Method for predicting residual effective life of rolling bearing based on fractional left stable motion

Abstract

The invention relates to a rolling bearing residual effective life prediction method based on fractional left stable motion, and the method comprises the steps: 1), decomposing an original vibration signal of a rolling bearing into K intrinsic mode functions through employing a variational mode decomposition method, and selecting an optimal mode through monotonicity, robustness and tendency indexes; 2) modeling a degradation process of the bearing by utilizing fractional Levy-based stable motion to obtain a degradation model; 3) setting a fault threshold, for the degradation model in the step2), acquiring a Hurst index of fractional Levy stable motion and estimating model parameters, and acquiring an estimated value of a feature index and estimated values of a drift coefficient and a diffusion coefficient; and 4) substituting the parameters obtained in the step 3) into the degradation model in the step 2), and predicting the residual effective life of the bearing by using a Monte Carlo method. Compared with the prior art, the method has the advantages of accurate prediction, resource waste reduction and the like.

Description

technical field [0001] The invention relates to the field of prediction of the remaining service life of rolling bearings, in particular to a method for predicting the remaining effective life of rolling bearings based on fractional levy stable motion. Background technique [0002] Nowadays, all kinds of mechanical equipment in modern industry have a certain service life. If not dealt with, the mechanical equipment beyond the service life may break down. Therefore, the remaining service life prediction of large rotating equipment has become an issue of increasing concern. . At present, there are many methods to predict the remaining service life of rotating equipment, such as Wiener process, Markov process, γ process, and these processes are memoryless. Since the degradation process of rolling bearings is not only related to the current state but also to the historical state, that is, the degradation process is a long-term slow-changing process with long memory or long corr...

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Problems solved by technology

However, both local average decomposition and empirical mode decomposition methods have problems such as mode aliasing and boundary effects

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