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Deep Learning-Based Machinery Fault Diagnostics
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Deep Learning-Based Machinery Fault Diagnostics

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (28 May 2022) | Viewed by 31473

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Hongtian Chen

E-Mail Website
Guest Editor
Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Interests: fault detection and diagnosis; high-speed trains; data mining and analytics; machine learning; quantum computation
Special Issues, Collections and Topics in MDPI journals
Dr. Kai Zhong

E-Mail Website
Guest Editor
Institutes of Physical Science and Information Technology, Anhui University, Anhui 230601, China
Interests: deep learning; machinery fault diagnosis; graph neural networks
Dr. Guangtao Ran

E-Mail Website
Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Interests: learning-based control; fault diagnosis; event-based control
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,

Papers are sought that address innovative solutions to the development and use of deep learning techniques for the monitoring and/or diagnosis of faults in machinery equipment for the purpose of advancing fault diagnosis science, and its applications in various machinery.

The scope of these papers may encompass (1) theory, methodology, and practice of deep learning; (2) analysis, representation, display, and preservation of information obtained from a set of machinery to carry out fault diagnosis and operating condition monitoring; and (3) scientific and technical support for the establishment and maintenance of technical standards in the field of machinery fault diagnosis.

In this Special Issue, original research articles and reviews are welcome. We look forward to receiving your contributions.

Dr. Hongtian Chen
Dr. Kai Zhong
Dr. Guangtao Ran
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. Machines 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

  • deep learning
  • signal processing
  • fault diagnosis
  • operating condition monitoring
  • machinery equipment

Published Papers (16 papers)

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Editorial

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4 pages, 178 KiB  
Editorial
Deep Learning-Based Machinery Fault Diagnostics
by Hongtian Chen, Kai Zhong, Guangtao Ran and Chao Cheng
Machines 2022, 10(8), 690; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10080690 - 13 Aug 2022
Cited by 2 | Viewed by 1685
Abstract
In recent years, deep learning has shown its unique potential and advantages in feature extraction and pattern recognition [...] Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)

