Special Issue "Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression"

A special issue of Coatings (ISSN 2079-6412). This special issue belongs to the section "Tribology".

Deadline for manuscript submissions: 30 September 2022 | Viewed by 3974

Special Issue Editors

Dr. Ke Feng
E-Mail Website
Guest Editor
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia
Interests: wear analysis; signal processing; vibration analysis; dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Jinde Zheng
E-Mail Website
Guest Editor
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
Interests: condition monitoring; wear analysis; nonlinear dynamics
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

Special Issue Information

Dear Colleagues,

We would like to invite you to submit your work to this Special Issue on “Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression”. The transmission system plays a vital role in a range of industry transmission systems, including wind turbines, vehicles, mining and material handling equipment, offshore vessels, and aircraft. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as gearbox, bearing and shafts), and wear propagation can reduce the durability of the contacting surface with coatings. As a result, the performance of the transmission system will degrade significantly, which can cause a sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, it is necessary to investigate the friction, vibration, and dynamic property of contact coating surfaces and monitor the operating conditions of the transmission system for health management. This Special Issue aims to collect the latest developments in this area, with special emphasis on wear and fatigue analysis, dynamic property of coating surfaces in transmission systems, as well as non-destructive condition monitoring for the health management of transmission systems. Contributions from academic research, application-oriented research, and industrial field studies are welcome.

Potential topics include but are not limited to the following:

  • Wear and fatigue analysis of transmission systems;
  • Dynamic property of transmission systems;
  • Surface degradation monitoring;
  • Vibration analysis of transmission systems;
  • Friction, corrosion properties of metal coating in transmission systems;
  • Modeling analysis of transmission systems under wear progression;
  • Intelligent techniques for monitoring system degradation behaviors.

Dr. Ke Feng
Dr. Jinde Zheng
Dr. Qing Ni
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. Coatings 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 2000 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

  • surface wear
  • wear and fatigue analysis
  • transmission system
  • condition monitoring
  • non-destructive condition monitoring
  • friction

Published Papers (6 papers)

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Editorial

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Editorial
Vibration-Based System Degradation Monitoring under Gear Wear Progression
Coatings 2022, 12(7), 892; https://doi.org/10.3390/coatings12070892 - 23 Jun 2022
Viewed by 291
Abstract
Surface wear is a common phenomenon in the service life of gear transmission systems [...] Full article

