Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Intelligent Machine Fault Diagnosis
share announcement

Intelligent Machine Fault Diagnosis

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

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 7115

Special Issue Editors

Dr. Ke Feng

E-Mail Website
Guest Editor
School of Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: machine condition monitoring; vibration analysis; fault diagnosis and prognostics; digital twin; dynamic; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Qing Ni

E-Mail Website
Guest Editor
School of Mechanical and Mechatronic Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: fault diagnosis; RUL prediction; vibration analysis; signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yongbo Li

E-Mail Website
Guest Editor
School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: information fusion; digital twin technology; structural health monitoring; fault diagnosis and prognosis; system reliability analysis; dynamic modelling of mechanical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yuejian Chen

E-Mail Website
Guest Editor
Institute of Rail Transit, Tongji University, Shanghai 201804, China
Interests: intelligent sensing; instrumentation; fault diagnostics and prognostics; artificial intelligence and machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoli Zhao

E-Mail Website
Guest Editor
School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210095, China
Interests: fault diagnostics and prognostics; artificial intelligence and machine learning; signal processing

Special Issue Information

Dear Colleagues,

Machinery has been widely applied in various applications, such as wind turbines, vehicles, and aircrafts; however, these complex and harsh working environments make this machinery prone to failure. Thus, it is vital to conduct an assessment of this machinery to guarantee its safe operation and working efficiency, as well as enabling optimal maintenance for decision making. As a critical part of machine health management, intelligent fault diagnostics and the prognostics of the machinery aim to identify the mode, severity, location, and degradation trend of faults. With this fault information, reliable and predictive maintenance-based decisions can be made to help avoid the sudden shutdown of machinery and some unexpected economic loss. Therefore, intelligent machine fault diagnostics and prognostics can significantly benefit industrial production.

This Special Issue focuses on cutting-edge algorithms/techniques for intelligent machine fault diagnostics and prognostics.

Potential topics include but are not limited to:

  • Intelligent machine fault diagnostics and prognostics based on various sensor data;
  • Dynamic analysis for machine condition monitoring;
  • Digital-twin-based fault diagnostics and prognostics;
  • Remaining useful life prediction of the machinery;
  • Machine fault diagnostics under non-stationary operating conditions;
  • Fatigue analysis of machinery;
  • Machine-learning-based fault diagnostics and prognostics.

Dr. Ke Feng
Dr. Qing Ni
Dr. Yongbo Li
Dr. Yuejian Chen
Dr. Xiaoli Zhao
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

  • machine
  • fault diagnostics
  • fault prognostics
  • vibration analysis
  • signal processing
  • machine learning
  • dynamics

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 3345 KiB  
Article
Performance Degradation Prognosis Based on Relative Characteristic and Long Short-Term Memory Network for Components of Brake Systems of in-Service Trains
by Jingxian Ding and Jianyong Zuo
Appl. Sci. 2022, 12(22), 11725; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211725 - 18 Nov 2022
Cited by 2 | Viewed by 989
Abstract
During the service life of brake systems, performance degradation of the components is inevitable. In order to grasp the health status of components of brake systems, and aiming at the problem that the performance degradation trend of the components of the brake system [...] Read more.
During the service life of brake systems, performance degradation of the components is inevitable. In order to grasp the health status of components of brake systems, and aiming at the problem that the performance degradation trend of the components of the brake system is not completely clear due to signal coupling between components, the influence of variable working conditions, and the long performance degradation cycle, a performance degradation prognosis method of the components of the brake system based on relative characteristic (RC) and the long short-term memory (LSTM) network was proposed. The input and output signals of the components were isolated and fused, the working condition-independent RC was extracted to construct the health indicator (HI), and the validity of the HI was tested by using the monotonicity, correlation, and robustness metrics. Moreover, considering the time memory characteristics, the trend prediction of the HI curve of the components of the brake system was carried out based on the LSTM network. Furthermore, data augmentation for the training and testing sets was performed. Taking the typical component of brake systems as an example, a performance degradation test was carried out. The analysis results of the test data show that the accuracy of the performance degradation prognosis of the intake filter was over 99%, which validates the effectiveness and accuracy of the proposed method. The research results could provide a reference for health management and to improve the active safety protection capability of brake systems of in-service trains. Full article
(This article belongs to the Special Issue Intelligent Machine Fault Diagnosis)
►▼ Show Figures

