOv4, the study can reduce the amount of the parameters by 78.04%, speed up the detection by 10.36 frames per second and decrease the model volume by 78%. Compared with other methods, the proposed method can achieve a higher detection accuracy, making it suitable for the fast and accurate detection of railway surface defects. Full article

(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring)

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30 pages, 4847 KiB

Open AccessArticle

A Novel Framework for Anomaly Detection for Satellite Momentum Wheel Based on Optimized SVM and Huffman-Multi-Scale Entropy

by Yuqing Li, Mingjia Lei, Pengpeng Liu, Rixin Wang and Minqiang Xu

Entropy 2021, 23(8), 1062; https://doi.org/10.3390/e23081062 - 17 Aug 2021

Cited by 9 | Viewed by 1789

Abstract

The health status of the momentum wheel is vital for a satellite. Recently, research on anomaly detection for satellites has become more and more extensive. Previous research mostly required simulation models for key components. However, the physical models are difficult to construct, and the simulation data does not match the telemetry data in engineering applications. To overcome the above problem, this paper proposes a new anomaly detection framework based on real telemetry data. First, the time-domain and frequency-domain features of the preprocessed telemetry signal are calculated, and the effective features are selected through evaluation. Second, a new Huffman-multi-scale entropy (HMSE) system is proposed, which can effectively improve the discrimination between different data types. Third, this paper adopts a multi-class SVM model based on the directed acyclic graph (DAG) principle and proposes an improved adaptive particle swarm optimization (APSO) method to train the SVM model. The proposed method is applied to anomaly detection for satellite momentum wheel voltage telemetry data. The recognition accuracy and detection rate of the method proposed in this paper can reach 99.60% and 99.87%. Compared with other methods, the proposed method can effectively improve the recognition accuracy and detection rate, and it can also effectively reduce the false alarm rate and the missed alarm rate. Full article

(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring)

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13 pages, 1000 KiB

Open AccessArticle

A New Universal Domain Adaptive Method for Diagnosing Unknown Bearing Faults

by Zhenhao Yan, Guifang Liu, Jinrui Wang, Huaiqian Bao, Zongzhen Zhang, Xiao Zhang and Baokun Han

Entropy 2021, 23(8), 1052; https://doi.org/10.3390/e23081052 - 16 Aug 2021

Cited by 12 | Viewed by 1852

Abstract

The domain adaptation problem in transfer learning has received extensive attention in recent years. The existing transfer model for solving domain alignment always assumes that the label space is completely shared between domains. However, this assumption is untrue in the actual industry and limits the application scope of the transfer model. Therefore, a universal domain method is proposed, which not only effectively reduces the problem of network failure caused by unknown fault types in the target domain but also breaks the premise of sharing the label space. The proposed framework takes into account the discrepancy of the fault features shown by different fault types and forms the feature center for fault diagnosis by extracting the features of samples of each fault type. Three optimization functions are added to solve the negative transfer problem when the model solves samples of unknown fault types. This study verifies the performance advantages of the framework for variable speed through experiments of multiple datasets. It can be seen from the experimental results that the proposed method has better fault diagnosis performance than related transfer methods for solving unknown mechanical faults. Full article

(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring)

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15 pages, 3424 KiB

Open AccessArticle

Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

by Yancai Xiao, Jinyu Xue, Mengdi Li and Wei Yang

Entropy 2021, 23(8), 975; https://doi.org/10.3390/e23080975 - 29 Jul 2021

Cited by 9 | Viewed by 1593

Abstract

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem. Full article

(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring)

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17 pages, 9763 KiB

Open AccessArticle

Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression

by Zhongshuo Hu, Jianwei Yang, Dechen Yao, Jinhai Wang and Yongliang Bai

Entropy 2021, 23(6), 660; https://doi.org/10.3390/e23060660 - 25 May 2021

Cited by 8 | Viewed by 2074

Abstract

In the signal processing of real subway vehicles, impacts between wheelsets and rail joint gaps have significant negative effects on the spectrum. This introduces great difficulties for the fault diagnosis of gearboxes. To solve this problem, this paper proposes an adaptive time-domain signal segmentation method that envelopes the original signal using a cubic spline interpolation. The peak values of the rail joint gap impacts are extracted to realize the adaptive segmentation of gearbox fault signals when the vehicle was moving at a uniform speed. A long-time and unsteady signal affected by wheel–rail impacts is segmented into multiple short-term, steady-state signals, which can suppress the high amplitude of the shock response signal. Finally, on this basis, multiple short-term sample signals are analyzed by time- and frequency-domain analyses and compared with the nonfaulty results. The results showed that the method can efficiently suppress the high-amplitude components of subway gearbox vibration signals and effectively extract the characteristics of weak faults due to uniform wear of the gearbox in the time and frequency domains. This provides reference value for the gearbox fault diagnosis in engineering practice. Full article

(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring)

