An Adaptive Prediction Method for Remaining Life of Service Equipment Based on Degradation Data Modeling

Abstract

The invention discloses an adaptive prediction method for the remaining life of service equipment based on degradation data modeling, which uses the Bayesian method and EM algorithm to realize the degradation modeling and remaining life prediction of the service equipment, including the following steps: 1) stochastic degradation modeling ; 2) Model random parameter update based on Bayesian method; 3) Remaining life prediction; 4) Model parameter estimation based on EM algorithm. The present invention introduces a parameter self-adaptive update mechanism based on the EM algorithm in the construction method of the exponential stochastic degradation model for the remaining service life prediction of service equipment, so that all parameters of the exponential stochastic degradation model are continuously updated with the accumulation of real-time data of the service equipment, thus predicting The results can better reflect the actual operation of the equipment and achieve the purpose of reducing the uncertainty of prediction. The invention does not need multiple historical data of similar equipment to initialize the degradation model, and can adaptively update model parameters and remaining life distribution.

Description

technical field [0001] The invention relates to the technical field of prediction of remaining life of service equipment, in particular to an adaptive prediction method of remaining life of service equipment based on degradation data modeling. Background technique [0002] With the rapid development of information and sensing technology, the research work in the field of Prognostics and health management (PHM) has aroused more researchers' interest, and the optimal maintenance decision made on this basis is very important for improving system reliability. , Preventing unknown failure events of the system and reducing maintenance costs have important practical benefits. The core problem of PHM is to effectively predict the remaining life of equipment based on the monitoring data obtained by sensors. [0003] Traditional life prediction methods are based on failure data. However, for equipment with high reliability and high cost, it is usually difficult to obtain a large numb...

AI Technical Summary

Problems solved by technology

However, there is no corresponding estimation method for the non-random unknown parameters in the model (parameters in the prior distribution of random parameters and the variance parameter of the error term), but assuming that there are multiple historical degradation data of similar equipment, using statistical method to estimate it

However, it is often difficult to obtain enough historical degradation data for similar equipment in practice, especially for newly operating equipment

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