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Machines
Special Issues
Feature Extraction and Condition Monitoring in Physics and Mechanics
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Feature Extraction and Condition Monitoring in Physics and Mechanics

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (6 June 2023) | Viewed by 6185

Special Issue Editors

Dr. Wanqing Song

E-Mail Website
Guest Editor
1. School of Electric and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2. School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
Interests: remaining useful life prediction; feature extrection of stochastic series; reliability analysis; nonlinear dynamic; prediction of stochastic series; long-range dependence; fractional modelling of stochastic series; stochastic signal process
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Villecco

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy
Interests: industrial design; entropy; fuzzy logic; computer-aided design (CAD); axiomatic design; MaxInf principle
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this special issue is to present a multidisciplinary state-of-the-art reference regarding theoretical and real-world challenges, and innovative solutions by inviting high-quality research papers spanning across data analysis tools, mathematical and statistical approaches and data mining techniques. This special issue focuses on the scientific examinations dealing with the behavior of macroscopic systems using the statistical properties of their microscopic components. The statistical physics and mechanics covers a wide range of topics, including microscopic behavior of physical systems, quantum statistical physics, condensed matter, equilibrium and non-equilibrium, statistical thermodynamics, biological and chemical systems such as cell physics, polymer and colloids, fluid mechanics related topics including turbulence, instability and reaction dynamics as well as other interdisciplinary applications. In engineering applications, such as mechanical engineering or electronics engineering, engineers may usually consider it as the output or response of a differential system or filter of integer order under the excitation of white noise. This Special Issue encourages both original research articles and review articles, both theories and applications in advanced statistical and mathematical modeling and in-depth examinations of the physical and mechanical systems. The research papers may incorporate one or a combination of analytical, numerical, statistical and experimental methodologies.

Dr. Wanqing Song
Dr. Francesco Villecco
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

  • artificial intelligence
  • bayesian analysis
  • big data
  • classification
  • clustering analysis
  • condensed matter
  • data analysis
  • data mining
  • decision making
  • distribution theory
  • equilibrium
  • fractals
  • fractional brownian motion
  • fractional calculus
  • machine learning
  • multivariate analysis
  • quantum
  • random forest
  • regression analysis
  • simulation
  • statistical mechanics
  • statistical physics
  • statistical thermodynamics
  • time series analysis
  • turbulence

Published Papers (3 papers)