Research

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18 pages, 4298 KiB  
Article
Thruster Fault Diagnostics and Fault Tolerant Control for Autonomous Underwater Vehicle with Ocean Currents
by Qunhong Tian, Tao Wang, Bing Liu and Guangtao Ran
Machines 2022, 10(7), 582; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10070582 - 18 Jul 2022
Cited by 11 | Viewed by 1723
Abstract
Autonomous underwater vehicle (AUV) is one of the most important exploration tools in the ocean underwater environment, whose movement is realized by the underwater thrusters, however, the thruster fault happens frequently in engineering practice. Ocean currents perturbations could produce noise for thruster fault [...] Read more.
Autonomous underwater vehicle (AUV) is one of the most important exploration tools in the ocean underwater environment, whose movement is realized by the underwater thrusters, however, the thruster fault happens frequently in engineering practice. Ocean currents perturbations could produce noise for thruster fault diagnosis, in order to solve the thruster fault diagnostics, a possibilistic fuzzy C-means (PFCM) algorithm is proposed to realize the fault classification in this paper. On the basis of the results of fault diagnostics, a fuzzy control strategy is proposed to solve the fault tolerant control for AUV. Considering the uncertainty of ocean currents, it proposes a min-max robust optimization problem to optimize the fuzzy controller, which is solved by a cooperative particle swarm optimization (CPSO) algorithm. Simulation and underwater experiments are used to verify the accuracy and feasibility of the proposed method of thruster fault diagnostics and fault tolerant control. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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14 pages, 888 KiB  
Article
Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning
by Hong Zheng, Keyuan Zhu, Chao Cheng and Zhaowang Fu
Machines 2022, 10(7), 526; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10070526 - 28 Jun 2022
Cited by 2 | Viewed by 1309
Abstract
Online monitors of the running gears systems of high-speed trains play critical roles in ensuring operational safety and reliability. Status signals collected from high-speed train running gears are very complex regarding working environments, random noises and many other real-world constraints. This paper proposed [...] Read more.
Online monitors of the running gears systems of high-speed trains play critical roles in ensuring operational safety and reliability. Status signals collected from high-speed train running gears are very complex regarding working environments, random noises and many other real-world constraints. This paper proposed fault detection (FD) models using canonical correlation analysis (CCA) and just-in-time learning (JITL) to process scalar signals of high-speed train gears, named as CCA-JITL. After data preprocessing and normalization, CCA transforms covariance matrices of high-dimension historical data into low-dimension subspaces and maximizes correlations between the most important latent dimensions. Then, JITL components formulate local FD models which utilize subsets of testing samples with larger Euclidean distances to training data. A case study introduced a novel system design of an online FD architecture and demonstrated that CCA-JITL FD models significantly outperformed traditional CCA models. The approach is applicable to other dimension reduction FD models such as PCA and PLS. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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16 pages, 2982 KiB  
Article
Fault Detection of Bearing by Resnet Classifier with Model-Based Data Augmentation
by Lu Qian, Qing Pan, Yaqiong Lv and Xingwei Zhao
Machines 2022, 10(7), 521; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10070521 - 27 Jun 2022
Cited by 16 | Viewed by 2430
Abstract
It is always an important and challenging issue to achieve an effective fault diagnosis in rotating machinery in industries. In recent years, deep learning proved to be a high-accuracy and reliable method for data-based fault detection. However, the training of deep learning algorithms [...] Read more.
It is always an important and challenging issue to achieve an effective fault diagnosis in rotating machinery in industries. In recent years, deep learning proved to be a high-accuracy and reliable method for data-based fault detection. However, the training of deep learning algorithms requires a large number of real data, which is generally expensive and time-consuming. To cope with this, we proposed a Resnet classifier with model-based data augmentation, which is applied for bearing fault detection. To this end, a dynamic model was first established to describe the bearing system by adjusting model parameters, such as speed, load, fault size, and the different fault types. Large amounts of data under various operation conditions can then be generated. The training dataset was constructed by the simulated data, which was then applied to train the Resnet classifier. In addition, in order to reduce the gap between the simulation data and the real data, the envelop signals were used instead of the original signals in the training process. Finally, the effectiveness of the proposed method was demonstrated by the real bearing experimental data. It is remarkable that the application of the proposed method can be further extended to other mechatronic systems with a deterministic dynamic model. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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14 pages, 1354 KiB  
Article
A Local Density-Based Abnormal Case Removal Method for Industrial Operational Optimization under the CBR Framework
