Building Local Models for Flexible Degradation Modeling and Prognostics

Abstract

To avoid unexpected failures of engineering systems, sensors have been widely used to monitor the degradation process of the systems. A number of studies have been conducted to analyze the collected sensor signals and predict the failure time. However, the existing studies are usually restricted and cannot be adapted to different practical situations. In this paper, we propose a systematic method for degradation modeling and prognosis that can be widely applied in different scenarios. In particular, the proposed method is capable to handle one or multiple sensors, powerful to capture the nonlinear relations between sensor signals and the degradation process with few assumptions, generic to consider multiple failure modes, flexible to deal with unequally spaced sensor measurements or asynchronous signals, and easily understandable with little preprocessing required. The main idea is to predict the failure time of an in-service unit based on a subset of the nearest historical units, where features are extracted from each sensor to describe the progression of sensor signals and local linear regression models are constructed to establish the relation between failure time and the extracted features. The prediction variance is then used as the goodness-of-fit measure, based on which decision-level fusion and feature-level fusion are proposed to combine multiple sensors. A case study with two datasets on the degradation modeling of aircraft engines is conducted which shows satisfactory performance of the proposed method. Note to Practitioners—This paper aims at modeling the collected sensor signals to understand the degradation process of the monitored engineering systems and predict the failure time. The main idea is to measure the similarity of units and predict the failure time of an in-service unit based on a subset of the nearest historical units. The developed method is widely applicable in different practical situations such as multiple sensors, multiple failure modes, asynchronous signals, and missing data. Furthermore, the method requires little preprocessing. There are several steps involved for implementing the proposed method: 1) collecting the sensor signals for historical units and the in-service unit; 2) extracting features from each sensor signal; 3) constructing a local linear model to predict the failure time based on the extracted features, and obtaining the prediction variance on the in-service unit; and 4) combining the information of different sensors using the decision-level fusion or feature-level fusion, if each unit is monitored by multiple sensors.