Research on Improved Fault Detection Method of Rolling Bearing Based on Signal Feature Fusion Technology

Fang, Zhenggaoyuan; Wu, Qing-E; Wang, Wenjing; Wu, Shuyan

doi:10.3390/app132412987

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…(c) Cluster #2 is labelled 'imbalanced data'. It contains important terms such as 'imbalanced data' [67], 'bearing fault detection' [68], 'dynamic modelling' [69], 'vibration monitoring' [70], and 'data augmentation' [71]. These terms are closely related to the handling of unbalanced data and vibration signal analysis.…”

Section: Analysis Of Co-cited Literaturementioning

confidence: 99%

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Chen,

Zhao,

Zhang

et al. 2024

Eng. Res. Express

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As a key component of mechanical equipment, real-time monitoring and diagnosis of rolling bearings play a critical role in ensuring the stable operation of equipment and the safety of operators. In order to present the current status and trends of fault diagnosis research on rolling bearings more intuitively, the scientific knowledge mapping was used to visualize and analyze the relevant literature in the article. The results show that the number of publications in this area of research has grown significantly in recent years, with China, India, the United States, and England having contributed significantly. The journals such as MECHANICAL SYSTEMS AND SIGNAL PROCESSING, MEASUREMENT, and JOURNAL OF SOUND AND VIBRATION have played an important role in disseminating cutting-edge technologies in this field. In addition, the exploration of modern methods based on data-driven and artificial intelligence, as well as their application to real-world problems, are gradually becoming the focus of research. Through summarising and analysing, the application of modern data processing techniques, the development of more efficient and practical intelligent fault diagnosis techniques, and the close integration of laboratory research and practical applications will become future research trends.

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Section: Analysis Of Co-cited Literaturementioning

confidence: 99%

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Chen,

Zhao,

Zhang

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…The potential fault detection techniques available in the literature are the Fourier transform (FT), fast Fourier transform (FFT), short-time Fourier transform (STFT), wavelet transform (WT), discrete wavelet transform (DWT), continuous wavelet transform (CWT), Hilbert transform (HT), Hilbert-Huang transform (HHT), etc. [5,6]. Their performance largely depends on one or other parameters mentioned in Table 1, with the nomenclature mentioned in Nomenclature part.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks

Rana,

Kishor,

Negi

et al. 2024

Applied Sciences

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High-voltage direct current (HVDC) transmission is preferred over high-voltage alternating current (HVAC) for long power lines for asynchronous power grid interconnection and high-level renewable energy integration. The control and protection functions associated with HVDC systems help with fast and secure clearance of faults. The control and protection challenges in the embedded HVDC network are of great concern for the stable and secure operation of an HVDC network. The DC fault current may reach an extremely high level in a rather short period because of the low impedance in a DC system, which is dangerous for converters, and disturbances in the AC network directly influence the performance of the HVDC system. Sometimes, faults on the AC side may lead to disconnection or failure of the DC link, causing reliability problems as well as huge economic losses. AC and DC protection solutions are being developed for HVDC systems to enhance their sustainability and reliability. As such, AC and DC faults should be detected and cleared at a faster rate. Therefore, in this article, the feasibility of the synchro-squeezed transform (SST) is analyzed for detection purposes. For more accurate and faster detection, the signal is first decomposed using the empirical mode decomposition (EMD) technique, and then the SST is applied. A discrete Teager energy (DTE) spectrum is obtained with the processed signal, which works as the detection index. The algorithm shows low sampling frequency requirements, with higher efficiency and reliability for the purpose. PSCAD/EMTDC version 4.6 software and MATLAB 2022a software is used for the modeling and simulation.

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Rolling bearing fault diagnosis based on efficient time channel attention optimized deep multi-scale convolutional neural networks

Li,

Zhu,

Chen

2024

Meas. Sci. Technol.

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In rolling bearing fault diagnosis, the collected vibration signal has nonlinear and non-Gaussian characteristics, which makes the signal feature extraction incomplete during the feature extraction process, leading to reduced fault diagnosis accuracy. This article proposes a model based on Efficient Time Channel Attention Depth Multi-Scale Convolutional Neural Network (EMCNN) to solve the above problems. This method designs a multi-scale hierarchical expansion strategy in the Multi-Scale Convolutional Neural Network (MSCNN), which can effectively extract different ranges of information from the signal. In addition, the Efficient Time Channel Attention module (E-TCAM) is designed and embedded into the MSCNN to enhance the attention to the important features in both channel and time dimensions, and also to avoid the problem of feature redundancy. Adamax optimization algorithm is used as the optimizer, which realizes the automatic adjustment and optimization of the learning rate and greatly improves the model training efficiency and performance performance. The effectiveness of the method was verified using the datasets from Case Western Reserve University and Xi'an Jiaotong University. By comparing with other diagnostic models, it was verified that the method had a high diagnostic rate and good generalization performance under nonlinear and non-Gaussian complex characteristics.

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Research on Improved Fault Detection Method of Rolling Bearing Based on Signal Feature Fusion Technology

Cited by 4 publications

References 23 publications

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks

Rolling bearing fault diagnosis based on efficient time channel attention optimized deep multi-scale convolutional neural networks

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