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Dan Wang

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Proceedings Article | 22 May 2024 Paper

Fault diagnosis of rolling bearing based on deep linear discriminant analysis

Xiongbin Liu, Yizhen Chen, Tianyuan Chen, Peng Han, Dan Wang

Proceedings Volume 13176, 131763A (2024) https://doi.org/10.1117/12.3028973

KEYWORDS: Data modeling, Feature extraction, Vibration, Education and training, Convolutional neural networks, Convolution, Signal processing, Statistical modeling, Windows, Nonlinear optics

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Rolling bearing is one of the important parts of rotating machinery, the monitoring and timely diagnosis of the running state of bearing is of great significance to ensure the safe operation of equipment. Aiming at the problems of multiple parameters and low recognition efficiency of existing intelligent diagnosis models, a rolling bearing fault diagnosis method based on Deep linear discriminant analysis (Deep LDA) was proposed combining the advantages of linear discriminant analysis and one-dimensional convolutional neural network that can effectively extract discriminant features and Deep feature information. After the one-dimensional original vibration signal is preprocessed, one-dimensional convolutional neural network is directly used to obtain the depth nonlinear characteristics of rolling bearings to avoid information loss when the original signal is converted into frequency domain signals. Using LDA, the model can acquire the ability to learn the potential representation of linear separable, and then extract the discriminant fault feature information from nonlinear fault information, and make the extracted sample feature obtain the best separation effect in the sample space, to achieve accurate classification of rolling bearing fault types. In order to verify the classification effect of Deep LDA, two sets of rolling bearing fault data sets were used to verify the proposed model. Experimental results and comparative analysis show that the proposed model can accurately identify different running states of rolling bearings, and has higher recognition accuracy and diagnosis efficiency.

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