Remaining life prediction method for rotating machinery based on integrated gmdh framework

Abstract

The invention discloses a method for predicting the remaining life of a rotating machine based on an integrated GMDH framework. The method includes the following steps: S1, collecting multiple sensor data of a plurality of rotating machines of the same type from normal operation to failure, and passing the data processing to obtain the training data set W; S2, divide the data set into three different GMDH prediction networks respectively; S3, use the prediction output of the three GMDH networks on the training samples as a three-layer BP The input of the neural network trains the BP neural network, and the BP neural network is used to integrate the prediction results of the three GMDH networks; S4, using the integrated GMDH framework to predict the remaining life of the rotating machinery, calculate and output the remaining life prediction value. Compared with a classic LSTM network and a single GMDH network, the present invention can effectively improve prediction accuracy and generalization ability, and has greater practical guiding significance.

Description

technical field [0001] The invention belongs to the technical field of prediction of the remaining life of rotating machinery, and in particular relates to a method for predicting the remaining life of rotating machinery based on an integrated GMDH framework. Background technique [0002] In the field of machinery industry, rotating machinery equipment is the most commonly used equipment. It often works in harsh working environments such as heavy loads and high strength. Therefore, it is prone to various faults that affect its normal operation, and even interrupt production, seriously affecting production quality. and work efficiency. Once a fault occurs and cannot be detected and properly disposed of in time, the fault point may spread rapidly, causing a chain reaction, paralyzing the complete equipment on the entire production line, and easily causing disasters, threatening the safety of people's lives and properties. Therefore, in order to ensure the long-term stable and...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problems of poor generalization ability and single application conditions of the current prediction method for the remaining life of rotating machinery, and proposes a method for predicting the remaining life of rotating machinery based on the integrated GMDH framework, which mainly includes the following steps:

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