Method for Determining Starting Point of Rolling Bearing Life Prediction Based on Linear Regression

Author:

Gao ,Lv ,Wu ,Si ,Hu

Abstract

Aimed at addressing the problem that the subjective selection of start prediction time (SPT) in rolling bearing remaining useful life (RUL) prediction will lead to excessive noise in the prediction signal, a linear-regression-based SPT point determination was proposed. The sliding window linear regression method was used to establish sliding windows in the root mean square (RMS) range to obtain the RMS gradient domain. The threshold for the RMS gradient was set, and the continuous trigger threshold mechanism to determine the SPT point was used. The experimental results show that the linear-regression-based method can adaptively determine the SPT point and improve the accuracy of life prediction.

Funder

National Natural Science Foundation of China

Key Science and Technology National Program of Henan Province

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. From Innovation to Standardization—A Century of Rolling Bearing Life Formula;Machines;2024-06-27

2. Lifetime Prediction Based on Age-State Dependent Diffusion Model;2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou);2023-10-12

3. A sliding sequence importance resample filtering method for rolling bearings remaining useful life prediction based on two Wiener-process models;Measurement Science and Technology;2023-10-11

4. Incipient Fault Point Detection Based on Multiscale Diversity Entropy;Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems;2023-06-13

5. Combining first prediction time identification and time-series feature window for remaining useful life prediction of rolling bearings with limited data;Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability;2023-01-12

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