A method of bearing fault classification and related apparatus

By constructing a sparse dictionary and calculating sparse coefficients through a dictionary learning algorithm, the nonlinearity and information redundancy problems of bearing fault signals are solved, and fault feature extraction and accurate classification are achieved under low signal-to-noise ratio conditions.

CN117648598BActive Publication Date: 2026-06-23PETROCHINA CO LTD +1
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Patent Information

Application Number
CN202210950785.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-08-09
Publication Date
2026-06-23
Estimated Expiration
2042-08-09

AI Technical Summary

Technical Problem

In complex mechanical equipment operating environments, early bearing fault signals have nonlinear and information redundancy characteristics, making it difficult to effectively extract fault features from low signal-to-noise ratio vibration signals, which leads to difficulties in the classification and diagnosis of rolling bearing faults.

Method used

A sparse dictionary is constructed using a dictionary learning algorithm. By solving the sparse optimization problem and calculating the sparse coefficients, the signal is reconstructed and the correlation is compared to achieve accurate identification of fault types.

Benefits of technology

Accurately extracting fault features from low signal-to-noise ratio vibration signals reduces noise interference and measurement point location limitations, thereby improving the accuracy of early fault identification.

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Abstract

The application discloses a bearing fault classification method and related device. The method comprises the following steps: inputting a first vibration signal collected under a plurality of known fault types of a bearing into a preset dictionary learning algorithm model after processing, and constructing a sparse dictionary containing a plurality of fault type training samples; processing a second vibration signal collected for fault classification to obtain a test sample, performing sparse optimization solving on the test sample, and obtaining a first sparse coefficient; respectively reconstructing the first sparse coefficient and the training sample under a plurality of fault types, and obtaining a plurality of fault type corresponding reconstruction signals; comparing the test sample with the plurality of fault type corresponding reconstruction signals, and determining the fault type corresponding to the test sample according to the comparison result. The application can accurately and effectively realize bearing fault classification diagnosis.
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