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14 pages, 2255 KiB

Open AccessArticle

A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery

by Sixiang Jia, Jinrui Wang, Xiao Zhang and Baokun Han

Entropy 2021, 23(4), 424; https://doi.org/10.3390/e23040424 - 01 Apr 2021

Cited by 23 | Viewed by 2382

Abstract

Domain adaptation-based models for fault classification under variable working conditions have become a research focus in recent years. Previous domain adaptation approaches generally assume identical label spaces in the source and target domains, however, such an assumption may be no longer legitimate in a more realistic situation that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of fault classes. To address the above deficiencies, we propose a partial transfer fault diagnosis model based on a weighted subdomain adaptation network (WSAN) in this paper. Our method pays more attention to the local data distribution while aligning the global distribution. An auxiliary classifier is introduced to obtain the class-level weights of the source samples, so the network can avoid negative transfer caused by unique fault classes in the source domain. Furthermore, a weighted local maximum mean discrepancy (WLMMD) is proposed to capture the fine-grained transferable information and obtain sample-level weights. Finally, relevant distributions of domain-specific layer activations across different domains are aligned. Experimental results show that our method could assign appropriate weights to each source sample and realize efficient partial transfer fault diagnosis. Full article

(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring)

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24 pages, 5474 KiB

Open AccessArticle

Fault Detection Based on Multi-Dimensional KDE and Jensen–Shannon Divergence

by Juhui Wei, Zhangming He, Jiongqi Wang, Dayi Wang and Xuanying Zhou

Entropy 2021, 23(3), 266; https://doi.org/10.3390/e23030266 - 24 Feb 2021

Cited by 7 | Viewed by 1935

Abstract

Weak fault signals, high coupling data, and unknown faults commonly exist in fault diagnosis systems, causing low detection and identification performance of fault diagnosis methods based on

T^{2}

statistics or cross entropy. This paper proposes a new fault diagnosis method based on [...] Read more.

Weak fault signals, high coupling data, and unknown faults commonly exist in fault diagnosis systems, causing low detection and identification performance of fault diagnosis methods based on

T^{2}

statistics or cross entropy. This paper proposes a new fault diagnosis method based on optimal bandwidth kernel density estimation (KDE) and Jensen–Shannon (JS) divergence distribution for improved fault detection performance. KDE addresses weak signal and coupling fault detection, and JS divergence addresses unknown fault detection. Firstly, the formula and algorithm of the optimal bandwidth of multidimensional KDE are presented, and the convergence of the algorithm is proved. Secondly, the difference in JS divergence between the data is obtained based on the optimal KDE and used for fault detection. Finally, the fault diagnosis experiment based on the bearing data from Case Western Reserve University Bearing Data Center is conducted. The results show that for known faults, the proposed method has

10 %

and

2 %

higher detection rate than

T^{2}

statistics and the cross entropy method, respectively. For unknown faults,

T^{2}

statistics cannot effectively detect faults, and the proposed method has approximately

15 %

higher detection rate than the cross entropy method. Thus, the proposed method can effectively improve the fault detection rate. Full article

(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring)

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19 pages, 2339 KiB

Open AccessArticle

Misalignment Fault Diagnosis for Wind Turbines Based on Information Fusion

by Yancai Xiao, Jinyu Xue, Long Zhang, Yujia Wang and Mengdi Li

Entropy 2021, 23(2), 243; https://doi.org/10.3390/e23020243 - 20 Feb 2021

Cited by 11 | Viewed by 2122

Abstract

Most conventional wind turbine fault diagnosis techniques only use a single type of signal as fault feature and their performance could be limited to such signal characteristics. In this paper, multiple types of signals including vibration, temperature, and stator current are used simultaneously for wind turbine misalignment diagnosis. The model is constructed by integrated methods based on Dempster–Shafer (D–S) evidence theory. First, the time domain, frequency domain, and time–frequency domain features of the collected vibration, temperature, and stator current signal are respectively taken as the inputs of the least square support vector machine (LSSVM). Then, the LSSVM outputs the posterior probabilities of the normal, parallel misalignment, angular misalignment, and integrated misalignment of the transmission systems. The posterior probabilities are used as the basic probabilities of the evidence fusion, and the fault diagnosis is completed according to the D–S synthesis and decision rules. Considering the correlation between the inputs, the vibration and current feature vectors’ dimensionalities are reduced by t-distributed stochastic neighbor embedding (t-SNE), and the improved artificial bee colony algorithm is used to optimize the parameters of the LSSVM. The results of the simulation and experimental platform demonstrate the accuracy of the proposed model and its superiority compared with other models. Full article

(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring)

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19 pages, 13618 KiB

Open AccessArticle

A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault

by Wentao Mao, Bin Sun and Liyun Wang

Entropy 2021, 23(2), 162; https://doi.org/10.3390/e23020162 - 29 Jan 2021

Cited by 14 | Viewed by 2376

Abstract

With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate. Full article

(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring)

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