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Research

18 pages, 2931 KiB  
Article
Dynamics Analysis and Deep Learning-Based Fault Diagnosis of Defective Rolling Element Bearing on the Multi-Joint Robot
by Wentao Zhang, Ting Zhang, Guohua Cui and Ying Pan
Machines 2022, 10(12), 1215; https://doi.org/10.3390/machines10121215 - 14 Dec 2022
Viewed by 1537
Abstract
Industrial robots typically perform a variety of tasks and occupy critical positions in modern manufacturing fields. When certain failures occur in the internal structures of robots, it tends to result in significant financial loss and the consumption of human resources. As a result, [...] Read more.
Industrial robots typically perform a variety of tasks and occupy critical positions in modern manufacturing fields. When certain failures occur in the internal structures of robots, it tends to result in significant financial loss and the consumption of human resources. As a result, timely and effective fault diagnosis is critical to ensuring the safe operation of robots. Deep learning-based methods are currently being widely used by researchers to address some shortcomings of traditional methods. However, due to realistic factor limitations, few researchers take the failure pattern of rotating machinery and the location of fault joints into account at the same time, so the fault types of multi-joint robots are not thoroughly investigated. In this case, we proposed a dynamic simulation method that considers the effects of bearing failures at various faulty joint locations and makes it possible to investigate more possible failure cases of multi-joint robots. In addition, we used LSTM and DCNN to perform staged fault diagnosis tasks, allowing us to gradually locate faulty joints and investigate detailed failure forms. According to the experimental results, distinguishable vibration signals corresponding to various fault states are successfully obtained, and our implemented algorithms are validated for their advanced performance in diagnosis tasks via comparative experiments. Full article
(This article belongs to the Special Issue Feature Extraction and Condition Monitoring in Physics and Mechanics)
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19 pages, 80504 KiB  
Article
Towards a Fault Diagnosis Method for Rolling Bearings with Time-Frequency Region-Based Convolutional Neural Network
by Jiahui Tang, Jimei Wu, Bingbing Hu and Jiajuan Qing
Machines 2022, 10(12), 1145; https://doi.org/10.3390/machines10121145 - 01 Dec 2022
Viewed by 1211
Abstract
An artificial-intelligence (AI)-based method for fault diagnosis is a strong candidate for industrial applications in the health management of rolling bearings. However, traditional fault diagnosis methods fail to improve the detection accuracy because they only extract a single feature and have limitations in [...] Read more.
An artificial-intelligence (AI)-based method for fault diagnosis is a strong candidate for industrial applications in the health management of rolling bearings. However, traditional fault diagnosis methods fail to improve the detection accuracy because they only extract a single feature and have limitations in feature representation. In addition, advanced object detection frameworks such as region-based convolutional neural networks have not yet been applied in fault diagnosis. To this end, a fault diagnosis model using a Time-Frequency Region-Based Convolutional Neural Network (TF-RCNN) is proposed in this paper. This method was mainly adopted to extract multiple regions that can characterize fault features from the Time-Frequency Representation (TFR). Specifically, an attention module was introduced so the model could focus on representative features. The existing classification strategy was also enhanced to perform multiple types of fault classification. Finally, an end-to-end rolling bearing fault diagnosis framework based on the TF-RCNN was developed with the aforementioned improvements. The effectiveness of this method was proven experimentally on artificial faults and real faults. The superiority of the proposed method is demonstrated using a comparison with the typical object detection method and an advanced fault diagnosis method. Full article
(This article belongs to the Special Issue Feature Extraction and Condition Monitoring in Physics and Mechanics)
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20 pages, 5247 KiB  
Article
A Subway Sliding Plug Door System Health State Adaptive Assessment Method Based on Interval Intelligent Recognition of Rotational Speed Operation Data Curve
by Hui Qi, Gaige Chen, Hongbo Ma, Xianzhi Wang and Yudong Yang
Machines 2022, 10(11), 1075; https://doi.org/10.3390/machines10111075 - 15 Nov 2022
Cited by 1 | Viewed by 1384
Abstract
The subway sliding plug door system is crucial for ensuring normal operation. Due to the differences in the structure and motor control procedures of different sliding plug door systems, the rotational speed monitoring data curves show great differences. It is a challenging problem [...] Read more.
The subway sliding plug door system is crucial for ensuring normal operation. Due to the differences in the structure and motor control procedures of different sliding plug door systems, the rotational speed monitoring data curves show great differences. It is a challenging problem to recognize the intervals of complex data curves, which fundamentally affect the sensitivity of feature extraction and the prediction of an assessment model. Aiming at the problem, a subway sliding plug door system health state adaptive assessment method is proposed based on interval intelligent recognition of rotational speed operation data curve. In the proposed method, firstly, the rotational speed operation data curve is adaptively divided by a long short-term memory (LSTM) neural network into four intervals, according to the motion characteristics of the door system. Secondly, the sensitive features of the door system are screened out by the random forest (RF) algorithm. Finally, the health state of the door system is assessed using the adaptive boosting (AdaBoost) classifier. The proposed method is comprehensively verified by the benchmark experiment data set. The results show that the average diagnostic accuracy of the method on multiple bench doors can reach 98.15%. The wider application scope and the higher state classification accuracy indicate that the proposed method has important engineering value and theoretical significance for the health management of subway sliding plug door systems. Full article
(This article belongs to the Special Issue Feature Extraction and Condition Monitoring in Physics and Mechanics)
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