Patent ID: 11928613
Assignee: EAST CHINA JIAOTONG UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A bearing fault diagnosis method based on a fuzzy broad learning model, comprising:
collecting vibration signal data of bearings in operation, and preprocessing the vibration signal data of the bearings in operation;
extracting time-domain feature parameters, frequency-domain feature parameters, and time-frequency-domain feature parameters from preprocessed vibration signal data;
performing a fusion processing and a normalization processing on the time-domain feature parameters, the frequency-domain feature parameters, and the time-frequency-domain feature parameters in sequence to obtain a fused data set; dividing the fused data set into training set data and test set data according to a predetermined ratio;
constructing an initial fuzzy broad learning model based on a broad learning system and a fuzzy system;
training the initial fuzzy broad learning model through the training set data to obtain a target fuzzy broad learning model;
calculating a membership value of vibration signal data of a bearing to be tested by the target fuzzy broad learning model; and determining a fault type of the bearing to be tested according to the membership degree value;
wherein the target fuzzy broad learning model comprises a fuzzy broad learning algorithm; a step of calculating the membership value of the vibration signal data of the bearing to be tested by the target fuzzy broad learning model and determining the fault type of the bearing to be tested according to the membership value comprises:
calculating the membership value of the vibration signal data of the bearing to be tested by the fuzzy broad learning algorithm, and calculating a classification matrix of the vibration signal data of the bearing to be tested according to the membership degree value; and
classifying the fault type of the bearing to be tested through the classification matrix.