Model training method and device, fault prediction method and device and electronic equipment

Abstract

The invention relates to a model training method and device, a fault prediction method and device and electronic equipment. The model training method comprises the following steps: selecting a plurality of service measuring point sequences related to a motor fault from a plurality of historical measuring point sequences, wherein the plurality of historical measuring point sequences are collected in a historical operation stage of a sample motor; screening out a plurality of data measuring point sequences having mathematical correlation with the plurality of service measuring point sequences from the plurality of historical measuring point sequences; selecting a plurality of target training sequences in the front of the relevance of the target motor fault and a plurality of covariable training sequences in the back of the relevance of the target motor fault from an original feature set composed of the plurality of service measuring point sequences and the plurality of data measuring point sequences; and training the target model through the plurality of target training sequences and the plurality of covariable training sequences to obtain a motor fault prediction model. According to the model training method, the fault prediction method and device and the electronic equipment provided by the embodiment of the invention, the reliability of the fault prediction data can be ensured.

Description

technical field [0001] The present application relates to the technical field of predictive maintenance of industrial equipment and processes, and specifically relates to a model training method, a fault prediction method, a device, and electronic equipment. Background technique [0002] Motor is the core power device of modern industry. It can convert electrical energy into mechanical energy and is widely used in traditional manufacturing, engineering construction, transportation and other industries. At present, due to complex working conditions, product design defects, improper operation of the staff, etc., the motor is prone to failure, and there is no corresponding prediction method for this kind of failure. Contents of the invention [0003] The purpose of the present application is to provide a model training method, a fault prediction method, a device and an electronic device to solve the above problems. [0004] The model training method provided by the embodimen...

AI Technical Summary

Problems solved by technology

At present, due to complex working conditions, product design defects, improper operation of the staff, etc., the motor is prone to failure, and there is no corresponding prediction method for this kind of failure

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