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Processes
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Intelligent Monitoring and Fault Diagnosis of Complex Industrial Processes or...
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Intelligent Monitoring and Fault Diagnosis of Complex Industrial Processes or Equipment

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 30 July 2024 | Viewed by 1521

Special Issue Editors

Dr. Zhen Chen

E-Mail Website
Guest Editor
Department of Industrial Engineering & Management, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: reliability modeling; condition monitoring; prognostics and health management; data fusion
Dr. Di Zhou

E-Mail Website
Guest Editor
College of Mechanical Engineering, Donghua University, Shanghai 200051, China
Interests: fault intelligent diagnosis; dynamic reliability evaluation; signal processing
Dr. Biao Lu

E-Mail Website
Guest Editor
College of Economic and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: prognostics and health management; maintenance optimization; smart manufacturing
Prof. Dr. Ershun Pan

E-Mail Website
Guest Editor
Department of Industrial Engineering & Management, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: quality and reliability engineering; prognostics and health management; production planning; lean management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the expanding processing requirements of industrial big data, the pervasive use of artificial intelligence (AI) technology has revolutionized various industries, such as manufacturing, nuclear, aerospace, railway vehicles, and smart vehicles, through applications like condition monitoring, fault diagnosis, and predictive maintenance. Although a significant number of interesting and significant works have been reported in various prestigious journals, some critical problems remain not fully explored and answered.  Some of these problems include the condition monitoring of complex industrial processes with time-varying working conditions, fault diagnosis for complex equipment under composite failure modes, optimal decision-making for predictive maintenance, and so on.

This Special Issue aims to address recent developments in the theory and application of health management for complex industrial processes or equipment. Topics include, but are not limited to, the following:

  • Intelligent condition monitoring for complex industrial processes
  • AI-based anomaly detection for complex industrial processes
  • Intelligent fault diagnosis for complex equipment or components
  • Multi-sensor data fusion with artificial intelligence algorithms
  • Fault prognostics for complex industrial processes
  • Degradation modeling and remaining useful life prediction
  • Predictive maintenance decision-making for complex systems
  • Self-data-driven diagnosis approaches

Dr. Zhen Chen
Dr. Di Zhou
Dr. Biao Lu
Prof. Dr. Ershun Pan
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. Processes is an international peer-reviewed open access monthly 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

  • complex industrial processes
  • complex equipment and components
  • condition monitoring
  • fault diagnosis
  • fault prognostics
  • remaining useful life prediction
  • maintenance optimization
  • intelligent algorithms
  • deep learning
  • statistical machine learning

Published Papers (3 papers)

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Research

20 pages, 4288 KiB  
Article
A Physics-Based Tweedie Exponential Dispersion Process Model for Metal Fatigue Crack Propagation and Prognostics
by Lin Yang, Zirong Wang, Zhen Chen and Ershun Pan
Processes 2024, 12(5), 849; https://doi.org/10.3390/pr12050849 - 23 Apr 2024
Viewed by 229
Abstract
Most structural faults in metal parts can be attributed to fatigue crack propagation. The analysis and prognostics of fatigue crack propagation play essential roles in the health management of mechanical systems. Due to the impacts of different uncertainty factors, the crack propagation process [...] Read more.
Most structural faults in metal parts can be attributed to fatigue crack propagation. The analysis and prognostics of fatigue crack propagation play essential roles in the health management of mechanical systems. Due to the impacts of different uncertainty factors, the crack propagation process exhibits significant randomness, which causes difficulties in fatigue life prediction. To improve prognostic accuracy, a physics-based Tweedie exponential dispersion process (TEDP) model is proposed via integrating Paris Law and the stochastic process. This TEDP model can capture both the crack growth mechanism and uncertainty. Compared with other existing models, the TEDP taking Wiener process, Gamma process, and inverse process as special cases is more general and flexible in modeling complex degradation paths. The probability density function of the model is derived based on saddle-joint approximation. The unknown parameters are calculated via maximum likelihood estimation. Then, the analytic expressions of the distributions of lifetime and product reliability are presented. Significant findings include that the proposed TEDP model substantially enhances predictive accuracy in lifetime estimations of mechanical systems under varying operational conditions, as demonstrated in a practical case study on fatigue crack data. This model not only provides highly accurate lifetime predictions, but also offers deep insights into the reliability assessments of mechanically stressed components. Full article
(This article belongs to the Special Issue Intelligent Monitoring and Fault Diagnosis of Complex Industrial Processes or Equipment)
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17 pages, 15319 KiB  
Article
Ternary Precursor Centrifuge Rolling Bearing Fault Diagnosis Based on Adaptive Sample Length Adjustment of 1DCNN-SeNet
by Feng Xu, Zhen Sui, Jiangang Ye and Jianliang Xu
Processes 2024, 12(4), 702; https://doi.org/10.3390/pr12040702 - 29 Mar 2024
Viewed by 471
Abstract
To address the issues of uneven sample lengths in the centrifuge machine bearings of the ternary precursor, inaccurate fault feature extraction, and insensitivity of important feature channels in rolling bearings, a rolling bearing fault diagnosis method based on adaptive sample length adjustment of [...] Read more.
To address the issues of uneven sample lengths in the centrifuge machine bearings of the ternary precursor, inaccurate fault feature extraction, and insensitivity of important feature channels in rolling bearings, a rolling bearing fault diagnosis method based on adaptive sample length adjustment of one-dimensional convolutional neural network (1DCNN) and squeeze-and-excitation network (SeNet) is proposed. Firstly, by controlling the cumulative variance contribution rate in the principal component analysis algorithm, adaptive adjustment of sample length is achieved, reducing data with uneven sample lengths to the same dimensionality for various classes. Then, the 1DCNN extracts local features from bearing signals through one-dimensional convolution-pooling operations, while the SeNet network introduces a channel attention mechanism which can adaptively adjust the importance between different channels. Finally, the 1DCNN-SeNet model is compared with four classic models through experimental analysis on the CWRU bearing dataset. The experimental results indicate that the proposed method exhibits high diagnostic accuracy in rolling bearings, demonstrating good adaptability and generalization capabilities. Full article
(This article belongs to the Special Issue Intelligent Monitoring and Fault Diagnosis of Complex Industrial Processes or Equipment)
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21 pages, 4500 KiB  
Article
High-Performance Defect Detection Methods for Real-Time Monitoring of Ceramic Additive Manufacturing Process Based on Small-Scale Datasets
by Xinjian Jia, Shan Li, Tongcai Wang, Bingshan Liu, Congcong Cui, Wei Li and Gong Wang
Processes 2024, 12(4), 633; https://doi.org/10.3390/pr12040633 - 22 Mar 2024
Viewed by 625
Abstract
Vat photopolymerization is renowned for its high flexibility, efficiency, and precision in ceramic additive manufacturing. However, due to the impact of random defects during the recoating process, ensuring the yield of finished products is challenging. At present, the industry mainly relies on manual [...] Read more.
Vat photopolymerization is renowned for its high flexibility, efficiency, and precision in ceramic additive manufacturing. However, due to the impact of random defects during the recoating process, ensuring the yield of finished products is challenging. At present, the industry mainly relies on manual visual inspection to detect defects; this is an inefficient method. To address this limitation, this paper presents a method for ceramic vat photopolymerization defect detection based on a deep learning framework. The framework innovatively adopts a dual-branch object detection approach, where one branch utilizes a fully convolution network to extract the features from fused images and the other branch employs a differential Siamese network to extract the differential information between two consecutive layer images. Through the design of the dual branches, the decoupling of image feature layers and image spatial attention weights is achieved, thereby alleviating the impact of a few abnormal points on training results and playing a crucial role in stabilizing the training process, which is suitable for training on small-scale datasets. Comparative experiments are implemented and the results show that using a Resnet50 backbone for feature extraction and a HED network for the differential Siamese network module yields the best detection performance, with an obtained F1 score of 0.89. Additionally, as a single-stage defect object detector, the model achieves a detection frame rate of 54.01 frames per second, which meets the real-time detection requirements. By monitoring the recoating process in real-time, the manufacturing fluency of industrial equipment can be effectively enhanced, contributing to the improvement of the yield of ceramic additive manufacturing products. Full article
(This article belongs to the Special Issue Intelligent Monitoring and Fault Diagnosis of Complex Industrial Processes or Equipment)
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