Figure 1

17 pages, 4110 KiB  
Article
Multi-View Information Fusion Fault Diagnosis Method Based on Attention Mechanism and Convolutional Neural Network
by Hongmei Li, Jinying Huang, Minjuan Gao, Luxia Yang and Yichen Bao
Appl. Sci. 2022, 12(22), 11410; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211410 - 10 Nov 2022
Cited by 2 | Viewed by 1217
Abstract
Multi-view information fusion can provide more accurate, complete and reliable data descriptions for monitoring objects, effectively improve the limitations and unreliability of single-view data. Existing multi-view information fusion based on deep learning mostly focuses on the feature level and decision level, with large [...] Read more.
Multi-view information fusion can provide more accurate, complete and reliable data descriptions for monitoring objects, effectively improve the limitations and unreliability of single-view data. Existing multi-view information fusion based on deep learning mostly focuses on the feature level and decision level, with large information loss, and does not distinguish the view weight in the fusion process. To this end, a multi-view data level information fusion model CAM_MCFCNN with view weight was proposed based on a channel attention mechanism and convolutional neural network. The model used the channel characteristics to implement multi-view information fusion at the data level stage, which made the fusion position and mode more natural and reduced the loss of information. A multi-channel fusion convolutional neural network was used for feature learning. In addition, the channel attention mechanism was used to learn the view weight, so that the algorithm could pay more attention to the views that contribute more to the fault identification task during the training process, and more reasonably integrate the information of different views. The proposed method was verified by the data of the planetary gearbox experimental platform. The multi-view data and single-view data were used as the input of the CAM_MCFCNN model and single-channel CNN model respectively for comparison. The average accuracy of CAM_MCFCNN on three constant-speed datasets reached 99.95%, 99.87% and 99.92%, which was an improvement of 0.95%, 2.25%, and 0.04%, compared with the single view with the highest diagnostic accuracy, respectively. When facing limited samples, CAM_MCFCNN had similar performance. Finally, compared with different multi-view information fusion algorithms, CAM_MCFCNN showed better stability and higher accuracy. The experimental results showed that the proposed method had better performance, higher diagnostic accuracy and was more reliable, compared with other methods. Full article
(This article belongs to the Special Issue Intelligent Machine Fault Diagnosis)
►▼ Show Figures

Figure 1

13 pages, 5526 KiB  
Article
Condition Monitoring of an All-Terrain Vehicle Gear Train Assembly Using Deep Learning Algorithms with Vibration Signals
by Sakthivel Gnanasekaran, Lakshmipathi Jakkamputi, Mohanraj Thangamuthu, Senthil Kumar Marikkannan, Jegadeeshwaran Rakkiyannan, Kannan Thangavelu and Gangadhar Kotha
Appl. Sci. 2022, 12(21), 10917; https://0-doi-org.brum.beds.ac.uk/10.3390/app122110917 - 27 Oct 2022
Cited by 4 | Viewed by 1253
Abstract
Condition monitoring of gear train assembly has been carried out with vibration signals acquired from an all-terrain vehicle (ATV) gearbox. The location of the defect in the gear was identified based on finite element analysis results. The vibration signals were acquired using an [...] Read more.
Condition monitoring of gear train assembly has been carried out with vibration signals acquired from an all-terrain vehicle (ATV) gearbox. The location of the defect in the gear was identified based on finite element analysis results. The vibration signals were acquired using an accelerometer under good and simulated fault conditions of the gear. The raw vibration signatures acquired from all the possible conditions of the gear train assembly were processed using the descriptive statistics tool. A set of descriptive statistical features were extracted from the raw vibrational signals. This study used a deep learning algorithm based on the tree family, which includes the decision tree, random forest, and random tree algorithms, to classify gear train conditions. Among the tree family algorithms, the random forest algorithm produced maximum classification accuracy of 99%. The decision rules were used to design an online monitoring system to display the gear condition. This study will help to implement online gear health monitoring in ATVs, ensuring the safety of drivers. Full article
(This article belongs to the Special Issue Intelligent Machine Fault Diagnosis)
►▼ Show Figures

