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Machines
Special Issues
Intelligent Maintenance and Health Management of Electromechanical Equipment
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Intelligent Maintenance and Health Management of Electromechanical Equipment

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 5110

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. Zihao Lei

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: prognostics and health management; signal processing; machine learning; deep learning; information fusion; digital twin
Dr. Yadong Xu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Interests: prognostics and health management; signal and image processing; machine learning; deep learning; information fusion; digital twin
Dr. Zhijun Ren

E-Mail Website
Guest Editor
1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2. Faculty of Applied Science, University of British Columbia, Kelowna, BC V1V 1V7, Canada
Interests: prognostics and health management; signal processing; machine learning; deep learning; imbalance learning
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
Prof. Dr. Guangrui Wen

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: signal processing; fault diagnostics and prognostics; dynamic balancing; machine learning; life cycle health monitoring and intelligent maintenance

Special Issue Information

Dear Colleagues,

Electromechanical equipment has been extensively used in various industrial applications, such as aerospace, the petrochemical industry, metallurgy, power generation, and various military systems. However, the complex and harsh working environment made the electromechanical equipment prone to failure. Therefore, the maintenance and health management of electromechanical equipment and its key components are essential to ensure their safe and stable operation. Intelligent maintenance and health management aim to combine intelligent tools and key techniques, such as data quality assurance, condition monitoring, fault diagnosis, degradation assessment, and useful life prediction and maintenance decisions, to help avoid unexpected economic loss and even serious accidents caused by the sudden shutdown of electromechanical equipment, so as to realize the continuous advancement of smart manufacturing and the continuous transformation of the industry. Therefore, intelligent maintenance and health management can benefit industrial production and significantly improve productivity and automation.

This Special Issue focuses on advanced algorithms/techniques for the intelligent maintenance and health management of electromechanical equipment.

Potential topics include but are not limited to:

  • Intelligent maintenance and health management based on digital twin;
  • Intelligent maintenance and health management based on signal processing;
  • Intelligent maintenance and health management based on machine learning;
  • Intelligent maintenance and health management based on deep learning;
  • Intelligent maintenance and health management under non-stationary conditions;
  • Intelligent maintenance and health management based on multi-source information fusion;
  • Wear and fatigue analysis.

Dr. Ke Feng
Dr. Zihao Lei
Dr. Yadong Xu
Dr. Zhijun Ren
Dr. Qing Ni
Prof. Dr. Guangrui Wen
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

  • electromechanical equipment
  • non-destructive testing
  • data quality assurance
  • condition monitoring
  • fault diagnosis
  • fault prognosis
  • maintenance decision
  • dynamics
  • signal processing
  • machine learning
  • digital twin

Published Papers (3 papers)

