A probabilistic approach to remaining useful life prediction of rolling element bearings

先验概率 断层(地质) 计算机科学 不可见的 降级(电信) 概率逻辑 方位(导航) 振动 贝叶斯概率 故障检测与隔离 可靠性工程 数据挖掘 工程类 人工智能 数学 计量经济学 地质学 电信 物理 量子力学 地震学 执行机构
作者
Guru Prakash,Sriram Narasimhan,Mahesh D. Pandey
出处
期刊:Structural Health Monitoring-an International Journal [SAGE Publishing]
卷期号:18 (2): 466-485 被引量:21
标识
DOI:10.1177/1475921718758517
摘要

In this article, we present a probabilistic approach for fault detection and prognosis of rolling element bearings based on a two-phase degradation model. One of the main issues in dealing with bearing degradation is that the degradation mechanism is unobservable and can only be inferred through appropriate surrogate measures obtained from indirect sensory measurements. Furthermore, the stochastic nature of the degradation path renders fault detection and estimating the end-of-life characteristics from such data extremely challenging. When such components are a part of a larger system, the exact degradation path depends on both the operating and loading conditions, which means that the most effective condition monitoring approach should estimate the degradation model parameters under operational conditions, and not solely from isolated component testing or historical information. Motivated by these challenges, a two-phase degradation model using surrogate measures of degradation from vibration measurements is proposed and a Bayesian approach is used to estimate the model parameters. The underlying methodology involves using priors from historical data, while the posterior calculations are undertaken using surrogate measures obtained from a monitored unit combined with the aforesaid priors. The problem of fault detection is posed as a change point location problem. This allows the prior knowledge obtained from the past failures to be integrated for maintenance planning of a currently working unit in a systematic way. The correlation between the degradation rate and the time of occurrence of the change point, an often overlooked aspect in prognosis, is also considered in here. A numerical example and a case study are presented to illustrate the overall methodology and the results obtained using this approach.
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