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Intelligent Control Technology and Fault Detection Analysis of Mechanical Equipment
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Intelligent Control Technology and Fault Detection Analysis of Mechanical Equipment

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (1 May 2023) | Viewed by 6908

Special Issue Editors

Dr. Haiyang Pan

E-Mail Website
Guest Editor
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
Interests: equipment status monitoring and fault diagnosis; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Haidong Shao

E-Mail Website
Guest Editor
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: intelligent fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinde Zheng

E-Mail Website
Guest Editor
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
Interests: signal processing; fault diagnosis; feature extraction; nonlinear dynamics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qingyun Liu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
Interests: machinery health monitoring; sensor technology; robot structure design; automatic control

Special Issue Information

Dear Colleagues,

With the rapid development of the manufacturing industry, the demand for precise mechanical equipment is increasing, but the traditional mechanical equipment control method struggles to meet the complex working environment. At present, relevant experts are gradually introducing intelligent control technology into mechanical equipment, which helps to improve the working efficiency of machinery and significantly reduce safety accidents.

In the intelligent process of mechanical equipment, the structure of the equipment presents complexity and precision, which means that the mechanical equipment must implement the condition monitoring of the equipment on the premise of safety and reliability, so as to ensure the effective implementation of intelligent equipment.

The purpose of this subject is to promote the construction of mechanical intelligence and monitoring platform, and propose a variety of novel high-quality mechanical equipment fault diagnosis and intelligent control methods. We welcome both original research articles and review articles discussing the current state of the art.

Topics include, but are not limited to:

(1) Fault detection;

(2) Online monitoring, intelligent diagnosis and prediction;

(3) Health condition evaluation;

(4) Dynamic modeling and analysis;

(5) Advanced vibration signal analysis;

(6) Sensor technology;

(7) Automatic control technology.

If you want to learn more information or need any advice, you can contact the Special Issue Editor Andrea Chen via <[email protected]> directly.

Dr. Haiyang Pan
Prof. Dr. Haidong Shao
Prof. Dr. Jinde Zheng
Prof. Dr. Qingyun Liu
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. Sensors 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 2600 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 learning
  • signal processing
  • fault diagnosis
  • automatic control
  • feature extraction
  • sensor technology

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Published Papers (2 papers)

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24 pages, 13613 KiB  
Article
An Adaptive Parameterized Domain Mapping Method and Its Application in Wheel–Rail Coupled Fault Diagnosis for Rail Vehicles
by Zihang Xu, Jianwei Yang, Dechen Yao, Jinhai Wang and Minghui Wei
Sensors 2023, 23(12), 5486; https://doi.org/10.3390/s23125486 - 10 Jun 2023
Viewed by 1378
Abstract
The rapid development of cities in recent years has increased the operational pressure of rail vehicles, and due to the characteristics of rail vehicles, including harsh operating environment, frequent starting and braking, resulting in rails and wheels being prone to rail corrugation, polygons, [...] Read more.
The rapid development of cities in recent years has increased the operational pressure of rail vehicles, and due to the characteristics of rail vehicles, including harsh operating environment, frequent starting and braking, resulting in rails and wheels being prone to rail corrugation, polygons, flat scars and other faults. These faults are coupled in actual operation, leading to the deterioration of the wheel–rail contact relationship and causing harm to driving safety. Hence, the accurate detection of wheel–rail coupled faults will improve the safety of rail vehicles’ operation. The dynamic modeling of rail vehicles is carried out to establish the character models of wheel–rail faults including rail corrugation, polygonization and flat scars to explore the coupling relationship and characteristics under variable speed conditions and to obtain the vertical acceleration of the axle box. An APDM time–frequency analysis method is proposed in this paper based on the PDMF adopting Rényi entropy as the evaluation index and employing a WOA to optimize the parameter set. The number of iterations of the WOA adopted in this paper is decreased by 26% and 23%, respectively, compared with PSO and SSA, which means that the WOA performs at faster convergence speed and with a more accurate Rényi entropy value. Additionally, TFR obtained using APDM realizes the localization and extraction of the coupled fault characteristics under rail vehicles’ variable speed working conditions with higher energy concentration and stronger noise resistance corresponding to prominent ability of fault diagnosis. Finally, the effectiveness of the proposed method is verified using simulation and experimental results that prove the engineering application value of the proposed method. Full article
(This article belongs to the Special Issue Intelligent Control Technology and Fault Detection Analysis of Mechanical Equipment)
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Review

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17 pages, 1307 KiB  
Review
Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities
by Gabriel Avelino R. Sampedro, Syifa Maliah Rachmawati, Dong-Seong Kim and Jae-Min Lee
Sensors 2022, 22(23), 9446; https://doi.org/10.3390/s22239446 - 2 Dec 2022
Cited by 16 | Viewed by 4733
Abstract
Three-dimensional printing, often known as additive manufacturing (AM), is a groundbreaking technique that enables rapid prototyping. Monitoring AM delivers benefits, as monitoring print quality can prevent waste and excess material costs. Machine learning is often applied to automating fault detection processes, especially in [...] Read more.
Three-dimensional printing, often known as additive manufacturing (AM), is a groundbreaking technique that enables rapid prototyping. Monitoring AM delivers benefits, as monitoring print quality can prevent waste and excess material costs. Machine learning is often applied to automating fault detection processes, especially in AM. This paper explores recent research on machine learning-based mechanical fault monitoring systems in fused deposition modeling (FDM). Specifically, various machine learning-based algorithms are applied to measurements extracted from different parts of a 3D printer to diagnose and identify faults. The studies often use mechanical-based fault analysis from data gathered from sensors that measure attitude, acoustic emission, acceleration, and vibration signals. This survey examines what has been achieved and opens up new opportunities for further research in underexplored areas such as SLM-based mechanical fault monitoring. Full article
(This article belongs to the Special Issue Intelligent Control Technology and Fault Detection Analysis of Mechanical Equipment)
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