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Friction, Vibration and Dynamic Properties of Transmission System under Wear...
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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: closed (30 September 2022) | Viewed by 19109

Printed Edition Available!
A printed edition of this Special Issue is available here.

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
Special Issues, Collections and Topics in MDPI journals
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 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

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

Published Papers (11 papers)

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Editorial

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4 pages, 215 KiB  
Editorial
Vibration-Based System Degradation Monitoring under Gear Wear Progression
by Ke Feng, Qing Ni and Jinde Zheng
Coatings 2022, 12(7), 892; https://0-doi-org.brum.beds.ac.uk/10.3390/coatings12070892 - 23 Jun 2022
Cited by 3 | Viewed by 1298
Abstract
Surface wear is a common phenomenon in the service life of gear transmission systems [...] Full article
(This article belongs to the Special Issue Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression)

Research

Jump to: Editorial

25 pages, 9382 KiB  
Article
Dynamic Modeling and Simulation Analysis of Inter-Shaft Bearings with Local Defects Considering Elasto-Hydrodynamic Lubrication
by Jing Tian, Xinping Ai, Fengling Zhang, Zhi Wang, Cai Wang and Yingtao Chen
Coatings 2022, 12(11), 1735; https://0-doi-org.brum.beds.ac.uk/10.3390/coatings12111735 - 13 Nov 2022
Cited by 3 | Viewed by 1411
Abstract
As an important component of large engines, inter-shaft bearing is easily damaged due to its poor working conditions. By analyzing the time–frequency distribution rules of fault signals and the evolution law of micro-faults, the bearing failure mechanism can be revealed, and the bearing [...] Read more.
As an important component of large engines, inter-shaft bearing is easily damaged due to its poor working conditions. By analyzing the time–frequency distribution rules of fault signals and the evolution law of micro-faults, the bearing failure mechanism can be revealed, and the bearing failure can be monitored in real time and prevented in advance. For the purpose of studying the mechanism of inter-shaft bearing faults, a 4-DOF (degree of freedom) dynamic model of inter-shaft bearing with local defects considering elasto-hydrodynamic lubrication (EHL) is proposed. Based on the established dynamic model, the impact characteristics and distribution rules of the fault signals of the bearing are accurately simulated, and the evolution law of the micro-faults is also analyzed. The effects of different speeds, loads and defect widths on maximum value (MV), absolute mean value (AMV), effective value (EV), amplitude of square root (AST), kurtosis factor (KF), impulse factor (IF), peak factor (PF) and shape factor (SF) are obtained. The findings show that the vibration amplitude of the bearing increases with the increase in defect size, and the faults are easier to diagnose accordingly. At the same time, PF, KF and IF are very sensitive to the initial failure of bearings. With the development of faults, the overall trend of AMV, AST and EV are relatively stable. The PF is sensitive to the change of rotating speeds and defect widths. The SF is insensitive to the change of rotating speeds, loads and defect widths. This lays a foundation for the research of monitoring and diagnosis methods of aeroengine inter-shaft bearing fault. Full article
(This article belongs to the Special Issue Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression)
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24 pages, 6683 KiB  
Article
A FCEEMD Energy Kurtosis Mean Filtering-Based Fault Feature Extraction Method
by Chengjiang Zhou, Ling Xing, Yunhua Jia, Shuyi Wan and Zixuan Zhou
Coatings 2022, 12(9), 1337; https://0-doi-org.brum.beds.ac.uk/10.3390/coatings12091337 - 14 Sep 2022
Cited by 1 | Viewed by 1130
Abstract
Aiming at the problem that fault feature extraction is susceptible to background noises and burrs, we proposed a new feature extraction method based on a new decomposition method and an effective intrinsic mode function (IMF) selection method. Firstly, pairs of white noises with [...] Read more.
Aiming at the problem that fault feature extraction is susceptible to background noises and burrs, we proposed a new feature extraction method based on a new decomposition method and an effective intrinsic mode function (IMF) selection method. Firstly, pairs of white noises with opposite signs were introduced to neutralize the residual noises in ensemble empirical mode decomposition (EEMD) and suppress mode mixing. Both the reconstruction error (1.8445 × 10−17) and decomposition time (0.01 s) were greatly reduced through fast, complementary ensemble empirical mode decomposition (FCEEMD). Secondly, we integrated the energy and kurtosis of the IMF and proposed an effective IMF selection method based on energy kurtosis mean filtering, and the background noise of the signal was greatly suppressed. Finally, the periodic impacts were extracted from the IMF reconstruction signal by multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). The fault frequencies were extracted from the periodic impacts through Hilbert demodulation, and the relative errors between the measured values and the theoretical values were all less than 0.05. The experimental results show that the proposed method can extract fault features more efficiently and provide a novel method for the fault diagnosis of rotating machinery. Full article
(This article belongs to the Special Issue Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression)
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18 pages, 4042 KiB  
Article
Fault Diagnosis of Check Valve Based on KPLS Optimal Feature Selection and Kernel Extreme Learning Machine
by Xuyi Yuan, Yugang Fan, Chengjiang Zhou, Xiaodong Wang and Guanghui Zhang
Coatings 2022, 12(9), 1320; https://0-doi-org.brum.beds.ac.uk/10.3390/coatings12091320 - 10 Sep 2022
Cited by 3 | Viewed by 1189
Abstract
The check valve is the core part of high-pressure diaphragm pumps. It has complex operation conditions and has difficulty characterizing fault states completely with its single feature. Therefore, a fault signal diagnosis model based on the kernel extreme learning machine (KELM) was constructed [...] Read more.
The check valve is the core part of high-pressure diaphragm pumps. It has complex operation conditions and has difficulty characterizing fault states completely with its single feature. Therefore, a fault signal diagnosis model based on the kernel extreme learning machine (KELM) was constructed to diagnose the check valve. The model adopts a multi-feature extraction method and reduces dimensionality through kernel partial least squares (KPLS). Firstly, we divided the check valve vibration signal into several non-overlapping samples. Then, we extracted 16 time-domain features, 13 frequency-domain features, 16 wavelet packet energy features, and energy entropy features from each sample to construct a multi-feature set characterizing the operation state of the check valve. Next, we used the KPLS method to optimize the 45 dimension multi-feature data and employed the processed feature set to establish a KELM fault diagnosis model. Experiments showed that the method based on KPLS optimal feature selection could fully characterize the operating state of the equipment with an accuracy rate of 96.88%. This result indicates the high accuracy and effectiveness of the multi-feature set constructed with the KELM fault diagnosis model. Full article
(This article belongs to the Special Issue Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression)
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14 pages, 6461 KiB  
Article
A Dynamic Wear Prediction Model for Studying the Interactions between Surface Wear and Dynamic Response of Spur Gears
by Jinzhao Ren and Huiqun Yuan
Coatings 2022, 12(9), 1250; https://0-doi-org.brum.beds.ac.uk/10.3390/coatings12091250 - 26 Aug 2022
Cited by 5 | Viewed by 1555
Abstract
Surface wear, as a major failure mode of gear systems, is an unavoidable phenomenon during the whole life of gears. It also induces other gear damages, such as fatigue cracks, surface pitting and spalling. Ultimately, those defects may result in the sudden failure [...] Read more.
Surface wear, as a major failure mode of gear systems, is an unavoidable phenomenon during the whole life of gears. It also induces other gear damages, such as fatigue cracks, surface pitting and spalling. Ultimately, those defects may result in the sudden failure of a gearbox transmission system, which can lead to a serious accident and unexpected economic loss. Therefore, it can provide huge cost and safety benefits to industries to monitor gear wear and predict its propagation. Gear wear raises the error rate of gear transmission systems, typically leading to improvements in dynamic loads, vibration, and noise. In return, the increased load conversely aggravates wear, creating a feedback cycle between dynamic responses and surface wear. For this purpose, a wear prediction model was incorporated into a tribo-dynamic model for quantitatively investigating how surface wear and gear vibration are mutually affected by each other. To obtain more precise dynamic responses, the tribo-dynamic model integrates the time-varying mesh stiffness, load-sharing ratio and friction parameters. To improve the computational efficiency and guarantee the calculation precision, an improved and updated wear depth methodology is constructed in the wear prediction model. This paper demonstrates the capability of the proposed dynamic wear prediction model in the investigation of the interaction effects between gear dynamics and surface wear, allowing for the development of improved gear wear prediction tools. The obtained results indicate that the surface wear impacts the dynamic characteristics, even with slight wear. In the initial stage of wear, the friction coefficient decreases slightly, largely due to the reduction in surface roughness; but the friction force increases because of the improved dynamic meshing force. Although the initial wear depth distributions of a pinion under dynamic and static conditions are similar, the wear depth distributions under dynamic conditions becomes significantly different compared to the those under static conditions with the wear process. The maximum wear depth of a pinion under dynamic conditions is about 1.6 times as the corresponding static conditions, when the wear cycle comes to 4 × 104. Similarly, the maximum accumulative wear depth of a pinion under dynamic conditions reaches 1.2 times of that under static conditions. Therefore, the proposed dynamic wear prediction model is more appropriate to be applied to the surface wear of gears. Full article
(This article belongs to the Special Issue Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression)
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14 pages, 3512 KiB  
Article
Design, Optimization and Cutting Performance Evaluation of an Internal Spray Cooling Turning Tool
by Leping Liu, Shengrong Shu, Huimin Li and Xuan Chen
Coatings 2022, 12(8), 1141; https://0-doi-org.brum.beds.ac.uk/10.3390/coatings12081141 - 08 Aug 2022
Cited by 2 | Viewed by 1471
Abstract
The traditional flood cooling method applied in the internal turning process has disadvantages, such as having a low cooling efficiency and being environmentally unfriendly. In the present work, an internal spray cooling turning tool was designed, and the performance was numerically and experimentally [...] Read more.
The traditional flood cooling method applied in the internal turning process has disadvantages, such as having a low cooling efficiency and being environmentally unfriendly. In the present work, an internal spray cooling turning tool was designed, and the performance was numerically and experimentally accessed. The heat transfer simulation model of the internal spray cooling turning tool was established by ANSYS Fluent, and the tool cooling structure parameters were optimized by the Taguchi method based on the CFD simulations, and obtains the diameters of the upper and lower nozzles of 3 mm and 1.5 mm, respectively; the distance between the upper nozzle and the tool tip of 18.5 mm. To evaluate the cutting and cooling performance of the optimized tool, internal turning experiments were conducted on QT500-7 workpieces. Results show that the optimized tool with internal spray cooling led to lower workpiece surface roughness and chip curling, compared to the conventional tools. Full article
(This article belongs to the Special Issue Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression)
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12 pages, 2129 KiB  
Article
Multisensor Feature Fusion Based Rolling Bearing Fault Diagnosis Method
by Jinyu Tong, Cang Liu, Haiyang Pan and Jinde Zheng
Coatings 2022, 12(6), 866; https://0-doi-org.brum.beds.ac.uk/10.3390/coatings12060866 - 19 Jun 2022
Cited by 2 | Viewed by 1841
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
(This article belongs to the Special Issue Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression)
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30 pages, 14732 KiB  
Article
Research on Fault Feature Extraction Method Based on Parameter Optimized Variational Mode Decomposition and Robust Independent Component Analysis
by Jingzong Yang, Chengjiang Zhou and Xuefeng Li
Coatings 2022, 12(3), 419; https://0-doi-org.brum.beds.ac.uk/10.3390/coatings12030419 - 21 Mar 2022
Cited by 13 | Viewed by 2406
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
(This article belongs to the Special Issue Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression)
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16 pages, 3293 KiB  
Article
A Fault Feature Extraction Method Based on LMD and Wavelet Packet Denoising
by Jingzong Yang and Chengjiang Zhou
Coatings 2022, 12(2), 156; https://0-doi-org.brum.beds.ac.uk/10.3390/coatings12020156 - 27 Jan 2022
Cited by 13 | Viewed by 1939
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
(This article belongs to the Special Issue Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression)
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17 pages, 5019 KiB  
Article
Sliding Dispersion Entropy-Based Fault State Detection for Diaphragm Pump Parts
by Chengjiang Zhou, Yunhua Jia, Haicheng Bai, Ling Xing and Yang Yang
Coatings 2021, 11(12), 1536; https://0-doi-org.brum.beds.ac.uk/10.3390/coatings11121536 - 14 Dec 2021
Cited by 3 | Viewed by 1958
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
(This article belongs to the Special Issue Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression)
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10 pages, 4288 KiB  
Article
A Fault Diagnosis Method Based on EEMD and Statistical Distance Analysis
by Tingzhong Wang, Tingting Zhu, Lingli Zhu and Ping He
Coatings 2021, 11(12), 1459; https://0-doi-org.brum.beds.ac.uk/10.3390/coatings11121459 - 28 Nov 2021
Cited by 3 | Viewed by 1544
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
(This article belongs to the Special Issue Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression)
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