The Symmetry/Asymmetry Phenomenon in the Fault Diagnosis Process of Industrial Machinery

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2203

Special Issue Editors


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Guest Editor
Associate Professor, School of Electrical Engineering, University of Jinan, Jinan 250022, China
Interests: non-stationary signal processing; time-frequency analysis; mechanical dynamics analysis; mechanical fault diagnosis

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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
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Special Issue Information

Dear Colleagues,

Industrial machinery often undergoes inevitable health degradation. Vibration-signal-based monitoring for machinery has always played an important role in the diagnosis and prognosis of industrial machinery. Vibration signals collected in healthy machinery usually appear to have good symmetry, even in the early fault stage. With the development of the fault, the symmetry of vibration signals gradually degenerates until it breaks. Therefore, it is important to detect the asymmetry/asymmetry of the signal for the timely diagnosis and prognosis of the machinery. The intention of this Special Issue is to present methods dealing mainly (but not exclusively) with state-of-the-art solutions for signal processing and dynamics modeling to deeply explore the symmetry/asymmetry phenomenon in the process of machinery diagnostics and prognostics.

Dr. Gang Yu
Dr. Shiqian Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • symmetry/asymmetry
  • vibration signal processing
  • machinery diagnostics and prognostics
  • machinery monitoring
  • early fault diagnosis

Published Papers (3 papers)

