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Applied Sciences
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Intelligent Fault Diagnosis and Monitoring
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Intelligent Fault Diagnosis and Monitoring

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 5726

Special Issue Editors

Prof. Dr. Hanxin Chen

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: fault diagnosis and prognosis; reliability; non-destructive testing; structural health evaluation; advanced signal analysis; nonlinear system identification; instrument measurement
Prof. Dr. Qinglai Wei

E-Mail Website
Guest Editor
The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Haidian District, Beijing 100190, China
Interests: computational intelligence; learning systems; learning control; optimal control

Special Issue Information

Dear Colleagues,

The condition monitoring and fault diagnosis technology for complex industrial production processes have always been the main goal of global future industrial production development, with informatization, intelligence, high reliability and high efficiency. Mechanical fault diagnosis and prediction are the key technology for intelligent and automated safety production in the modern manufacturing production lines. The development of the theories and methods for mechanical fault diagnosis under non-stationary operating condition is an urgent requirement for modern industrial production processes to achieve intelligence, digital monitoring and diagnostic prediction. It has become a technical bottleneck and recognized challenge for the applications of fault diagnosis technology in key mechanical equipment in engineering practice. This Special Issue focuses on the applications of intelligent techniques for sensor signal analysis, such as vibration, acoustics, image, stress, strain, electromagnetic, etc. It includes the development and applications of the advanced methods and theories in the research areas of fault mechanism analysis, system dynamic analysis and model, machine learning, artificial intelligence, data mining, signal processing, reliability analysis, remaining life prognosis, non-destructive testing, advanced imaging process, optimization method, big data analysis, sensors, instrument measurement, nonlinear system analysis, robotic control technology, information technique, etc. It invites research papers and reviews that explore innovative approaches and tools.

Prof. Dr. Hanxin Chen
Prof. Dr. Qinglai Wei
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

  • fault diagnosis and prognosis
  • structural health monitoring
  • machine learning
  • image and signal processing
  • non-destructive testing
  • multiple sensors
  • nonlinear system identification
  • reliability
  • big data
  • cloud computing

Published Papers (6 papers)

