Adaptive prediction method of residual service life of service equipment modeled based on degradation data

Abstract

The invention discloses an adaptive prediction method of residual service life of service equipment modeled based on degradation data. Degradation modeling and residual service life prediction of the service equipment are realized by a Bayesian method and an EM algorithm, and the method comprises the following steps of (1) randomly degrading and modeling; (2) updating random model parameters based on the Bayesian method; (3) predicting the residual service life; and (4) establishing model parameters based on the EM algorithm. An adaptive parameter updating mechanism based on the EM algorithm is introduced into a method for establishing a random index degradation model for predicting residual service life of service equipment, so that all parameters of the random index degradation model are continuously updated when the real-time data of the service equipment are accumulated, and thus the actual operation condition of the equipment can be reflected by predicted results, and the purpose of minimizing uncertainty prediction is achieved. According to the adaptive prediction method, historical data of multiple similar equipment are not required to initialize the degradation model, namely model parameters and residual service life distribution can be adaptively updated.

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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