Research

Jump to: Editorial

Article
Multisensor Feature Fusion Based Rolling Bearing Fault Diagnosis Method
Coatings 2022, 12(6), 866; https://doi.org/10.3390/coatings12060866 - 19 Jun 2022
Viewed by 378
Abstract
To fully utilize the fault information and improve the diagnosis accuracy of rolling bearings, a multisensor feature fusion method is proposed. The method contains two steps. First, the intrinsic mode function (IMF) of each sensor vibration signal is calculated by variational mode decomposition [...] Read more.
To fully utilize the fault information and improve the diagnosis accuracy of rolling bearings, a multisensor feature fusion method is proposed. The method contains two steps. First, the intrinsic mode function (IMF) of each sensor vibration signal is calculated by variational mode decomposition (VMD), and the redundant information such as noise is eliminated. Then, the time-domain, frequency-domain and multiscale entropy features are extracted based on the preferred IMF and fused into one multidomain feature dataset. In the second step, the deep autoencoder network (DAEN) is constructed and the multisensor fusion features of the first step are used as input of the DAEN, and the multisensor fusion features are further extracted and classified. The experimental results show that the proposed model has a higher classification accuracy compared with the existing methods. Full article
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Article
Research on Fault Feature Extraction Method Based on Parameter Optimized Variational Mode Decomposition and Robust Independent Component Analysis
Coatings 2022, 12(3), 419; https://doi.org/10.3390/coatings12030419 - 21 Mar 2022
Cited by 1 | Viewed by 642
Abstract
The variational mode decomposition mode (VMD) has a reliable mathematical derivation and can decompose signals adaptively. At present, it has been widely used in mechanical fault diagnosis, financial analysis and prediction, geological signal analysis, and other fields. However, VMD has the problems of [...] Read more.
The variational mode decomposition mode (VMD) has a reliable mathematical derivation and can decompose signals adaptively. At present, it has been widely used in mechanical fault diagnosis, financial analysis and prediction, geological signal analysis, and other fields. However, VMD has the problems of insufficient decomposition and modal aliasing due to the unclear selection method of modal component k and penalty factor α. Therefore, it is difficult to ensure the accuracy of fault feature extraction and fault diagnosis. To effectively extract fault feature information from bearing vibration signals, a fault feature extraction method based on VMD optimized with information entropy, and robust independent component analysis (RobustICA) was proposed. Firstly, the modal component k and penalty factor α in VMD were optimized by the principle of minimum information entropy to improve the effect of signal decomposition. Secondly, the optimal parameters weresubstituted into VMD, and several intrinsic mode functions (IMFs) wereobtained by signal decomposition. Secondly, the kurtosis and cross-correlation coefficient criteria were comprehensively used to evaluate the advantages and disadvantages of each IMF.And then, the optimal IMFs were selected to construct the observation signal channel to realize the signal-to-noise separation based on RobustICA. Finally, the envelope demodulation analysis of the denoised signal was carried out to extract the fault characteristic frequency. Through the analysis of bearing simulation signal and actual data, it shows that this method can extract the weak characteristics of rolling bearing fault signal and realize the accurate identification of fault. Meanwhile, in the bearing simulation signal experiment, the results of kurtosis value, cross-correlation coefficient, root mean square error, and mean absolute error are 6.162, 0.681, 0.740, and 0.583, respectively. Compared with other traditional methods, better index evaluation value is obtained. Full article
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Article
A Fault Feature Extraction Method Based on LMD and Wavelet Packet Denoising
Coatings 2022, 12(2), 156; https://doi.org/10.3390/coatings12020156 - 27 Jan 2022
Cited by 1 | Viewed by 668
Abstract
Aiming at the problem of fault feature extraction of a diaphragm pump check valve, a fault feature extraction method based on local mean decomposition (LMD) and wavelet packet transform is proposed. Firstly, the collected vibration signal was decomposed by LMD. After several amplitude [...] Read more.
Aiming at the problem of fault feature extraction of a diaphragm pump check valve, a fault feature extraction method based on local mean decomposition (LMD) and wavelet packet transform is proposed. Firstly, the collected vibration signal was decomposed by LMD. After several amplitude modulation (AM) and frequency modulation (FM) components were obtained, the effective components were selected according to the Kullback-Leible (K-L) divergence of all component signals for reconstruction. Then, wavelet packet transform was used to denoise the reconstructed signal. Finally, the characteristics of the fault signal were extracted by Hilbert envelope spectrum analysis. Through experimental analysis, the results show that compared with other traditional methods, the proposed method can effectively overcome the phenomenon of mode aliasing and extract the fault characteristics of a check valve more effectively. Experiments show that this method is feasible in the fault diagnosis of check valve. Full article
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Article
Sliding Dispersion Entropy-Based Fault State Detection for Diaphragm Pump Parts
Coatings 2021, 11(12), 1536; https://doi.org/10.3390/coatings11121536 - 14 Dec 2021
Cited by 1 | Viewed by 711
Abstract
Aiming at the disadvantages of low trend, poor characterization performance, and poor anti-noise performance of traditional degradation features such as dispersion entropy (DE), a fault detection method based on sliding dispersion entropy (SDE) is proposed. Firstly, a sliding window is added to the [...] Read more.
Aiming at the disadvantages of low trend, poor characterization performance, and poor anti-noise performance of traditional degradation features such as dispersion entropy (DE), a fault detection method based on sliding dispersion entropy (SDE) is proposed. Firstly, a sliding window is added to the signal before extracting the DE feature, and the root mean square of the signal inside the sliding window is used to replace the signal in the window to realize down sampling, which enhances the trend of DE. Secondly, the hyperbolic tangent sigmoid function (TANSIG) is introduced to map the signals to different categories when extracting the DE feature, which is more in line with the signal distribution of mechanical parts and the monotonicity of the degradation feature is improved. For noisy signal, the introduction of locally weighted scatterplot smoothing (LOWESS) can remove the burrs and fluctuations of the SDE curve, and the anti-noise performance of SDE is improved. Finally, the SDE state warning line is constructed based on the 2σ criterion, which can determine the fault warning point in time and effectively. The state detection results of bearing and check valve show that the proposed SDE improves the trend, monotonicity, and robustness of the state tracking curve, and provides a new method for fault state detection of mechanical parts. Full article
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Article
A Fault Diagnosis Method Based on EEMD and Statistical Distance Analysis
Coatings 2021, 11(12), 1459; https://doi.org/10.3390/coatings11121459 - 28 Nov 2021
Viewed by 620
Abstract
Serious vibration or wear with large friction usually appear when faults occur, which leads to more serious faults such as the destruction of the oil film, bringing great damages to both the society and economic sector. Therefore, the accurate diagnosis of a fault [...] Read more.
Serious vibration or wear with large friction usually appear when faults occur, which leads to more serious faults such as the destruction of the oil film, bringing great damages to both the society and economic sector. Therefore, the accurate diagnosis of a fault in the early stage is important for the safety operation of machinery. To effectively extract the fault features for diagnosis, EMD-based methods are widely used. However, these methods spend lots of efforts diagnosing faults and require plenty of professional knowledge of diagnosis. Although many intelligent classifiers can be used to automatically diagnose faults such as wear, a broken tooth and imbalance, the combing EMD-based method, the scarcity of samplings with labels hinder the application of these methods to engineering. It is because the model of the intelligent classifier must be constructed based on sufficient samplings with a label. To solve this problem, we propose a novel fault diagnosis method, which is performed based on the EEMD and statistical distance analysis. In this method, the EEMD is used to decompose one original signal into several IMFs and then the probability density distribution of each IMF is calculated. To diagnose the fault of the machinery, the Euclidean distance between the signal acquired under an unknown fault with the other referenced signals acquired previously under various fault types is calculated. At last, the fault of the signal is the same with the referenced signal when the distance is the smallest. To verify the effectiveness of our proposed method, a dataset of bearings with different faults, and a dataset of 2009 Prognostics and Health Management (PHM) data challenge, including gear, bearing and shaft faults are used. The result shows that the proposed method can not only automatically diagnose faults effectively, but also fewer samplings with a label are used compared with the intelligent methods. Full article
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