Enhanced symplectic characteristics mode decomposition method and its application in fault diagnosis of rolling bearing

Cheng, Zhengyang; Wang, Rongji

doi:10.1016/j.measurement.2020.108108

Cited by 20 publications

(7 citation statements)

References 28 publications

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“…Both rolling bearing and diesel engine vibration signals are characterized by time-varying, nonlinear non-smoothness [ 28 ]. Therefore, the feasibility of SSST with the ViT method was verified using the publicly available CWRU bearing vibration signal dataset.…”

Section: Experimental Results and Comparative Analysismentioning

confidence: 99%

Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer

Li¹,

Liu²,

Yan³

et al. 2023

Sensors

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The reliability and safety of diesel engines gradually decrease with the increase in running time, leading to frequent failures. To address the problem that it is difficult for the traditional fault status identification methods to identify diesel engine faults accurately, a diesel engine fault status identification method based on synchro squeezing S-transform (SSST) and vision transformer (ViT) is proposed. This method can effectively combine the advantages of the SSST method in processing non-linear and non-smooth signals with the powerful image classification capability of ViT. The vibration signals reflecting the diesel engine status are collected by sensors. To solve the problems of low time-frequency resolution and weak energy aggregation in traditional signal time-frequency analysis methods, the SSST method is used to convert the vibration signals into two-dimensional time-frequency maps; the ViT model is used to extract time-frequency image features for training to achieve diesel engine status assessment. Pre-set fault experiments are carried out using the diesel engine condition monitoring experimental bench, and the proposed method is compared with three traditional methods, namely, ST-ViT, SSST-2DCNN and FFT spectrum-1DCNN. The experimental results show that the overall fault status identification accuracy in the public dataset and the actual laboratory data reaches 98.31% and 95.67%, respectively, providing a new idea for diesel engine fault status identification.

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Section: Experimental Results and Comparative Analysismentioning

confidence: 99%

Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer

Li¹,

Liu²,

Yan³

et al. 2023

Sensors

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show abstract

“…Research [25] has shown that differential and integral signals can improve fault characteristics. To fully profit from these, calculus operator is used to the processing of signal components.…”

Section: Esgmd-cc Theorymentioning

confidence: 99%

Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM

He,

Liu,

Geng

et al. 2024

SDHM

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Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods, making it challenging to ensure the fault diagnosis accuracy and reliability. A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator (ESGMD-CC) and artificial fish swarm algorithm (AFSA) optimized extreme learning machine (ELM) is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis. Firstly, SGMD decomposes the raw vibration signal into multiple Symplectic geometry components (SGCs). Secondly, the iterations are reset by the cosine difference limitation to effectively separate the redundant components from the representative components. Additionally, the calculus operator is performed to strengthen weak fault features and make them easier to extract, and the singular value decomposition (SVD) weighted by power spectrum entropy (PSE) can be utilized as the sample feature representation. Finally, AFSA iteratively optimized ELM is adopted as the optimized classifier for fault identification. The superior performance of the proposed method has been validated by various experiments.

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“…Yu et al [54] proposed a fault feature extraction method based on ITD with sparse coding shrinkage. Cheng et al [55] improved the symplectic geometry mode decomposition (SGMD) by calculus operators and characteristic value decomposition to improve the characteristic enhancement capability and noise robustness.…”

Section: Fault Feature Extractionmentioning

confidence: 99%

Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

Liu

Zhang²,

Dong

et al. 2023

Processes

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The diesel engine, as the main power source of equipment, faces practical problems in the maintenance process, such as difficulty in fault location and a lack of preventive maintenance techniques. Currently, breakdown maintenance and cyclical preventive maintenance are the main means of maintenance support after a diesel engine failure, but these methods require professional maintenance personnel to carry out manual fault diagnosis, which is time-consuming. Prognostics and health management (PHM), as a new technology in the field of equipment maintenance support, has significant advantages in improving equipment reliability and safety, enhancing equipment maintenance support capability, and reducing maintenance support costs. In view of this, when introducing PHM into diesel engine maintenance support, the research progress and development trend of the key technologies of PHM for diesel engines are carried out with the objective of achieving precise maintenance and scientific management of diesel engines, and the key technologies demand traction. Firstly, the development history of PHM technology is reviewed, and its basic concept and main functions are introduced. Secondly, the system architecture of PHM for diesel engines is constructed, and its key technologies are summarized. Then, the research progress in the field of PHM for diesel engines is reviewed from four aspects: data acquisition, data processing, fault diagnosis, and health status assessment. Finally, the challenges faced by diesel engine PHM in engineering applications are analyzed, effective solutions to address these challenges are explored, and the future development trend is foreseen.

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Enhanced symplectic characteristics mode decomposition method and its application in fault diagnosis of rolling bearing

Cited by 20 publications

References 28 publications

Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer

Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer

Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM

Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

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