by Xiangyu Peng, Yalin Wang, Lin Guan and Yongfei Xue
Machines 2022, 10(6), 471; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10060471 - 12 Jun 2022
Cited by 3 | Viewed by 1520
Abstract
Operational optimization is essential in modern industry and unsuitable operations will deteriorate the performance of industrial processes. Since measuring error and multiple working conditions are inevitable in practice, it is necessary to reduce their negative impacts on operational optimization under the case-based reasoning [...] Read more.
Operational optimization is essential in modern industry and unsuitable operations will deteriorate the performance of industrial processes. Since measuring error and multiple working conditions are inevitable in practice, it is necessary to reduce their negative impacts on operational optimization under the case-based reasoning (CBR) framework. In this paper, a local density-based abnormal case removal method is proposed to remove the abnormal cases in a case retrieval step, so as to prevent performance deterioration in industrial operational optimization. More specifically, the reasons as to why classic CBR would retrieve abnormal cases are analyzed from the perspective of case retrieval in industry. Then, a local density-based abnormal case removal algorithm is designed based on the Local Outlier Factor (LOF), and properly integrated into the traditional case retrieval step. Finally, the effectiveness and the superiority of the local density-based abnormal case removal method was tested by a numerical simulation and an industrial case study of the cut-made process of cigarette production. The results show that the proposed method improved the operational optimization performance of an industrial cut-made process by 23.5% compared with classic CBR, and by 13.3% compared with case-based fuzzy reasoning. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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22 pages, 4194 KiB  
Article
An Improved Fault Diagnosis Approach Using LSSVM for Complex Industrial Systems
by Shuyue Guan, Darong Huang, Shenghui Guo, Ling Zhao and Hongtian Chen
Machines 2022, 10(6), 443; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10060443 - 04 Jun 2022
Cited by 8 | Viewed by 1527
Abstract
Fault diagnosis is a challenging topic for complex industrial systems due to the varying environments such systems find themselves in. In order to improve the performance of fault diagnosis, this study designs a novel approach by using particle swarm optimization (PSO) with wavelet [...] Read more.
Fault diagnosis is a challenging topic for complex industrial systems due to the varying environments such systems find themselves in. In order to improve the performance of fault diagnosis, this study designs a novel approach by using particle swarm optimization (PSO) with wavelet mutation and least square support (LSSVM). The implementation entails the following three steps. Firstly, the original signals are decomposed through an orthogonal wavelet packet decomposition algorithm. Secondly, the decomposed signals are reconstructed to obtain the fault features. Finally, the extracted features are used as the inputs of the fault diagnosis model established in this research to improve classification accuracy. This joint optimization method not only solves the problem of PSO falling easily into the local extremum, but also improves the classification performance of fault diagnosis effectively. Through experimental verification, the wavelet mutation particle swarm optimazation and least sqaure support vector machine ( WMPSO-LSSVM) fault diagnosis model has a maximum fault recognition efficiency that is 12% higher than LSSVM and 9% higher than extreme learning machine (ELM). The error of the corresponding regression model under the WMPSO-LSSVM algorithm is 0.365 less than that of the traditional linear regression model. Therefore, the proposed fault scheme can effectively identify faults that occur in complex industrial systems. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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16 pages, 3958 KiB  
Article
An Adaptive Fusion Convolutional Denoising Network and Its Application to the Fault Diagnosis of Shore Bridge Lift Gearbox
by Rongqiang Zhao and Xiong Hu
Machines 2022, 10(6), 424; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10060424 - 26 May 2022
Cited by 2 | Viewed by 1471
Abstract
Traditional fault diagnosis methods are limited in the condition detection of shore bridge lifting gearboxes due to their limited ability to extract signal features and their sensitivity to noise. In order to solve this problem, an adaptive fusion convolutional denoising network (AF-CDN) was [...] Read more.
Traditional fault diagnosis methods are limited in the condition detection of shore bridge lifting gearboxes due to their limited ability to extract signal features and their sensitivity to noise. In order to solve this problem, an adaptive fusion convolutional denoising network (AF-CDN) was proposed in this paper. First, a novel 1D and 2D adaptive fused convolutional neural network structure is built. The fusion of both 1D and 2D convolutional models can effectively improve the feature extraction capability of the network. Then, a gradient updating method based on the Kalman filter mechanism is designed. The effectiveness of the developed method is evaluated by using the benchmark datasets and the actual data collected for the shore bridge lift gearbox. Finally, the effectiveness of the proposed algorithm is proved through the experimental validation in the paper. The main contributions of this paper are described as follows: the proposed AF-CDN can improve the diagnosis accuracy by 1.5–9.1% when compared with the normal CNN methods. The robustness of the diagnostic network can be significantly improved. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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17 pages, 1387 KiB  