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Research

21 pages, 5505 KiB  
Article
A Novel Method for Bearing Fault Diagnosis Based on a Parallel Deep Convolutional Neural Network
by Zhuonan Lin, Yongxing Wang, Yining Guo, Xiangrui Tong, Fanrong Wei and Ning Tong
Symmetry 2024, 16(4), 432; https://doi.org/10.3390/sym16040432 - 4 Apr 2024
Viewed by 508
Abstract
The symmetry of vibration signals collected from healthy machinery, which gradually degenerates with the development of faults, must be detected for timely diagnosis and prognosis. However, conventional methods may miss spatiotemporal relationships, struggle with varying sampling rates, and lack adaptability to changing loads [...] Read more.
The symmetry of vibration signals collected from healthy machinery, which gradually degenerates with the development of faults, must be detected for timely diagnosis and prognosis. However, conventional methods may miss spatiotemporal relationships, struggle with varying sampling rates, and lack adaptability to changing loads and conditions, affecting diagnostic accuracy. A novel bearing fault diagnosis approach is proposed to address these issues, which integrates the Gramian angular field (GAF) transformation with a parallel deep convolutional neural network (DCNN). The crux of this method lies in the preprocessing of input signals, where sampling rate normalization is employed to minimize the effects of varying sampling rates on diagnostic outcomes. Subsequently, the processed signals undergo GAF transformation, converting them into an image format that effectively represents their spatiotemporal relationships in a two-dimensional space. These images serve as inputs to the parallel DCNN, facilitating feature extraction and fault classification through deep learning techniques and leading to improved generalization capabilities on test data. The proposed method achieves an overall accuracy of 96.96%, even in the absence of training data within the test set. Discussions are also conducted to quantify the effects of sampling rate normalization and model structures on diagnostic accuracy. Full article
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19 pages, 7982 KiB  
Article
Early Fault Diagnosis of Bearings Based on Symplectic Geometry Mode Decomposition Guided by Optimal Weight Spectrum Index
by Chenglong Wei, Yiqi Zhou, Bo Han and Pengchuan Liu
Symmetry 2024, 16(4), 408; https://doi.org/10.3390/sym16040408 - 1 Apr 2024
Viewed by 564
Abstract
When the rotating machinery fails, the signal generated by the faulty component often no longer maintains the original symmetry, which makes the vibration signal with nonlinear and non-stationary characteristics, and is easily affected by background noise and other equipment excitation sources. In the [...] Read more.
When the rotating machinery fails, the signal generated by the faulty component often no longer maintains the original symmetry, which makes the vibration signal with nonlinear and non-stationary characteristics, and is easily affected by background noise and other equipment excitation sources. In the early stage of fault occurrence, the fault signal is weak and difficult to extract. Traditional fault diagnosis methods are not able to easily diagnose fault information. To address this issue, this paper proposes an early fault diagnosis method for symplectic geometry mode decomposition (SGMD) based on the optimal weight spectrum index (OWSI). Firstly, using normal and fault signals, the optimal weight spectrum is derived through convex optimization. Secondly, SGMD is used to decompose the fault signal, obtaining a series of symplectic geometric modal components (SGCs) and calculating the optimal weight index of each component signal. Finally, using the principle of maximizing the OWSI, sensitive components reflecting fault characteristics are selected, and the signal is reconstructed based on this index. Then, envelope analysis is performed on the sensitive components to extract early fault characteristics of rolling bearings. OWSI can effectively distinguish the interference components in fault signals using normal signals, while SGMD has the characteristic of unchanged phase space structure, which can effectively ensure the integrity of internal features in data. Using actual fault data of rolling bearings for verification, the results show that the proposed method can effectively extract sensitive components that reflect fault characteristics. Compared with existing methods such as Variational Mode Decomposition (VMD), Feature Mode Decomposition (FMD), and Spectral Kurtosis (SK), this method has better performance. Full article
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16 pages, 12116 KiB  
Article
Monitoring of Thermoacoustic Combustion Instability via Recurrence Quantification Analysis and Optimized Deep Belief Network
by Qingwen Zeng, Chunyan Hu, Jiaxian Sun, Yafeng Shen and Keqiang Miao
Symmetry 2024, 16(3), 266; https://doi.org/10.3390/sym16030266 - 22 Feb 2024
Viewed by 699
Abstract
Thermoacoustic oscillation is indeed a phenomenon characterized by the symmetric coupling of thermal and acoustic waves. This paper introduces a novel approach for monitoring and predicting thermoacoustic combustion instability using a combination of recurrence quantification analysis (RQA) and an optimized deep belief network [...] Read more.
Thermoacoustic oscillation is indeed a phenomenon characterized by the symmetric coupling of thermal and acoustic waves. This paper introduces a novel approach for monitoring and predicting thermoacoustic combustion instability using a combination of recurrence quantification analysis (RQA) and an optimized deep belief network (DBN). Six samples of combustion state data were collected using two distinct types of burners to facilitate the training and validation of GA-DBN. The proposed methodology leverages RQA to extract intricate patterns and dynamic features from time series data representing combustion behavior. By quantifying the recurrence plot of specific patterns, the analysis provides valuable insights into the underlying thermoacoustic dynamics. Among three different feature extraction methods, RQA stands out remarkably in performance. These RQA-derived features serve as input to a carefully tuned DBN, which is trained to learn the complex relationships within the combustion process. The classification accuracy of deep belief network optimized by genetic algorithm (GA-DBN) reached an impressive 99.8%. Subsequent multiple comparisons were conducted between GA-DBN, DBN, and support vector machine (SVM), revealing that GA-DBN consistently demonstrated satisfactory classification results. This method holds significant importance in monitoring intricate combustion states. Full article
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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: Identification of modal parameters using an improved sparse blind source separation
Authors: Gang Yu
Affiliation: School of Electrical Engineering, University of Jinan, Jinan 250022, China
Abstract: During the last decade, blind source separation (BSS) method has become an effective tool to characterize and identify modal parameters of linear systems. However, in practical engineering, the assumptions of guaranteeing conventional BSS method successful application cannot be satisfied frequently, which lead to some challenging issues. One of these challenges is how to deal with the modal identification issue in the under-determined case, which means the number of sensors being less than that of the active modals. In this paper, we explore an efficient under-determined BSS method called sparse BSS (SBSS). The drawbacks of conventional SBSS are first listed and an improved SBSS method is proposed to deal with the mentioned problems, which is shown to be more suitable for engineering applications. A 5-degrees-of-freedom numerical system and two experiments are employed to validate the effectiveness of the proposed method. The identified results of modal parameters show highly satisfied accuracy via comparative analysis, which illustrates the proposed SBSS having a potential application in structural engineering. Key words: Modal identification; Sparse blind source separation; Under-determined

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