Nonparametric model-based prognostics

J. Hines, D. Garvey
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引用次数: 7

Abstract

Equipment, process, and system prognostic techniques can be classified as belonging to one of three major classes of methods: 1) conventional reliability-based using failure times (Weibull), 2) population based with environmental considerations (e.g. proportional hazards modeling), and 3) individual based (e.g. general path model). A new individual-based prognostic algorithm, termed the path classification and estimation (PACE) model, has been developed and is based entirely on failure data. This model recasts the general path model (GPM), which is the foundation of the majority of the modern individual based prognosis algorithms, as a classification problem, where a current device's degradation path is classified according to a series of exemplar paths and the results of the classification are used to estimate the remaining useful life (RUL) of the device. The requirement of the existence of a failure threshold is removed, thereby enabling the PACE to be applied to ldquoreal worldrdquo systems, where a single failure threshold is not likely to occur. If the failure threshold is known, simple formatting may be applied to the degradation paths such that they can be easily used with the PACE. The newly proposed method was applied to data collected from the hydraulic steering system of a drill used for deep oil exploration with the objective of detecting, diagnosing, and prognosing faults. The PACE was used to predict the RUL for several failure modes using actual data. For this work, a three tiered architecture was implemented, where conventional reliability methods were used to estimate the population-based RUL, PACE population-based prognosers were trained to map the cause of a failure mode to the RUL, and PACE individual prognosers were trained to map the effects of a failure mode to the RUL. It was found that the population based prognoser produced RUL estimates with large errors (75 hours) and uncertainties (261 hours). The individual prognosers were found to significantly outperform the population based prognoser, with errors ranging from 1.2 to 11.4 hours with 95% confidence intervals ranging from 0.67 to 32.02 hours.
基于非参数模型的预测
设备、过程和系统预测技术可以分为三大类方法之一:1)基于故障时间的传统可靠性(威布尔),2)基于环境因素的群体(如比例风险建模),以及3)基于个体(如一般路径模型)。一种新的基于个体的预测算法,称为路径分类和估计(PACE)模型,已经开发出来,完全基于故障数据。该模型将一般路径模型(general path model, GPM)重新定义为一个分类问题,根据一系列的样本路径对当前设备的退化路径进行分类,并使用分类结果来估计设备的剩余使用寿命(RUL)。GPM是大多数现代基于个体的预测算法的基础。排除了存在故障阈值的要求,从而使PACE能够应用于不太可能出现单个故障阈值的虚拟世界系统。如果故障阈值是已知的,则可以将简单的格式化应用于降级路径,以便它们可以轻松地与PACE一起使用。将该方法应用于深部石油钻探钻机液压转向系统的数据采集,实现了故障的检测、诊断和预测。利用实际数据,利用PACE预测了几种失效模式下的RUL。在这项工作中,实施了一个三层架构,其中使用传统的可靠性方法来估计基于人群的RUL,训练基于PACE人群的预测者将故障模式的原因映射到RUL,训练PACE个人预测者将故障模式的影响映射到RUL。结果发现,基于人群的预后产生的RUL估计误差较大(75小时),不确定性较大(261小时)。发现个体预测明显优于基于群体的预测,误差范围为1.2至11.4小时,95%置信区间为0.67至32.02小时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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