Article
Fault Detection for Interval Type-2 T-S Fuzzy Networked Systems via Event-Triggered Control
by Zhongda Lu, Chunda Zhang, Fengxia Xu, Zifei Wang and Lijing Wang
Machines 2022, 10(5), 347; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10050347 - 08 May 2022
Cited by 3 | Viewed by 1425
Abstract
This paper investigates the event-triggered fault diagnosis (FD) problem for interval type-2 (IT2) Takagi–Sugeno (T-S) fuzzy networked systems. Firstly, an FD fuzzy filter is proposed by using IT2 T-S fuzzy theory to generate a residual signal. This means that the FD filter premise [...] Read more.
This paper investigates the event-triggered fault diagnosis (FD) problem for interval type-2 (IT2) Takagi–Sugeno (T-S) fuzzy networked systems. Firstly, an FD fuzzy filter is proposed by using IT2 T-S fuzzy theory to generate a residual signal. This means that the FD filter premise variable needs to not be identical to the nonlinear networked systems (NNSs). The evaluation functions are referenced to determine the occurrence of system faults. Secondly, under the event-triggered mechanism, a fault residual system (FRS) is established with parameter uncertainty, external disturbance and time delay, which can reduce signal transmission and communication pressure. Thirdly, the progressive stability of the fault residual system is guaranteed by using the Lyapunov theory. For the energy bounded condition of external noise interference, the performance criterion is established using linear matrix inequalities. The matrix parameters of the target FD filter are obtained by the convex optimization method. A less conservative fault diagnosis method can be obtained. Finally, the simulation example is provided to illustrate the effectiveness and the practicalities of the proposed theoretical method. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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27 pages, 9713 KiB  
Article
Health Assessment of Complex System Based on Evidential Reasoning Rule with Transformation Matrix
by Zhigang Li, Zhijie Zhou, Jie Wang, Wei He and Xiangyi Zhou
Machines 2022, 10(4), 250; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10040250 - 31 Mar 2022
Cited by 5 | Viewed by 1507
Abstract
In current research of complex system health assessment with evidential reasoning (ER) rule, the relationship between the indicators reference grades and pre-defined assessment result grades is regarded as a one to one correspondence. However, in engineering practice, this strict mapping relationship is difficult [...] Read more.
In current research of complex system health assessment with evidential reasoning (ER) rule, the relationship between the indicators reference grades and pre-defined assessment result grades is regarded as a one to one correspondence. However, in engineering practice, this strict mapping relationship is difficult to meet, and it may degrease the accuracy of the assessment. Therefore, a new ER rule-based health assessment model for a complex system with a transformation matrix is adopted. First, on the basis of the rule-based transformation technique, expert knowledge is embedded on the transformation matrix to solve the inconsistent problems between the input and the output, which keeps completeness and consistency of information transformation. Second, a complete health assessment model is established via the calculation and optimization of the model parameters. Finally, the effectiveness of the proposed model can be validated in contrast with other methods. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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18 pages, 4836 KiB  
Article
Fault Diagnosis of Motor Vibration Signals by Fusion of Spatiotemporal Features
by Lijing Wang, Chunda Zhang, Juan Zhu and Fengxia Xu
Machines 2022, 10(4), 246; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10040246 - 30 Mar 2022
Cited by 11 | Viewed by 2233
Abstract
This paper constructs a spatiotemporal feature fusion network (STNet) to enhance the influence of spatiotemporal features of signals on the diagnostic performance during motor fault diagnosis. The STNet consists of the spatial feature processing capability of convolutional neural networks (CNN) and the temporal [...] Read more.
This paper constructs a spatiotemporal feature fusion network (STNet) to enhance the influence of spatiotemporal features of signals on the diagnostic performance during motor fault diagnosis. The STNet consists of the spatial feature processing capability of convolutional neural networks (CNN) and the temporal feature processing capability of recurrent neural networks (RNN). It is used for fault diagnosis of motor vibration signals. The network uses dual-stream branching to extract the fault features of motor vibration signals by a convolutional neural network and gated recurrent unit (GRU) simultaneously. The features are also enhanced by using the attention mechanism. Then, the temporal and spatial features are fused and input into the softmax function for fault discrimination. After that, the fault diagnosis of motor vibration signals is completed. In addition, several sets of experimental evaluations are conducted. The experimental results show that the vibration signal processing method combined with spatiotemporal features can effectively improve the recognition accuracy of motor faults. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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15 pages, 5030 KiB  
