A fault diagnosis method based on multi-source data fusion and deep optimization network

Abstract

The application discloses a kind of based on multi-source data fusion and deep optimization network's fault diagnosis method, belong to wind turbine bearing fault diagnosis technical field, including: obtaining the multi-source sensor data of wind turbine under different working conditions, and the multi-source sensor data collected is preprocessed;Design multi-source data feature fusion algorithm based on correlation variance contribution, the multi-source sensor data after pre-processing is fused, and the fuzzy entropy value of the multi-source sensor data after fusion is extracted as the feature vector of input intelligent fault diagnosis model;Intelligent fault diagnosis model DBE based on optimized deep belief network is constructed, and the method and hippocampus optimization algorithm of greedy learning are used to train DBE;Based on the trained DBE, fault diagnosis is carried out.The method solves the problems of signal abnormal value and data missing under the influence of wind turbine variable working condition and external noise interference and fault diagnosis reliability, improves the accuracy of wind turbine fault diagnosis.

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