Remaining Useful Life Prediction of High-Frequency Swing Self-Lubricating Liner

Xiuhong Hao,Deng Pan,Shuqiang Wang,Mengfan Chen,Li Qing

doi:10.1155/2021/8843374

Abstract

The remaining useful life (RUL) prediction of self-lubricating spherical plain bearings is essential for replacement decision-making and the reliability of high-end equipment. The high-frequency swing self-lubricating liner (HSLL) is the key component of self-lubricating spherical plain bearings under high-frequency oscillation conditions. In this study, a RUL prediction method was proposed based on the Wiener process and grey system theory. First, the predictive processing of the wear depth was carried out using the grey model GM(1,1) to reduce the randomness and enhance the inherent regularity of the life test data. A degradation process model was established and the RUL was predicted online with the model parameter estimates based on the Bayesian updating strategy. Finally, examples were provided to elaborate the RUL prediction of the HSLL. The results show that the prediction accuracy of the proposed RUL prediction model is higher than that of the simple Wiener process during the entire residual life cycle of the HSLL. Based on the original wear data, the prediction accuracy of the RUL exhibited a strong dependence on prior samples and was relatively low owing to the larger deviation of the wear rate between the test sample and prior samples.

Highlights

As key activity connectors that are applied extensively to aerospace, engineering machinery, and water projects, selflubricating spherical plain bearings (SSPB) offer numerous advantages, such as a compact structure, no need for supplementary lubricant, and long life
A woven self-lubricating liner is critical for ensuring the service performance index of SSPB. e remaining useful life (RUL) prediction of the self-lubricating liner serves as the core and foundation of the service life assessment of SSPB, as well as the fault prediction and health management of highend equipment [3,4,5]
With the rapid development of machine learning theory, RUL predictions based on support vector machines have been developed [6, 7]. e performance degradation parameters of the mechanical parts are fitted and the degradation laws are obtained based on the artificial intelligence algorithm. e RUL is calculated with the failure threshold

Summary

Introduction

As key activity connectors that are applied extensively to aerospace, engineering machinery, and water projects, selflubricating spherical plain bearings (SSPB) offer numerous advantages, such as a compact structure, no need for supplementary lubricant, and long life. Pan et al proposed a RUL prediction method based on the Wiener degradation model by considering temporal uncertainty, measurement. Because numerous interference factors exist during life testing or equipment running, excessive randomness appears in the degradation data and the accuracy of the RUL prediction is significantly reduced. A RUL model was proposed based on the grey system theory and Wiener process. Ereafter, the degradation process model of the wear depth was established and the RUL was predicted at any moment with the model parameter estimates based on the Bayesian updating strategy. Compared to the simple Wiener process, the prediction accuracy of the proposed method for the RUL was improved significantly in the entire residual life cycle.

Original Data Processing Based on Grey System Theory

Degradation Process Model of HSLL

MAE m