Article
Multipoint Feeding Strategy of Aluminum Reduction Cell Based on Distributed Subspace Predictive Control
by Jiarui Cui, Peining Wang, Xiangquan Li, Ruoyu Huang, Qing Li, Bin Cao and Hui Lu
Machines 2022, 10(3), 220; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10030220 - 21 Mar 2022
Cited by 2 | Viewed by 1768
Abstract
With the continuous development of large-scale aluminum reduction cells, the problem of the uniform distribution of alumina concentration in the cell has become more and more serious for the reduction process. In order to achieve the uniform distribution of the alumina concentration, a [...] Read more.
With the continuous development of large-scale aluminum reduction cells, the problem of the uniform distribution of alumina concentration in the cell has become more and more serious for the reduction process. In order to achieve the uniform distribution of the alumina concentration, a data-driven distributed subspace predictive control feeding strategy is proposed in this paper. Firstly, the aluminum reduction cell is divided into multiple sub-systems that affect each other according to the position of the feeding port. Based on the subspace method, the prediction model of the whole cell is identified, and the prediction output expression of each sub-system is deduced by decomposition. Secondly, the feeding controller is designed for each aluminum reduction cell subsystem, and the input and output information can be exchanged between each controller through the network. Thirdly, under consideration of the influence of other subsystems, each subsystem solves the Nash-optimal control feeding quantity, so that each subsystem realizes distributed feeding. Finally, the simulation results show that, compared with the traditional control method, the proposed distributed feeding control strategy can significantly improve the problem of the uniform distribution of alumina concentration and improve the current efficiency of the aluminum reduction cell. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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24 pages, 5701 KiB  
Article
A Model for Flywheel Fault Diagnosis Based on Fuzzy Fault Tree Analysis and Belief Rule Base
by Xiaoyu Cheng, Shanshan Liu, Wei He, Peng Zhang, Bing Xu, Yawen Xie and Jiayuan Song
Machines 2022, 10(2), 73; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10020073 - 20 Jan 2022
Cited by 9 | Viewed by 2302
Abstract
In the fault diagnosis of the flywheel system, the input information of the system is uncertain. This uncertainty is mainly caused by the interference of environmental factors and the limited cognitive ability of experts. The BRB (belief rule base) shows a good ability [...] Read more.
In the fault diagnosis of the flywheel system, the input information of the system is uncertain. This uncertainty is mainly caused by the interference of environmental factors and the limited cognitive ability of experts. The BRB (belief rule base) shows a good ability for dealing with problems of information uncertainty and small sample data. However, the initialization of the BRB relies on expert knowledge, and it is difficult to obtain the accurate knowledge of flywheel faults when constructing BRB models. Therefore, this paper proposes a new BRB model, called the FFBRB (fuzzy fault tree analysis and belief rule base), which can effectively solve the problems existing in the BRB. The FFBRB uses the Bayesian network as a bridge, uses an FFTA (fuzzy fault tree analysis) mechanism to build the BRB’s expert knowledge, uses ER (evidential reasoning) as its reasoning tool, and uses P-CMA-ES (projection covariance matrix adaptation evolutionary strategies) as its optimization model algorithm. The feasibility and superiority of the proposed method are verified by an example of a flywheel friction torque fault tree. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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20 pages, 2518 KiB  
Article
Intelligent Fault Diagnosis Method for Blade Damage of Quad-Rotor UAV Based on Stacked Pruning Sparse Denoising Autoencoder and Convolutional Neural Network
by Pu Yang, Chenwan Wen, Huilin Geng and Peng Liu
Machines 2021, 9(12), 360; https://0-doi-org.brum.beds.ac.uk/10.3390/machines9120360 - 16 Dec 2021
Cited by 10 | Viewed by 2448
Abstract
This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network (sPSDAE-CNN). This method processes the original input data by using a stack denoising autoencoder. Different from the traditional autoencoder, stack pruning sparse denoising [...] Read more.
This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network (sPSDAE-CNN). This method processes the original input data by using a stack denoising autoencoder. Different from the traditional autoencoder, stack pruning sparse denoising autoencoder includes a fully connected autoencoding network, the features extracted from the front layer of the network are used for the operation of the subsequent layer, which means that some new connections will appear between the front and rear layers of the network, reduce the loss of information, and obtain more effective features. Firstly, a one-dimensional sliding window is introduced for data enhancement. In addition, transforming one-dimensional time-domain data into the two-dimensional gray image can further improve the deep learning (DL) ability of models. At the same time, pruning operation is introduced to improve the training efficiency and accuracy of the network. The convolutional neural network model with sPSDAE has a faster training speed, strong adaptability to noise interference signals, and can also suppress the over-fitting problem of the convolutional neural network to a certain extent. Actual experiments show that for the fault of unmanned aerial vehicle (UAV) blade damage, the sPSDAE-CNN model we use has better stability and reliable prediction accuracy than traditional convolutional neural networks. At the same time, For noise signals, better results can be obtained. The experimental results show that the sPSDAE-CNN model still has a good diagnostic accuracy rate in a high-noise environment. In the case of a signal-to-noise ratio of −4, it still has an accuracy rate of 90%. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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15 pages, 985 KiB  
