Method for predicting remaining life of mechanical equipment based on multi-condition dynamic benchmarking

Abstract

The invention provides a method for predicting remaining life of mechanical equipment based on multi-condition dynamic benchmarking, comprising the following steps of: Firstly, establishing a mechanical equipment degradation state space model comprising the state transition equation and the observation equation; Secondly, estimating the unknown parameters and signal transformation parameters of the model, that is, estimating the parameters of the state transition equation in the maximum likelihood estimation method based on the training sample failure time data and transforming the monitoringsignals under different operating conditions into the transformation parameters of the reference condition monitoring signal by linear interpolation estimation, and estimating the parameters of the observation equation by the transformed signal, then performing dynamically benchmarking on the monitoring signal of the test sample under different working conditions and estimating the state value ofthe test sample in the particle filter algorithm, finally, calculating the analytical solution of the probability density function of the remaining life of the test sample. The method for predicting remaining life of mechanical equipment based on multi-condition dynamic benchmarking can perform monitoring signal benchmarking under multiple conditions dynamically and dynamically in the process of remaining life prediction, which is beneficial for improving the accuracy of the prediction of the remaining life of mechanical equipment.

Description

technical field [0001] The invention belongs to the technical field of mechanical equipment health management and remaining life prediction, and in particular relates to a method for predicting the remaining life of mechanical equipment based on dynamic benchmarking of multiple working conditions. Background technique [0002] With the advancement of science and technology, mechanical equipment continues to become larger, more complex, and more sophisticated. The diversification of its functions makes the operating conditions more complicated, which easily leads to frequent failures, which not only affects the safety of equipment operation, but also brings economic benefits. Huge losses, and even a serious threat to people's lives. Therefore, it is particularly important to carry out health management and remaining life prediction for mechanical equipment to achieve preventive maintenance. [0003] In actual production, multi-working conditions have two main impacts on the ...

AI Technical Summary

Problems solved by technology

[0003] In actual production, multi-working conditions have two main impacts on the remaining life prediction of mechanical equipment. One is to change the degradation rate of mechanical equipment, and the other is to cause a sudden change in the amplitude of the monitoring signal.

The existing technology often only considers the influence of different working conditions on the degradation rate of mechanical equipment, but ignores its influence on the signal amplitude

Sudden changes in signal amplitude can easily lead to misjudgment of the degradation state process of mechanical equipment, which in turn leads to a decrease in the accuracy of remaining life prediction

Images