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Research

20 pages, 9995 KiB  
Article
Sound-Based Intelligent Detection of FOD in the Final Assembly of Rocket Tanks
by Tantao Lin, Yongsheng Zhu, Zhijun Ren, Kai Huang, Xinzhuo Zhang, Ke Yan and Shunzhou Huang
Machines 2023, 11(2), 187; https://doi.org/10.3390/machines11020187 - 31 Jan 2023
Cited by 1 | Viewed by 1445
Abstract
The traditional method of relying on human hearing to detect foreign object debris (FOD) events during rocket tank assembly processes has the limitation of strong reliance on humans and difficulty in establishing objective detection records. This can lead to undetected FOD entering the [...] Read more.
The traditional method of relying on human hearing to detect foreign object debris (FOD) events during rocket tank assembly processes has the limitation of strong reliance on humans and difficulty in establishing objective detection records. This can lead to undetected FOD entering the engine with the fuel and causing major launch accidents. In this study, we developed an automatic, intelligent FOD detection system for rocket tanks based on sound signals to overcome the drawbacks of manual detection, enabling us to take action to prevent accidents in advance. First, we used log-Mel transformation to reduce the high sampling rate of the sound signal. Furthermore, we proposed a multiscale convolution and temporal convolutional network (MS-CTCN) to overcome the challenges of multi-scale temporal feature extraction to detect suspicious FOD events. Finally, we used the proposed post-processing strategies of label smoothing and threshold discrimination to refine the results of FOD event detection and ultimately determine the presence of FOD. The proposed method was validated through FOD experiments. The results showed that the method had an accuracy rate of 99.16% in detecting FOD and had a better potential to prevent accidents compared to the baseline method. Full article
(This article belongs to the Special Issue Intelligent Maintenance and Health Management of Electromechanical Equipment)
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14 pages, 4736 KiB  
Article
Linear Quadratic Optimal Control with the Finite State for Suspension System
by Qidi Fu, Jianwei Wu, Chuanyun Yu, Tao Feng, Ning Zhang and Jianrun Zhang
Machines 2023, 11(2), 127; https://doi.org/10.3390/machines11020127 - 17 Jan 2023
Cited by 1 | Viewed by 1334
Abstract
The control algorithm could greatly help the suspension system improve the comprehensive performance of the vehicle. Existing control methods need to obtain the intermediate states, which are difficult to obtain directly or accurately when estimated by filters or observers. Thus, this paper proposed [...] Read more.
The control algorithm could greatly help the suspension system improve the comprehensive performance of the vehicle. Existing control methods need to obtain the intermediate states, which are difficult to obtain directly or accurately when estimated by filters or observers. Thus, this paper proposed a new practical finite state LQR control method to deal with this problem. By combining with the output state of the finite sensor of the vehicle suspension system and weakening the unknown state as the goal, an optimization model is established with the design variables as the LQR weight coefficients. Then, the direct relationship between the current control input and the finite sensor output is obtained, and the finite state LQR control is realized. Taking the quarter-car suspension model as an example, the corresponding noise is added considering sensor accuracy, and the control performance of the four control methods is studied considering the uncertainties of suspension system parameters. In addition, the acceleration of sprung mass and the dynamic travel coefficient of suspension have been separately calculated by methods of finite state LQR control, LQR control, and PID control. The results show that there is not much difference between them under shock excitation or random excitation. However, the finite state LQR control method has the best comprehensive control performance in that its dynamic tire load coefficient is better than other methods; it could take into account the suspension work stroke coefficient, dynamic tire load coefficient, and sprung mass’ acceleration of the vehicle suspension system at the same time. In order to realize the optimal control effect with limited sensor arrangement, the finite state LQR control method only needs to obtain the current sensor output and the current control input, without estimating the unknown intermediate state. By this means, the proposed control method greatly simplifies the design of the control system and has great advantages on practical value. Full article
(This article belongs to the Special Issue Intelligent Maintenance and Health Management of Electromechanical Equipment)
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19 pages, 4462 KiB  
Article
Dimensionality Reduction Methods of a Clustered Dataset for the Diagnosis of a SCADA-Equipped Complex Machine
by Luca Viale, Alessandro Paolo Daga, Alessandro Fasana and Luigi Garibaldi
Machines 2023, 11(1), 36; https://doi.org/10.3390/machines11010036 - 29 Dec 2022
Cited by 4 | Viewed by 1569
Abstract
Machinery diagnostics in the industrial field have assumed a fundamental role for both technical, economic and safety reasons. The use of sensors, data collection and analysis has increasingly advanced to investigate the health of machinery, predict the presence of faults and recognize their [...] Read more.
Machinery diagnostics in the industrial field have assumed a fundamental role for both technical, economic and safety reasons. The use of sensors, data collection and analysis has increasingly advanced to investigate the health of machinery, predict the presence of faults and recognize their nature. The amount of data necessary for this purpose means that it is often necessary to implement dimension reduction methods to pre-process the useful features for the classification. Furthermore, the use of a multi-class dataset could involve data clustering in its multi-dimensional space. This study proposes a novel dimensionality reduction method, consisting of the combination of two different techniques. It aims at improving the quality of the features and, consequently, the classification performance with high-dimension clustered datasets. In addition, a case study is analyzed thanks to the data published by the Prognostics and Health Management Europe (PHME) society on the Data Challenge 2021. The results show an excellent recognition of the machine state of health both in terms of damage detection and identification. The performance indices also show an improvement in classification compared to other dimension reduction methods. Full article
(This article belongs to the Special Issue Intelligent Maintenance and Health Management of Electromechanical Equipment)
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