Article
Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method
by Shubin Wang, Yukun Tian, Xiaogang Deng, Qianlei Cao, Lei Wang and Pengxiang Sun
Machines 2021, 9(11), 272; https://0-doi-org.brum.beds.ac.uk/10.3390/machines9110272 - 06 Nov 2021
Cited by 4 | Viewed by 1513
Abstract
Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVAkNN, for monitoring the disturbances of power transmission systems. In the proposed [...] Read more.
Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVAkNN, for monitoring the disturbances of power transmission systems. In the proposed method, CVA is first used to extract the dynamic features by analyzing the data correlation and establish a statistical model with two monitoring statistics T2 and Q. Then, in order to handling the periodic oscillation of power data, the two statistics are reconstructed in phase space, and the k-nearest neighbor (kNN) technique is applied to design the statistics nearest neighbor distance DT2 and DQ as the enhanced monitoring indices. Further considering the detection difficulty of weak disturbances with the insignificant symptoms, statistical local analysis (SLA) is integrated to construct the primary and improved residual vectors of the CVA dynamic features, which are capable to prompt the disturbance detection sensitivity. The verification results on the real industrial data show that the SLCVAkNN method can detect the occurrence of power system disturbance more effectively than the traditional data-driven monitoring methods. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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16 pages, 458 KiB  
Article
Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems
by Chen Xu and Yawen Mao
Machines 2021, 9(11), 247; https://0-doi-org.brum.beds.ac.uk/10.3390/machines9110247 - 23 Oct 2021
Cited by 10 | Viewed by 1547
Abstract
This paper focuses on the nonlinear system identification problem, which is a basic premise of control and fault diagnosis. For Hammerstein output-error nonlinear systems, we propose an auxiliary model-based multi-innovation fractional stochastic gradient method. The scalar innovation is extended to the innovation vector [...] Read more.
This paper focuses on the nonlinear system identification problem, which is a basic premise of control and fault diagnosis. For Hammerstein output-error nonlinear systems, we propose an auxiliary model-based multi-innovation fractional stochastic gradient method. The scalar innovation is extended to the innovation vector for increasing the data use based on the multi-innovation identification theory. By establishing appropriate auxiliary models, the unknown variables are estimated and the improvement in the performance of parameter estimation is achieved owing to the fractional-order calculus theory. Compared with the conventional multi-innovation stochastic gradient algorithm, the proposed method is validated to obtain better estimation accuracy by the simulation results. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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19 pages, 3058 KiB  
Article
A Process Monitoring Method Based on Dynamic Autoregressive Latent Variable Model and Its Application in the Sintering Process of Ternary Cathode Materials
by Ning Chen, Fuhai Hu, Jiayao Chen, Zhiwen Chen, Weihua Gui and Xu Li
Machines 2021, 9(10), 229; https://0-doi-org.brum.beds.ac.uk/10.3390/machines9100229 - 07 Oct 2021
Cited by 7 | Viewed by 1899
Abstract
Due to the ubiquitous dynamics of industrial processes, the variable time lag raises great challenge to the high-precision industrial process monitoring. To this end, a process monitoring method based on the dynamic autoregressive latent variable model is proposed in this paper. First, from [...] Read more.
Due to the ubiquitous dynamics of industrial processes, the variable time lag raises great challenge to the high-precision industrial process monitoring. To this end, a process monitoring method based on the dynamic autoregressive latent variable model is proposed in this paper. First, from the perspective of process data, a dynamic autoregressive latent variable model (DALM) with process variables as input and quality variables as output is constructed to adapt to the variable time lag characteristic. In addition, a fusion Bayesian filtering, smoothing and expectation maximization algorithm is used to identify model parameters. Then, the process monitoring method based on DALM is constructed, in which the process data are filtered online to obtain the latent space distribution of the current state, and T2 statistics are constructed. Finally, by comparing with an existing method, the feasibility and effectiveness of the proposed method is tested on the sintering process of ternary cathode materials. Detailed comparisons show the superiority of the proposed method. Full article
(This article belongs to the Special Issue Deep Learning-Based Machinery Fault Diagnostics)
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