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Research

16 pages, 6424 KiB  
Article
A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus
by Yangtao Li, Haiquan Liang and Xiaodong Tan
Appl. Sci. 2023, 13(24), 13128; https://doi.org/10.3390/app132413128 - 09 Dec 2023
Viewed by 683
Abstract
The MVB is a kind of widely used vehicle-level bus, which is crucial for the normal operation of trains. However, the MVB bus contains device node terminals and the topology is complex, which makes fault location difficult. The voltage waveform of the MVB [...] Read more.
The MVB is a kind of widely used vehicle-level bus, which is crucial for the normal operation of trains. However, the MVB bus contains device node terminals and the topology is complex, which makes fault location difficult. The voltage waveform of the MVB physical layer can reflect the electrical characteristics and fault characteristics of a network in real time and has the value of assisting the diagnosis of MVB network faults. Based on the characteristics of the MVB physical-layer voltage waveform, this paper studies the optimal sampling rate and feature-extraction algorithm of the MVB voltage waveform and the combination of the data of the data-link layer to assign accurate timestamps to waveform data, and finally presents a design for an MVB dual-mode data-acquisition platform with multiple sampling rates/test conditions. Experimental results show that the proposed algorithm can accurately extract waveform feature information under the optimal sampling rate of 62.5 MHz, which makes the collection of MVB voltage waveform data more reliable and practical. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)
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20 pages, 3807 KiB  
Article
Dynamic Reliability Assessment Method for a Pantograph System Based on a Multistate T-S Fault Tree, Dynamic Bayesian
by Yafeng Chen, Jing Wen, Yingjie Tian, Shubin Zheng, Qianwen Zhong and Xiaodong Chai
Appl. Sci. 2023, 13(19), 10711; https://doi.org/10.3390/app131910711 - 26 Sep 2023
Cited by 1 | Viewed by 620
Abstract
The operational reliability of rail vehicle pantograph systems is evaluated by transforming T-S multistate fault trees into dynamic Bayesian networks (DBNs), which take into account system multistability, long-lasting operation, dynamic failure, and maintenance recovery. The T-S multistate fault tree structure is constructed by [...] Read more.
The operational reliability of rail vehicle pantograph systems is evaluated by transforming T-S multistate fault trees into dynamic Bayesian networks (DBNs), which take into account system multistability, long-lasting operation, dynamic failure, and maintenance recovery. The T-S multistate fault tree structure is constructed by the content validity ratio and content validity index; the T-S gate rule expressing causal uncertainty is constructed by using fuzzy theory and dependent uncertain ordered weighted averaging expert scoring, and finally, the pantograph T-S multistate fault tree is transformed into a DBN model characterizing the dynamic interaction and time dependence of the system. The dynamic evolution laws of reliability of a pantograph system in maintenance and maintenance-free states over time are inferred, compared and analyzed. The results show that the system availability of a pantograph system decreases continuously during 720 days of operation. The system availability without maintenance decreases to 0.881, and the system availability with maintenance is 0.952. The reliability of a pantograph system can be effectively ensured with maintenance during the operation period; the sensitivity analysis is performed by changing the failure rate of the equipment to 120% or 80%; the fall indicator, the electrical control box, and the elevating bow motor are the weak links in the system, and the impact of fault escalation on the reliability of a pantograph system is analyzed. It is then verified that the system reliability can be further improved by using a preventive maintenance strategy, and the steady-state reliability can be gradually reached, which is about 0.9968, providing a reference for the maintenance of a pantograph system. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)
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21 pages, 4639 KiB  
Article
A Multi-Adversarial Joint Distribution Adaptation Method for Bearing Fault Diagnosis under Variable Working Conditions
by Zhichao Cui, Hui Cao, Zeren Ai and Jihui Wang
Appl. Sci. 2023, 13(19), 10606; https://doi.org/10.3390/app131910606 - 23 Sep 2023
Cited by 2 | Viewed by 820
Abstract
Deep network fault diagnosis requires a lot of labeled data and assumes identical data distributions for training and testing. In industry, varying equipment conditions lead to different data distributions, making it challenging to maintain consistent fault diagnosis performance across conditions. To this end, [...] Read more.
Deep network fault diagnosis requires a lot of labeled data and assumes identical data distributions for training and testing. In industry, varying equipment conditions lead to different data distributions, making it challenging to maintain consistent fault diagnosis performance across conditions. To this end, this paper designs a transfer learning model named the multi-adversarial joint distribution adaptation network (MAJDAN) to achieve effective fault diagnosis across operating conditions. MAJDAN uses a one-dimensional lightweight convolutional neural network (1DLCNN) to directly extract features from the original bearing vibration signal. Combining the distance-based domain-adaptive method, maximum mean difference (MMD), with the multi-adversarial network will simultaneously reduce the conditional and marginal distribution differences between the domains. As a result, MAJDAN can efficiently acquire domain-invariant feature information, addressing the challenge of cross-domain bearing fault diagnosis. The effectiveness of the model was verified based on two sets of different bearing vibration signals, and one-to-one and one-to-many working condition migration task experiments were carried out. Simultaneously, various levels of noise were introduced to the signal to enable analysis and comparison. The findings demonstrate that the suggested approach achieves exceptional diagnostic accuracy and exhibits robustness. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)
