Rotary machine residual life prediction method based on integrated GMDH framework

Abstract

The invention discloses a rotary machine residual life prediction method based on an integrated GMDH framework, and the method comprises the following steps: S1, collecting the data of a plurality ofsensors in a process from normal operation to fault failure of a plurality of rotary machines of the same type, and obtaining a training data set W through data processing; s2, dividing the data set differently to be used for constructing three GMDH prediction networks with differences respectively; s3, taking prediction output of the three GMDH networks on the training sample as input of a three-layer BP neural network to train the BP neural network, wherein the BP neural network is used for integrating prediction results of the three GMDH networks; and S4, predicting the residual life of therotary machine by utilizing the integrated GMDH framework, and calculating and outputting a residual life prediction value. Compared with a classic LSTM network and a single GMDH network, the methodcan effectively improve the prediction precision 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...

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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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