Method and system for predicting residual life of mechanical equipment

Abstract

The invention discloses a method and system for predicting the residual life of mechanical equipment, and belongs to the field of state monitoring and residual life prediction. According to the method, a convolutional neural network and a bidirectional gating circulation unit are combined to form the hybrid neural network, so that time and space features are effectively extracted, the residual life prediction precision is improved, on the basis, attention weights are introduced into the hybrid neural network, a cooperative training mechanism of the hybrid neural network and an optimized genetic algorithm is adopted, according to the genetic algorithm, optimal attention weight parameters are searched through continuously transmitting parameters and feeding back loss, attention weight distribution of different time steps in residual life prediction is optimized, the importance of different time steps is accurately reflected, and compared with some traditional machine learning methods and deep learning, the method has the advantage that the root-mean-square error and the prediction score are much lower, and the problem that the existing residual life prediction precision is not high is effectively solved.

Description

technical field [0001] The invention belongs to the field of state monitoring and remaining life prediction, and more particularly relates to a method and system for predicting the remaining life of mechanical equipment. Background technique [0002] In recent years, with the rapid development of intelligent manufacturing, condition monitoring technology has been widely adopted in various engineering systems, such as wind energy conversion systems, battery systems, rolling bearing systems, etc. Surveillance technology has important implications for prediction and health management. Through the analysis of monitoring data, people can carry out residual life prediction to estimate the usable life of equipment before failure. Accurate remaining life predictions of industrial equipment such as turbofan engines, bearings, etc. can prevent machine failures in time and effectively reduce maintenance costs. Based on recent research, the remaining life prediction techniques of mech...

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Problems solved by technology

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a method and system for predicting the remaining life of mechanical equipment, the purpose of which is to assign different weights to data points of different time steps to highlight data points containing more degradation information , so as to solve the technical problem of low remaining service life prediction accuracy in traditional methods

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