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18 pages, 6909 KiB  
Article
A Novel Approach to Satellite Component Health Assessment Based on the Wasserstein Distance and Spectral Clustering
by Yongchao Hui, Yuehua Cheng, Bin Jiang, Xiaodong Han and Lei Yang
Appl. Sci. 2023, 13(16), 9438; https://doi.org/10.3390/app13169438 - 21 Aug 2023
Cited by 1 | Viewed by 734
Abstract
This research presents a multiparameter approach to satellite component health assessment aimed at addressing the increasing demand for in-orbit satellite component health assessment. The method encompasses three key enhancements. Firstly, the utilization of the Wasserstein distance as an indicator simplifies the decision-making process [...] Read more.
This research presents a multiparameter approach to satellite component health assessment aimed at addressing the increasing demand for in-orbit satellite component health assessment. The method encompasses three key enhancements. Firstly, the utilization of the Wasserstein distance as an indicator simplifies the decision-making process for assessing the health of data distributions. This enhancement allows for a more robust handling of noisy sensor data, resulting in improved accuracy in health assessment. Secondly, the original limitation of assessing component health within the same parameter class is overcome by extending the evaluation to include multiple parameter classes. This extension leads to a more comprehensive assessment of satellite component health. Lastly, the method employs spectral clustering to determine the boundaries of different health status classes, offering an objective alternative to traditional expert-dependent approaches. By adopting this technique, the proposed method enhances the objectivity and accuracy of the health status classification. The experimental results show that the method is able to accurately describe the trends in the health status of components. Its effectiveness in real-time health assessment and monitoring of satellite components is confirmed. This research provides a valuable reference for further research on satellite component health assessment. It introduces novel and enhanced ideas and methodologies for practical applications. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)
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21 pages, 5979 KiB  
Article
Instantaneous Square Current Signal Analysis for Motors Using Vision Transformer for the Fault Diagnosis of Rolling Bearings
by Fei Chen, Xin Zhou, Binbin Xu, Zheng Yang and Zege Qu
Appl. Sci. 2023, 13(16), 9349; https://doi.org/10.3390/app13169349 - 17 Aug 2023
Viewed by 1061
Abstract
Using vibration signals for bearing fault diagnosis can generally achieve good diagnostic results. However, it is not suitable for practical industrial applications due to the restricted installation and high cost of vibration sensors. Therefore, the easily obtainable motor current signal (MCS) has received [...] Read more.
Using vibration signals for bearing fault diagnosis can generally achieve good diagnostic results. However, it is not suitable for practical industrial applications due to the restricted installation and high cost of vibration sensors. Therefore, the easily obtainable motor current signal (MCS) has received widespread attention in recent years. Meanwhile, traditional fault diagnosis methods cannot meet the diagnostic accuracy requirements because of the low signal-to-noise ratio (SNR) of the MCS. Committed to achieving bearing fault diagnosis through MCS, a rolling bearing fault diagnosis method, ISCV-ViT, based on the MCS and the Vision Transformer (ViT) model, is proposed. In particular, a signal processing method based on the instantaneous square current value (ISCV) is proposed to process the MCS directly obtained through a frequency converter into time-domain images. Then, the ViT model is applied for bearing fault diagnosis. Finally, experimental verification is carried out based on the public bearing dataset of Paderborn University (PU) and the bearing dataset of Shenzhen Technology University (SZTU). The analysis of the experimental results demonstrates that the average accuracy of the ISCV-ViT for the two datasets is up to 96.60% and 94.87%, respectively. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)
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21 pages, 6299 KiB  
Article
Weak Fault Diagnosis Method of Rolling Bearings Based on Variational Mode Decomposition and a Double-Coupled Duffing Oscillator
by Shijie Shan, Jianming Zheng, Kai Wang, Ting Chen and Yuhua Shi
Appl. Sci. 2023, 13(14), 8505; https://doi.org/10.3390/app13148505 - 23 Jul 2023
Viewed by 802
Abstract
Aiming at the problems of the low detection accuracy and difficult identification of the early weak fault signals of rolling bearings, this paper proposes a method for detecting the early weak fault signals of rolling bearings based on a double-coupled Duffing system and [...] Read more.
Aiming at the problems of the low detection accuracy and difficult identification of the early weak fault signals of rolling bearings, this paper proposes a method for detecting the early weak fault signals of rolling bearings based on a double-coupled Duffing system and VMD. The influence rule of system initial value on the response characteristics of a double-coupled Duffing system is studied, and the basis for its determination is given. The frequency of the built-in power of the system is normalized, and a variance evaluation standard for the output value of the double-coupled Duffing system for weak fault signals detection is established. In order to solve the interference problem of fault monitoring signals, VMD is proposed to pre-process the fault monitoring signals. The weak fault signal detection method proposed in this paper is tested and verified by simulation signals and rolling bearing fault signals. The results show that the method proposed in this paper can detect the weak fault signal with the lowest signal-to-noise ratio reduced by 2.96 dB compared with the traditional Duffing detection system, and it can accurately detect the early weak fault signal of rolling bearings. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Monitoring)
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