Intelligent fault diagnosis method based on multi-mode fusion deep learning

Abstract

The invention, which belongs to the technical field of industrial equipment fault diagnosis, discloses an intelligent fault diagnosis method based on multi-mode fusion deep learning. Fault features implied in structured data and unstructured data are extracted respectively; the different extracted fault features are fused organically; and fault classification is carried out by using a softmax classifier to realize prediction and diagnosis of the health state of industrial equipment. With the method disclosed by the invention, fault feature extraction, feature fusion and fault classification ofmulti-mode heterogeneous data from different sensors can be well realized. Because realization of fault feature extraction, feature fusion and fault classification of multi-mode heterogeneous data from different sensors, the diagnosis cost is saved and high universality is realized. The intelligent fault diagnosis method can be extended to the fault diagnosis of various industrial devices.

Description

technical field [0001] The invention relates to an intelligent fault diagnosis method based on deep learning, belonging to the technical field of industrial equipment fault diagnosis. Background technique [0002] In complex industrial manufacturing processes such as aerospace, a large number of large and complex equipment are involved. Once the equipment fails and cannot be eliminated or repaired in time, it will cause huge economic losses to the enterprise, the country and even human society. In order to avoid such problems, it is very important to effectively evaluate and predict the health status of industrial equipment with the help of operating industrial big data for timely fault identification and diagnosis. [0003] Intelligent fault diagnosis methods play an important role in predicting potential equipment faults and identifying current fault types. Most of the existing intelligent fault diagnosis methods are based on the historical data of equipment operation wit...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the defects of the existing technology, in order to solve the problem of extracting omni-directional fault information based on multi-modal and heterogeneous data and intelligently diagnosing equipment faults, a method based on multi-modal fusion deep learning is proposed Intelligent Fault Diagnosis Method

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