Equipment fault diagnosis method and device, electronic equipment and storage medium

Abstract

The invention provides an equipment fault diagnosis method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a source domain training data set and a target domain training data set according to target fault data; the target fault data is fault data of equipment having the same fault type and different fault modes with the to-be-diagnosed equipment; determining a first model parameter according to the source domain training data set and a fault diagnosis training model; determining a second model parameter according to the first model parameter, the source domain training data set, the target domain training data set, a preset model parameter and a fault diagnosis training model; determining a fault diagnosis model according to the second model parameter and a fault diagnosis training model; and performing fault diagnosis on the to-be-diagnosed equipment according to the fault diagnosis model. The fault diagnosis model of the to-be-diagnosed equipment is trained through the fault data of other equipment, knowledge migration from the existing fault data to the fault diagnosis model of the to-be-diagnosed equipment is realized, and the diversity of training the fault diagnosis model is improved.

Description

technical field [0001] The present application relates to the technical field of equipment fault diagnosis, and in particular, to a method, apparatus, electronic device, and storage medium for equipment fault diagnosis. Background technique [0002] With the continuous advancement of large-scale, complex, integrated, and intelligent industrial equipment, the degradation process and failure modes of industrial equipment are becoming more and more complex, and the demand and requirements for fault detection and diagnosis algorithms are increasing. In the context of industrial big data, thanks to the powerful model building and learning capabilities of deep learning, data-driven intelligent fault diagnosis has become automated and effective. [0003] However, the operation process of industrial equipment is complex, and the collected data has the characteristics of large scale, high dimension, multi-modality, nonlinearity, etc. As a result, the data-driven intelligent fault dia...

AI Technical Summary

Problems solved by technology

[0004] The present application provides a device fault diagnosis method, device, electronic device and storage medium, which are used to solve the defect in the prior art that the fault data cannot be obtained in advance during the operation of the device to be diagnosed, so that the fault diagnosis model cannot be trained in a targeted manner , realize the training of the fault diagnosis model of the equipment to be diagnosed through the fault data of other equipment, so that the knowledge transfer from the existing fault data to the fault diagnosis model of the equipment to be diagnosed improves the diversity of the training fault diagnosis model

Images