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Symmetry
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Advances and Applications in Data-Driven Process Monitoring, Fault Diagnosis and...
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Advances and Applications in Data-Driven Process Monitoring, Fault Diagnosis and Control

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 14097

Special Issue Editors

Dr. Zhiwen Chen

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha, 410083, China
Interests: machine learning; data mining and analytic; PHM and fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hao Luo

E-Mail Website
Guest Editor
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
Interests: fault diagnosis; fault-tolerant control; distributed optimization; subspace methods
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chao Cheng

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
Interests: fault diagnosis; distributed systems; information processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, driven by the rapid advancements in electronics, information and communication technology, disruptive changes are taking place in the industrial environment. Due to the ever-increasing demands on product quality and economic benefit, not only are intelligent components and devices implemented and networked, but real-time supervision and control systems are also running in parallel. Consequently, the degree of automation in modern industrial systems is continuously growing. This fact challenges scientists and engineers to develop advanced process monitoring, fault diagnosis and control methodologies, using offline, stored, or online process data to solve optimal process monitoring, fault diagnosis and control issues. In addition, new methods have recently been developed, based on multivariate statistical analysis (including multivariate symmetry and asymmetry), data analytics (including information symmetry), machine learning (including deep learning), and data-driven control.

This planned Special Issue of Symmetry aims to provide a forum for researchers and industrial engineers to exchange the latest results on data-driven process monitoring, fault diagnosis and control techniques, and to discuss the vital issues, challenges and possible future trends in modern large-scale industrial systems. The papers to be accepted in this Special Issue are expected to provide the latest developments in data-driven design approaches, especially new theoretical results with practical applications. We would like to invite domestic and foreign experts to contribute with their research by employing the symmetry or asymmetry concepts in their methods and methodologies, including, but not limited to the areas listed below.

Submit your paper and select the Journal “Symmetry” and the Special Issue “Advances and Applications in Data-Driven Process Monitoring, Fault Diagnosis and Control” via: MDPI submission system. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.

Prof. Dr. Zhiwen Chen
Prof. Dr. Hao Luo
Prof. Dr. Chao Cheng
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. Symmetry 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

  • data-driven process monitoring and fault diagnosis
  • model-free or data-driven control design
  • data-driven performance evaluation, decisions and their applications
  • data-driven optimization methods and applications
  • real-time model-free learning methods and practical applications
  • deep learning

Published Papers (7 papers)