Figure 1

15 pages, 4455 KiB  
Article
Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion
by Yong Wang, Xiaoqiang Guo, Xinhua Liu and Xiaowen Liu
Appl. Sci. 2022, 12(19), 9642; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199642 - 26 Sep 2022
Viewed by 1187
Abstract
To detect the running state of an A-class thermal insulation board production line in real time, conveniently and accurately, a fault diagnosis method based on multi-sensor data fusion was proposed. The proposed algorithm integrates the ideas of Convolutional Neural Network (CNN), Long Short-Term [...] Read more.
To detect the running state of an A-class thermal insulation board production line in real time, conveniently and accurately, a fault diagnosis method based on multi-sensor data fusion was proposed. The proposed algorithm integrates the ideas of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Attention Mechanism, and combines a Dilated Convolution Module (DCM) with LSTM to recognize complex signals of multiple sensors. By introducing an attention mechanism, the recognition performance of the network was improved. Finally, the real-time status information of the production line was obtained by integrating attention weight. Experimental results show that for the custom multi-sensor dataset of A-class insulation board production line, the proposed CNN-LSTM fault diagnosis method achieved 98.97% accuracy. Compared with other popular algorithms, the performance of the proposed CNN-LSTM model performed excellently in each evaluation index is better. Full article
(This article belongs to the Special Issue Intelligent Machine Fault Diagnosis)
►▼ Show Figures

Figure 1

18 pages, 3216 KiB  
Article
Adaptive Residual Life Prediction for Small Samples of Mechanical Products Based on Feature Matching Preprocessor-LSTM
by Yongming Liu, Junyu Song, Zhuanzhe Zhao, Guowen Ye, Zhibo Liu and Yang Zhou
Appl. Sci. 2022, 12(16), 8236; https://0-doi-org.brum.beds.ac.uk/10.3390/app12168236 - 17 Aug 2022
Cited by 2 | Viewed by 1172
Abstract
In order to solve the problem of predicting the residual life of mechanical products accurately based on small-sample data, this paper proposes a small-sample adaptive residual life prediction model of mechanical products based on feature matching preprocessor-LSTM. First, aiming at the problem of [...] Read more.
In order to solve the problem of predicting the residual life of mechanical products accurately based on small-sample data, this paper proposes a small-sample adaptive residual life prediction model of mechanical products based on feature matching preprocessor-LSTM. First, aiming at the problem of low accuracy of remaining life prediction for small samples of mechanical products caused by multiple time scales and multiple fault states, the failure time data and performance degradation data are fused, and the failure rate and standard deviation are used as the remaining life prediction criteria to intuitively reflect The possibility of failure of a component or system at a certain point in time. Considering the demand of adaptive small-sample residual life prediction data, this paper establishes the adaptive matching pre-processor model of life characteristics. On this basis, the LSTM neural network is used to establish a small-sample adaptive residual life prediction model. Then, the XJTU-SY bearing life data set and the test data of the small-sample life characteristics measured by the RV reducer are used as the research objects, and a small amount of the data set is randomly selected. The remaining life expectancy is predicted from the sample data and compared with its standard remaining life, respectively. The comparison results show that the overall prediction error is small. This study shows that the remaining life prediction model established can better predict the remaining life of mechanical product sub-sample data and provides a feasible method for predicting the remaining life of mechanical product sub-samples. Full article
(This article belongs to the Special Issue Intelligent Machine Fault Diagnosis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop