Asymmetric Studies with Complex Mechanical Systems

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1095

Special Issue Editors


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Guest Editor
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
Interests: deep transfer learning; federated learning; signal processing; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Engineering, University of Cambridge, Cambridge, UK
Interests: condition monitoring; reliability; machine learning
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: mahcine diagnostics and prognostics; signal processing; sparse representation; machine learning

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Guest Editor
School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Interests: signal processing; fault diagnosis and prognosis; vibration analysis and suppression
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Asymmetry analysis is a crucial aspect of mechanical engineering that helps in identifying and diagnosing faults in machines. By studying the asymmetrical characteristics of a mechanical system, engineers can pinpoint the root cause of issues such as unbalance, misalignment, and faults of motors, bearings, etc. With the advent of modern technology, the complexity of mechanical systems has increased, making it more challenging to detect and predict faults accurately.

Machine learning has emerged as a powerful tool for asymmetric analysis in mechanical fault detection and diagnosis. By leveraging the power of artificial intelligence, engineers can train machines to identify patterns and anomalies in data, making it easier to detect faults in real time. The integration of machine learning with cloud computing and Industry 4.0 technologies has opened up new possibilities for improving the accuracy and efficiency of fault diagnosis in complex systems.

Topics may include, but are not limited to:

  • Asymmetry analysis of mechanical systems;
  • Abnormal detection;
  • Intelligent diagnosis;
  • Fault prognosis;
  • Real-time monitoring;
  • Predictive maintenance.

Dr. Zhuyun Chen
Dr. Borong Hu
Dr. Yun Kong
Dr. Minghui Hu
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. Symmetry 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

  • asymmetry analysis
  • abnormal detection
  • condition monitoring
  • artificial intelligence

Published Papers (1 paper)

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Research

17 pages, 4609 KiB  
Article
Improved Vibration Signal Models of Localized Faults of Sun Gears to Predict Modulation
by Xiaoqing Yang, Gang Yang, Qiang Zeng, Canyi Du, Xiangkun Zeng, Feifei Yu and Zhuyun Chen
Symmetry 2023, 15(9), 1621; https://doi.org/10.3390/sym15091621 - 22 Aug 2023
Viewed by 749
Abstract
The vibration modulation of the localized faults of sun gears is complicated because of the structure and motion features of planetary gearboxes. It is challenging to completely determine the vibration modulation mechanism. To address this issue, the influences of fluctuations in speed on [...] Read more.
The vibration modulation of the localized faults of sun gears is complicated because of the structure and motion features of planetary gearboxes. It is challenging to completely determine the vibration modulation mechanism. To address this issue, the influences of fluctuations in speed on the factors that affect vibration like the transfer path function, time-varying projection function, and meshing force were studied. Improved vibration amplitude and frequency modulation models were then established by combining the factors affecting vibration considering the speed fluctuations caused by the localized faults of sun gears. Regarding the features of vibration modulation of localized faults of sun gears, we concluded that both fault feature frequency and carrier rotational frequency modulate the harmonics of the meshing frequency, which form two groups of modulation sidebands. In addition, the harmonics of the fault feature frequency over the whole frequency range are modulated by the rotational frequency of the carrier, which is more dominant in the resonance domain. The experimental results of localized faults of sun gears strongly agree with those deduced using the proposed models. This research contributes an effective frequency indicator that can be used to confirm the condition of planetary gearboxes. Full article
(This article belongs to the Special Issue Asymmetric Studies with Complex Mechanical Systems)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: An Explainable Deep Wavelet Network for Abnormal Detection of Machinery
Authors: Yi He, Weidong Xu, Haiyang Wan, Zhuyun Chen*
Affiliation: School of Mechanical and Automotive Engineering, South China University of Technology
Abstract: The detection of abnormalities in machinery is crucial for ensuring the safety and efficiency of industrial operations. In recent years, deep learning techniques have shown great potential in abnormal detection tasks. However, the lack of interpretability in deep learning models hinders their practical application in industrial settings. In this study, we propose an Explainable Deep Wavelet Network (EDWN) for abnormal detection in machinery. The EDWN combines the strengths of deep learning and wavelet transform to enhance the interpretability of the model. The wavelet transform allows for the extraction of both time and frequency domain features, which are then fed into a deep neural network for abnormal detection. Experimental results on a real-world machinery dataset demonstrate the effectiveness and interpretability of the EDWN. The proposed model not only achieves high accuracy in abnormal detection but also provides insights into the underlying patterns contributing to abnormalities.

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