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Research

17 pages, 1887 KiB  
Article
Machine Learning Techniques for Multi-Fault Analysis and Detection on a Rotating Test Rig Using Vibration Signal
by Iulian Lupea and Mihaiela Lupea
Symmetry 2023, 15(1), 86; https://doi.org/10.3390/sym15010086 - 28 Dec 2022
Cited by 7 | Viewed by 1755
Abstract
Machine health monitoring of rotating mechanical systems is an important task in manufacturing engineering. In this paper, a system for analyzing and detecting mounting defects on a rotating test rig is developed. The test rig comprises a slender shaft with a central disc, [...] Read more.
Machine health monitoring of rotating mechanical systems is an important task in manufacturing engineering. In this paper, a system for analyzing and detecting mounting defects on a rotating test rig is developed. The test rig comprises a slender shaft with a central disc, supported symmetrically by oscillating ball bearings. The shaft is driven at constant speed (with tiny variations) through a timing belt. Faults, such as the translation of the central disc along the shaft, the disc eccentricity, and defects on the motor reducer position or timing belt mounting position, are imposed. Time and frequency domain features, extracted from the vibration signal, are used as predictors in fault detection. This task is modeled as a multi-class classification problem, where the classes correspond to eight health states: one healthy and seven faulty. Data analysis, using unsupervised and supervised algorithms, provides significant insights (relevance of features, correlation between features, classification difficulties, data visualization) into the initial dataset, a balanced one. The experiments are performed using classifiers from MATLAB and six feature sets. Quadratic SVM achieves the best performance: 99.18% accuracy for the set of all 41 features extracted from X and Y accelerometer axes, and 98.93% accuracy for the subset of the 18 most relevant features. Full article
(This article belongs to the Special Issue Advances and Applications in Data-Driven Process Monitoring, Fault Diagnosis and Control)
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19 pages, 1772 KiB  
Article
A Dynamic Opposite Learning-Assisted Grey Wolf Optimizer
by Yang Wang, Chengyu Jin, Qiang Li, Tianyu Hu, Yunlang Xu, Chao Chen, Yuqian Zhang and Zhile Yang
Symmetry 2022, 14(9), 1871; https://doi.org/10.3390/sym14091871 - 07 Sep 2022
Cited by 5 | Viewed by 1210
Abstract
The grey wolf optimization (GWO) algorithm is widely utilized in many global optimization applications. In this paper, a dynamic opposite learning-assisted grey wolf optimizer (DOLGWO) was proposed to improve the search ability. Herein, a dynamic opposite learning (DOL) strategy is adopted, which has [...] Read more.
The grey wolf optimization (GWO) algorithm is widely utilized in many global optimization applications. In this paper, a dynamic opposite learning-assisted grey wolf optimizer (DOLGWO) was proposed to improve the search ability. Herein, a dynamic opposite learning (DOL) strategy is adopted, which has an asymmetric search space and can adjust with a random opposite point to enhance the exploitation and exploration capabilities. To validate the performance of DOLGWO algorithm, 23 benchmark functions from CEC2014 were adopted in the numerical experiments. A total of 10 popular algorithms, including GWO, TLBO, PIO, Jaya, CFPSO, CFWPSO, ETLBO, CTLBO, NTLBO and DOLJaya were used to make comparisons with DOLGWO algorithm. Results indicate that the new model has strong robustness and adaptability, and has the significant advantage of converging to the global optimum, which demonstrates that the DOL strategy greatly improves the performance of original GWO algorithm. Full article
(This article belongs to the Special Issue Advances and Applications in Data-Driven Process Monitoring, Fault Diagnosis and Control)
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14 pages, 2868 KiB  
Article
Few-Shot Learning for Fault Diagnosis: Semi-Supervised Prototypical Network with Pseudo-Labels
by Jun He, Zheshuai Zhu, Xinyu Fan, Yong Chen, Shiya Liu and Danfeng Chen
Symmetry 2022, 14(7), 1489; https://doi.org/10.3390/sym14071489 - 21 Jul 2022
Cited by 4 | Viewed by 2112
Abstract
Achieving deep learning-based bearing fault diagnosis heavily relies on large labeled training samples. However, in real industry applications, labeled data are scarce or even impossible to obtain. In this study, we addressed a challenging few-shot bearing fault diagnosis problem with few or no [...] Read more.
Achieving deep learning-based bearing fault diagnosis heavily relies on large labeled training samples. However, in real industry applications, labeled data are scarce or even impossible to obtain. In this study, we addressed a challenging few-shot bearing fault diagnosis problem with few or no training labeled samples of novel categories. To tackle this problem, we considered a semi-supervised prototype network based on few-shot bearing fault diagnosis with pseudo-labels. The existing prototypical networks with pseudo-label methods train a pseudo label model to label unlabeled samples using high-dimensional labeled data, which cannot eliminate the instability of the pseudo-label model caused by dimensional labeled features. To mitigate this issue, we used kernel principal component analysis to reduce the dimensions of and remove redundant information from high-dimensional data. Specifically, we used the pseudo-label prediction algorithm with probability distance to label unlabeled samples, aiming to improve the labeling accuracy. We applied two well-known bearing data sets for the validation experiments with symmetry parameters. The findings illustrated that the classification accuracy of the proposed method is higher than that of other existing methods. Full article
(This article belongs to the Special Issue Advances and Applications in Data-Driven Process Monitoring, Fault Diagnosis and Control)
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22 pages, 10679 KiB  
Article
Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN
by Jie Ma, Shule Li and Xinyu Wang
Symmetry 2022, 14(2), 320; https://doi.org/10.3390/sym14020320 - 04 Feb 2022
Cited by 6 | Viewed by 1344
Abstract
Owing to the symmetry of the rolling bearing structure and the rotating operation mode, the rolling bearing works in a complex environment. It is very easy to be submerged by noise and misdiagnosis. For the non-stationary signal in variable speed state, this paper [...] Read more.
Owing to the symmetry of the rolling bearing structure and the rotating operation mode, the rolling bearing works in a complex environment. It is very easy to be submerged by noise and misdiagnosis. For the non-stationary signal in variable speed state, this paper presents a condition monitoring method based on deep belief network (DBN) optimized by multi-order fractional Fourier transform (FRFT) and sparrow search algorithm (SSA). Firstly, the fractional Fourier transform based on curve feature segmentation is used to filter the fault vibration signal and extract the fault feature frequency. Then, the fault features are input into the SSA-DBN model for training, and the bearing fault features are classified, identified, and diagnosed. Finally, the rotating machinery fault simulator in the laboratory of Ottawa University is taken as the practical application object to verify the effectiveness of the method. The experimental results show that the proposed method has higher recognition accuracy and stronger stability. Full article
