Abstract

Multi-stream degradation signals have been widely used to predict the residual useful lifetime of partially degraded systems. To achieve this goal, most of the existing prognostics models assume that degradation signals are complete, i.e., they are observed continuously and frequently at regular time grids. In reality, however, degradation signals are often (highly) incomplete, i.e., containing missing and corrupt observations. Such signal incompleteness poses a significant challenge for the parameter estimation of prognostics models. To address this challenge, this article proposes a prognostics methodology that is capable of using highly incomplete multi-stream degradation signals to predict the residual useful lifetime of partially degraded systems. The method first employs multivariate functional principal components analysis to fuse multi-stream signals. Next, the fused features are regressed against time-to-failure using (log)-location-scale regression. To estimate the fused features using incomplete multi-stream degradation signals, we develop two computationally efficient algorithms: subspace detection and signal recovery. The performance of the proposed prognostics methodology is evaluated using simulated datasets and a degradation dataset of aircraft turbofan engines from the NASA repository.

摘要

多流降解信号已被广泛用于预测部分降解系统的剩余使用寿命。为了实现该目标，大多数现有的预测模型都假设降解信号是完整的，即，在规则的时间网格上连续频繁地观察到它们。然而，实际上，退化信号通常（高度）不完整，即包含丢失和损坏的观测值。这种信号不完整对预测模型的参数估计提出了重大挑战。为了解决这一挑战，本文提出了一种预测方法，该方法能够使用高度不完整的多流降解信号来预测部分降解系统的剩余使用寿命。该方法首先采用多元功能主成分分析来融合多流信号。接下来，使用（log）-位置-比例回归将融合的特征针对失效时间进行回归。为了使用不完整的多流降级信号估计融合特征，我们开发了两种计算有效的算法：子空间检测与信号恢复。使用模拟数据集和来自NASA储存库的飞机涡扇发动机的退化数据集评估了所提出的预测方法的性能。