Abstract

This article introduces a Bayesian multiple change point model for a collection of degradation signals in order to predict remaining useful life of rotational bearings. The model is designed for longitudinal data, where each trajectory is a time series segmented into multiple states of degradation using a product partition structure. An efficient Markov chain Monte Carlo algorithm is designed to implement the model. The model is run on in situ data, where vibration measurements are taken to indicate bearing degradation. The results suggest that bearing degradation exhibit an auto-correlation structure that we incorporate into the product partition model and often experience more than one degradation phase.

摘要

本文介绍了用于一组退化信号的贝叶斯多变点模型，以预测旋转轴承的剩余使用寿命。该模型是为纵向数据设计的，其中每个轨迹都是一个时间序列，使用产品分区结构分割成多个退化状态。设计了一种高效的马尔可夫链蒙特卡罗算法来实现该模型。该模型基于现场数据运行，其中进行振动测量以指示轴承退化。结果表明，轴承退化表现出一种自相关结构，我们将其纳入产品划分模型中，并且经常经历多个退化阶段。