Guangdong Ocean University

Bo Wen

Guoyan Yu

Zheng Liu

Donglin Hou

Qiang Liu

Jiawei Zhang

Xiaoming Xu

Jiale Liao

A rolling bearing combined fault diagnosis method based on a multi-domain feature construction is provided, including: collecting vibration signals of rolling bearings, extracting time-domain and frequency-domain features of fault-sensitive vibration signals of the rolling bearings, and obtaining an in-domain feature set; performing an improved empirical mode decomposition on the vibration signals of the rolling bearings to obtain time-frequency features; constructing a multi-domain fault sample set based on the in-domain feature set and the time-frequency features; performing a fault mechanism analysis on the multi-domain fault sample set to obtain modulation features under each single fault; carrying out a feature engineering on the time-domain and frequency-domain features, and screening out relevant features, redundant features and irrelevant features; performing an unsupervised hierarchical clustering on test set fault samples, and then inputting the test set fault samples into a KNN model for a classification, and obtaining rolling bearing combined fault diagnosis results.

Rolling bearing combined fault diagnosis method based on multi-domain feature construction

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Zhanjiang, China

Bo Wen