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Recent Advances in Prognostics and Health Management in Industry 4.0...
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Recent Advances in Prognostics and Health Management in Industry 4.0 Era

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 4507

Special Issue Editors

Prof. Dr. Yiwei Cheng

E-Mail Website
Guest Editor
School of Mechanical and Electronic Information, China University of Geosciences, Wuhan 430078, China
Interests: big data analytics; machining health monitoring; intelligent fault diagnosis; remaining useful life prediction
Prof. Dr. Jun Wu

E-Mail Website
Guest Editor
School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: structure health monitoring; online fault monitoring and diagnosis; maintenance decision
Special Issues, Collections and Topics in MDPI journals
Dr. Pengfei Liang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
Interests: industrial big data analysis; artificial intelligence algorithm; signal analysis and processing; machine health monitoring

Special Issue Information

Dear Colleagues,

Prognostics and health management (PHM) is an important technology used to improve the reliability, safety, and stability of industrial systems and engineering equipment. It mainly uses various monitoring methods for the real-time monitoring of operation status and analyzes the fault mechanism, so as to realize the deep mining of health status monitoring information, the real-time perception of the health status, the intelligent diagnosis of fault types, the accurate prediction of the remaining useful life in the future service period, and the formulation of efficient maintenance programs.

With the rapid development of information technology, more and more new technologies have been applied in the field of PHM, such as cloud computing, Big Data, Internet of Things, artificial intelligence, block chain, etc. This Special Issue mainly focuses on the application of these new technologies in PHM. We are soliciting original contributions of leading researchers and practitioners from academia as well as industry, which address a wide range of theoretical and application issues in PHM.

 This Special Issue will focus on (but not limited to) the following topics:

  • Mechanical fault mechanism analysis and simulation modeling;
  • Smart sensors, online monitoring, and anomaly detection for engineering system and equipment;
  • Health degradation analysis and modeling;
  • Intelligent fault diagnosis for engineering system and equipment;
  • Remaining useful life prediction for machinery and batteries;
  • Efficient maintenance decision and safety analysis for complex system;
  • Industrial artificial intelligence for PHM;
  • Internet of Things for PHM;
  • Digital twins and block chain for PHM;
  • Cloud computing and big data technology for PHM.

Prof. Dr. Yiwei Cheng
Prof. Dr. Jun Wu
Dr. Pengfei Liang
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. Electronics 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

  • prognostics and health management
  • big data analysis
  • industrial artificial intelligence
  • Internet of Things
  • digital twins

Published Papers (3 papers)

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Research

19 pages, 7943 KiB  
Article
Health Indicator Similarity Analysis-Based Adaptive Degradation Trend Detection for Bearing Time-to-Failure Prediction
by Zhipeng Chen, Haiping Zhu, Liangzhi Fan and Zhiqiang Lu
Electronics 2023, 12(7), 1569; https://doi.org/10.3390/electronics12071569 - 27 Mar 2023
Cited by 1 | Viewed by 934
Abstract
Time-to-failure (TTF) prediction of bearings is vital to the prognostic and health management of rotating machines. Owing to the shifty degradation trends (DTs) of bearings, it is still difficult to obtain accurate TTF prognostic results. To solve this problem, this paper proposes an [...] Read more.
Time-to-failure (TTF) prediction of bearings is vital to the prognostic and health management of rotating machines. Owing to the shifty degradation trends (DTs) of bearings, it is still difficult to obtain accurate TTF prognostic results. To solve this problem, this paper proposes an online, continuously updated TTF prognostic method based on health indicator (HI) similarity analysis and DT detection. First, multiple degradation features are extracted and fused to construct principal component HI by using dynamic principal component analysis. Next, exponential degradation models are fitted using the HI values for future state prediction. By regarding several HI values as a tested segment, the DT is detected by analyzing the similarity of the tested segment and the fitted curve. Finally, TTF is predicted by extrapolating the DT to hit the estimated failure threshold. Two case studies based on public bearing datasets demonstrate the superiority of the proposed approach over state-of-the-art methods. Full article
(This article belongs to the Special Issue Recent Advances in Prognostics and Health Management in Industry 4.0 Era)
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23 pages, 4721 KiB  
Article
Reliability Analysis of Failure-Dependent System Based on Bayesian Network and Fuzzy Inference Model
by Shangjia Xiang, Yaqiong Lv, Yifan Li and Lu Qian
Electronics 2023, 12(4), 1026; https://doi.org/10.3390/electronics12041026 - 18 Feb 2023
Cited by 2 | Viewed by 1194
Abstract
With the rapid development of information and automation technology, the manufacturing system is evolving towards more complexity and integration. The system components will inevitably suffer from degeneration, and the impact of component-level failure on the system reliability is a valuable issue to be [...] Read more.
With the rapid development of information and automation technology, the manufacturing system is evolving towards more complexity and integration. The system components will inevitably suffer from degeneration, and the impact of component-level failure on the system reliability is a valuable issue to be studied, especially when failure dependence exists among the components. Thus, it is vital to construct a system reliability evaluation mechanism that helps to characterize the healthy status of the system and facilitate wise decision making. In this paper, a reliability analysis framework for a failure-dependent system is proposed, in which copula functions with optimized parameters are used for the description of different failure correlations, and a fuzzy inference model is constructed to derive the subsystem reliability based on the component-level failure correlation. Finally, a Bayesian network is applied to infer the system reliability based on the system structure combined with the impact of failure correlation inside. Simulation results of the proposed method show that the inference results of system reliability are reasonable and effective in different cases. Compared with the copula Bayesian network method, the proposed method shows better adaptability to failure-dependent systems to varying degrees. This work can provide theoretical guidance for evaluating the reliability of manufacturing systems of different types. Full article
(This article belongs to the Special Issue Recent Advances in Prognostics and Health Management in Industry 4.0 Era)
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15 pages, 3000 KiB  
Article
Fault Diagnosis for Rolling Bearings Based on Multiscale Feature Fusion Deep Residual Networks
by Xiangyang Wu, Haibin Shi and Haiping Zhu
Electronics 2023, 12(3), 768; https://doi.org/10.3390/electronics12030768 - 03 Feb 2023
Cited by 8 | Viewed by 1481
Abstract
Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized in the fault diagnosis field. However, there are two common problems in deep-learning-based fault diagnosis methods: (1) many researchers attempt to deepen the layers of deep learning models for higher [...] Read more.
Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized in the fault diagnosis field. However, there are two common problems in deep-learning-based fault diagnosis methods: (1) many researchers attempt to deepen the layers of deep learning models for higher diagnostic accuracy, but degradation problems of deep learning models often occur; and (2) the use of multiscale features can easily be ignored, which makes the extracted data features lack diversity. To deal with these problems, a novel multiscale feature fusion deep residual network is proposed in this paper for the fault diagnosis of rolling bearings, one which contains multiple multiscale feature fusion blocks and a multiscale pooling layer. The multiple multiscale feature fusion block is designed to automatically extract the multiscale features from raw signals, and further compress them for higher dimensional feature mapping. The multiscale pooling layer is constructed to fuse the extracted multiscale feature mapping. Two famous rolling bearing datasets are adopted to evaluate the diagnostic performance of the proposed model. The comparison results show that the diagnostic performance of the proposed model is superior to not only several popular models, but also other advanced methods in the literature. Full article
(This article belongs to the Special Issue Recent Advances in Prognostics and Health Management in Industry 4.0 Era)
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