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Applied Sciences
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Signal Analysis and Fault Diagnosis in Mechanical Engineering
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Signal Analysis and Fault Diagnosis in Mechanical Engineering

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 6096

Special Issue Editors

Dr. Shiqian Chen

E-Mail Website
Guest Editor
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, China
Interests: machine fault diagnosis under non-stationary conditions; time-frequency analysis; adaptive mode decomposition
Special Issues, Collections and Topics in MDPI journals
Dr. Siliang Lu

E-Mail Website
Guest Editor
College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Interests: condition monitoring and fault diagnosis of mechatronic systems; robotics and industrial automation
Dr. Dong Wang

E-Mail Website
Guest Editor
The State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: intelligent operation and maintenance; mathematical basis of fault feature extraction and sparse measure; prognostic and health management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of modern industry, mechanical equipment is becoming more complex and intelligent, which often leads to higher requirements for its operational reliability. Fault diagnosis is an effective tool to ensure the operational safety of the equipment. However, complicated operation conditions lead to complex characteristics of the condition signals of the equipment (e.g., loud noise and non-stationary features), which bring challenges for fault diagnosis techniques. In response to this, various advanced signal analysis and machine learning methods have recently been developed and applied in mechanical fault diagnosis. We sincerely invite academic researchers and specialists to contribute original research papers to this Special Issue, and potential topics include, but are not limited to:

(1) Signal processing in mechanical fault diagnosis,
(2) Weak fault feature extraction method,
(3) Fault diagnosis with AI methods,
(4) Fault diagnosis under non-stationary conditions,
(5) Multivariate data analysis in fault diagnosis.

Dr. Shiqian Chen
Dr. Siliang Lu
Dr. Dong Wang
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

  • fault diagnosis
  • health monitoring
  • feature extraction
  • mode decomposition
  • time-frequency analysis
  • machine learning
  • intelligent operation and maintenance
  • multivariate data analysis

Published Papers (5 papers)