(This article belongs to the Special Issue Advances and Applications in Data-Driven Process Monitoring, Fault Diagnosis and Control)
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16 pages, 3831 KiB  
Article
Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker
by Zhiwen Chen, Ketian Liang, Tao Peng and Yang Wang
Symmetry 2022, 14(2), 295; https://doi.org/10.3390/sym14020295 - 01 Feb 2022
Cited by 6 | Viewed by 1601
Abstract
In many industrial systems, symmetry is the key to ensuring efficiency and reliability. For example, in electric vehicles, the driving system often requires high symmetry. As widely used motors, permanent magnet synchronous motors (PMSMs) are often used in highly symmetrical structures as the [...] Read more.
In many industrial systems, symmetry is the key to ensuring efficiency and reliability. For example, in electric vehicles, the driving system often requires high symmetry. As widely used motors, permanent magnet synchronous motors (PMSMs) are often used in highly symmetrical structures as the driving devices. Consequently, maintaining the symmetry of the system relies on the normal and stable operation of the PMSM, and it is necessary to diagnose faults in the PMSM in a timely manner. In PMSM fault diagnosis methods, frequency domain features of the stator current are extensively used. However, these features change with the switching of motor operating conditions, leading to difficulty of diagnosis in multiple operating conditions. Therefore, a fault diagnosis method based on a convolutional neural network (CNN) phase tracker is proposed in this paper. Through phase tracking and angular domain resampling, the fundamental frequency of stator currents in different operating conditions are aligned, so as to fix the distribution of frequency domain features and solve the problem of features changing with operating conditions. Experimental results show that the proposed method can resample the stator current signals with a small error, detect faults in a relatively short time with high accuracy, and diagnose fault type and severity level under multiple operating conditions. Full article
(This article belongs to the Special Issue Advances and Applications in Data-Driven Process Monitoring, Fault Diagnosis and Control)
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20 pages, 3079 KiB  
Article
Health State Prediction of Aero-Engine Gas Path System Considering Multiple Working Conditions Based on Time Domain Analysis and Belief Rule Base
by Xiaojing Yin, Guangxu Shi, Shouxin Peng, Yu Zhang, Bangcheng Zhang and Wei Su
Symmetry 2022, 14(1), 26; https://doi.org/10.3390/sym14010026 - 26 Dec 2021
Cited by 4 | Viewed by 1970
Abstract
The gas path system is an important part of an aero-engine, whose health states can affect the security of the airplane. During the process of aircraft operation, the gas path system will have different working conditions over time, owing to the change of [...] Read more.
The gas path system is an important part of an aero-engine, whose health states can affect the security of the airplane. During the process of aircraft operation, the gas path system will have different working conditions over time, owing to the change of control parameters. However, the different working conditions which change the symmetry of the system will affect parameters of the health state prediction model for the gas path system. The symmetry of the system will also change. Therefore, it is important to consider the influence of variable working conditions when predicting the health states of gas path system. The accuracy of the health state prediction results of the gas path system will be low if the same evaluation standard is used for different working conditions. In addition, the monitoring data of the gas path system’s health state feature quantity is huge while the fault data which can reflect the health states of the gas path system are poor. Thus, it is difficult to establish a health state prediction model only by using the monitoring data of the gas path system. In order to avoid problems, this paper proposes a health state prediction model considering multiple working conditions based on time domain analysis and a belief rule base. First, working condition is divided by using time domain characteristics. Then, a belief rule base (BRB) theory-based health state prediction model is built, which can fuse expert knowledge and fault monitoring data to improve modeling accuracy. The reference value of the feature is given by the fuzzy C-means algorithm in a model. To decrease the uncertainty of expert knowledge, the covariance matrix adaptive evolution strategy (CMA-ES) is used as the optimization algorithm. Finally, a NASA public dataset without labels is used to verify the proposed health state model. The results show that the proposed health prediction model of a gas path system can accurately realize health state prediction under multiple working conditions. Full article
(This article belongs to the Special Issue Advances and Applications in Data-Driven Process Monitoring, Fault Diagnosis and Control)
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20 pages, 5441 KiB  
Article
Compound Fault Diagnosis of Rolling Bearing Based on ACMD, Gini Index Fusion and AO-LSTM
by Jie Ma and Xinyu Wang
Symmetry 2021, 13(12), 2386; https://doi.org/10.3390/sym13122386 - 10 Dec 2021
Cited by 4 | Viewed by 2137
Abstract
Due to the symmetry of the rolling bearing structure and the rotating operation mode, it will cause the coupling modulation phenomenon when it is damaged in multiple places at the same time, which makes it difficult to accurately identify all kinds of faults. [...] Read more.
Due to the symmetry of the rolling bearing structure and the rotating operation mode, it will cause the coupling modulation phenomenon when it is damaged in multiple places at the same time, which makes it difficult to accurately identify all kinds of faults. For such problems, a compound fault diagnosis method based on adaptive chirp mode decomposition (ACMD), Gini index fusion and long short-term memory (LSTM) neural network optimized by Aquila Optimizer (AO) is proposed. Firstly, a series of IMF components are obtained by decomposing the vibration signal by means of ACMD, and the required components are selected by using the correlation coefficient method. Then, the Gini index of the square envelope (GISE) and the Gini index of the square envelope spectrum (GISES) of each component are calculated, respectively, and they are fused to construct a highly dimensional feature matrix. Then, with the aim of solving the problem of difficult selection of LSTM hyperparameters, the AO-LSTM model is constructed. Finally, the feature matrix is divided into a training set and a test set. The training set is input into the model for training, and then the training network is used to predict the test set, and outputs diagnostic results. The simulation and experimental results show that the proposed method can achieve higher accuracy and stronger robustness, compared with the existing intelligent diagnosis methods for bearing compound faults. Full article
(This article belongs to the Special Issue Advances and Applications in Data-Driven Process Monitoring, Fault Diagnosis and Control)
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