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Research

18 pages, 8983 KiB  
Article
Monitoring and Evaluation of High-Speed Railway Turnout Grinding Effect Based on Field Test and Simulation
by Qian Xiao, Yihang Yang, Chao Chang and Dongzhe Li
Appl. Sci. 2023, 13(16), 9177; https://doi.org/10.3390/app13169177 - 11 Aug 2023
Cited by 1 | Viewed by 1024
Abstract
Turnouts are the weak spot in high-speed rail systems, and it is simple for the phenomenon of the wheel–rail force and the carbody lateral acceleration over-limit to arise when the train passes through, which affects the service life of the rail and the [...] Read more.
Turnouts are the weak spot in high-speed rail systems, and it is simple for the phenomenon of the wheel–rail force and the carbody lateral acceleration over-limit to arise when the train passes through, which affects the service life of the rail and the running stability of the train. In this paper, the turnout with wheel–rail force over-limit and carbody lateral acceleration over-limit is selected for analysis, and the profiles of the wheel and rail are monitored. Then, the vehicle–turnout coupled multi-body dynamics model is simulated. Additionally, the portable vibration analyzer, the comprehensive inspection train, and the wheel–rail contact dynamic stress tester monitors the data and evaluates the impact of rail grinding on high-speed railway. The results of this study demonstrated that the turnout profiles are in good agreement with the standard wheel profiles following grinding, and the wheel–rail contact point and equivalent conicity both improved. When the train passes the ground turnout at high speed with and without the wheel polygonal wear, the wheel–rail force and the carbody acceleration were clearly improved. Using the wheel–rail contact dynamic stress tester, the comprehensive inspection train, and the portable vibration analyzer monitoring the changes in the carbody acceleration, the wheel–rail force and the carbody acceleration are definitely better after grinding. Similar to the pattern in the simulation, the train’s running steadiness increased by grinding. Full article
(This article belongs to the Special Issue Signal Analysis and Fault Diagnosis in Mechanical Engineering)
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13 pages, 2990 KiB  
Article
Modified Maximum Likelihood Estimation Metal Magnetic Memory Quantitative Identifying Model of Weld Defect Levels Based on Dempster–Shafer Theory
by Haiyan Xing, Cheng Xu, Ming Yi, Shenrou Gao and Weinan Liu
Appl. Sci. 2023, 13(13), 7959; https://doi.org/10.3390/app13137959 - 07 Jul 2023
Viewed by 624
Abstract
Metal magnetic memory (MMM) is a nondestructive testing technology based on the magnetomechanical effect, which is widely used in the qualitative detection of stress concentration zones for welded joints. However, there is inevitable residual stress after welding, which brings the bottleneck of quantitative [...] Read more.
Metal magnetic memory (MMM) is a nondestructive testing technology based on the magnetomechanical effect, which is widely used in the qualitative detection of stress concentration zones for welded joints. However, there is inevitable residual stress after welding, which brings the bottleneck of quantitative identification between the weld residual stress concentration and the early hidden damage. In order to overcome the bottleneck of quantitative identification of weld defect levels with MMM technology, a modified maximum likelihood estimation (MLE) MMM quantitative identifying model is first proposed. The experimental materials are Q235B welded plate specimens. Fatigue tension experiments were operated to find the MMM feature laws of critical hidden crack by comparing with synchronous X-ray detection results. Six MMM characteristic parameters, which are, ΔHp(x), Gxmax, Zxmax, ΔHp(y), Gymax and Zymax, are extracted corresponding to the normal state, the hidden crack state and the macroscopic crack, respectively. The MLE values of the six parameters are obtained by the kernel density functions with optimized bandwidth from the view of mathematical statistics. Furthermore, the modified MLE MMM quantitative identifying model is established based on D–S theory to overcome the partial overlap of MLE values among different defect levels, of which the uncertainty is as low as 0.3%. The verification result from scanning electron microscopy (SEM) is consistent with the prediction of the modified MLE MMM model, which provides a new method for quantitative identification of weld defect levels. Full article
(This article belongs to the Special Issue Signal Analysis and Fault Diagnosis in Mechanical Engineering)
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19 pages, 11445 KiB  
Article
Incipient Fault Feature Enhancement of Rolling Bearings Based on CEEMDAN and MCKD
by Ling Zhao, Xin Chi, Pan Li and Jiawei Ding
Appl. Sci. 2023, 13(9), 5688; https://doi.org/10.3390/app13095688 - 05 May 2023
Cited by 2 | Viewed by 929
Abstract
A rolling bearing vibration signal fault feature enhancement method based on adaptive complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and maximum correlated kurtosis deconvolution (MCKD) is proposed to address the issue that rolling bearings are prone to noise in the [...] Read more.
A rolling bearing vibration signal fault feature enhancement method based on adaptive complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and maximum correlated kurtosis deconvolution (MCKD) is proposed to address the issue that rolling bearings are prone to noise in the early stage and difficult to extract feature information accurately. The method uses the CEEMDAN algorithm to reduce the noise of the rolling bearing vibration signal in the first step; then, the MCKD algorithm is used to deconvolve the signal to enhance the weak shock components in the signal and improve the SNR. Finally, the envelope spectrum analysis is performed to extract the feature frequencies. Simulation and experimental results show that the CEEMDAN-MCKD method can highlight the fault characteristic frequency and multiplier frequency better than other methods and realize the characteristic enhancement of incipient fault vibration signals of rolling bearings under constant and variable operating conditions. Full article
(This article belongs to the Special Issue Signal Analysis and Fault Diagnosis in Mechanical Engineering)
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19 pages, 18170 KiB  
Article
Dynamic Characteristics and Fault Mechanism of the Gear Tooth Spalling in Railway Vehicles under Traction Conditions
by Yunlei Lin, Junbo Li, Peixuan Chen, Yongjie Su and Jinhai Wang
Appl. Sci. 2023, 13(8), 4656; https://doi.org/10.3390/app13084656 - 07 Apr 2023
Cited by 1 | Viewed by 919
Abstract
Gear tooth spalling is one of the inevitable fault modes in the long-term service of the traction transmission system of railway vehicles, which can worsen the dynamic load of the rotating mechanical system and reduce the operating quality. Therefore, it is necessary to [...] Read more.
Gear tooth spalling is one of the inevitable fault modes in the long-term service of the traction transmission system of railway vehicles, which can worsen the dynamic load of the rotating mechanical system and reduce the operating quality. Therefore, it is necessary to study its fault mechanism to guide fault diagnosis scientifically. This paper established a planar railway vehicle model with a traction transmission system and an analytical time-varying meshing stiffness (TVMS) model of the spalling spur gear. Then, it analyzed the dynamic characteristics under traction conditions. The research found that the spalling length and depth affect the amplitude of the TVMS at the defect, while the width affects the range of the TVMS loss. The crest factor is the best evaluation indicator in ideal low-noise environments due to its sensitivity and linearity, but it is not good in strong-noise environments. Similarly, a time–frequency analysis tool cannot significantly detect the sideband characteristics that are excited by spalling. After high-pass filtering, the root mean square and variance exhibit excellent classification and vehicle speed independence in strong-noise environments. This research achievement can provide adequate theoretical support for feature selection and making strategies for fault diagnosis of railway vehicle gear systems. Full article
(This article belongs to the Special Issue Signal Analysis and Fault Diagnosis in Mechanical Engineering)
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22 pages, 6672 KiB  
Article
Theoretical Investigation of Mesh Relationship and Mesh Stiffness of Internal Spur Gears with Tooth Wear
by Yanan Wang, Keyuan Li, Baijie Qiao, Zhixian Shen and Xuefeng Chen
Appl. Sci. 2023, 13(3), 2022; https://doi.org/10.3390/app13032022 - 03 Feb 2023
Viewed by 1891
Abstract
The internal gear is part of the planetary and epicyclic gear pairs in the transmission system of the helicopter. Gear tooth wear is one of the most usual gear failures. This paper establishes an analytical model to evaluate the influence of tooth wear [...] Read more.
The internal gear is part of the planetary and epicyclic gear pairs in the transmission system of the helicopter. Gear tooth wear is one of the most usual gear failures. This paper establishes an analytical model to evaluate the influence of tooth wear on the mesh relationship. A new mesh relationship can be derived for internal spur gears with tooth wear by the proposed analytical model. Consequently, using the new mesh relationship, the two most important meshing excitations, mesh stiffness and unloaded static transmission error (USTE), are quantitatively evaluated for the internal gear with tooth wear. The results indicate that tooth wear mainly affects the meshing ranges of single-tooth and double-teeth in mesh stiffness, rather than its amplitude. Additionally, the amplitudes of USTE increase with the increasing wear depth. Finally, this study can offer a foundation for the dynamic modeling and fault diagnosis of internal spur gears with wear faults. Full article
(This article belongs to the Special Issue Signal Analysis and Fault Diagnosis in Mechanical Engineering)
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