Constructing Health Indicators for Systems with Few Failure Instances Using Unsupervised Learning
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Abstract
Health indicators are crucial to assess the health of complex systems. In recent years, several studies have developed data-driven health indicators using supervised learning methods. However, due to preventive maintenance, there are often not enough failure instances to train a supervised learning model, i.e., the data is unlabelled with an unknown actual Remaining Useful Life (RUL). In this paper, we therefore propose an unsupervised learning model to construct a health indicator for an aircraft system. The considered system is operated under highly-varying operating conditions. We train a Convolutional Neural Network (CNN) to predict the sensor measurements from the operating conditions. We train this neural network solely with the sensor measurements of just-installed, non-degraded systems. The CNN therefore learns the normal range of the sensor measurements, given the operating conditions, for non-degraded systems only. For a degraded system, the predicted sensor measurements deviate from the actual sensor measurements. Based on the prediction errors, we construct a health indicator for the aircraft system. We apply this approach to develop a health indicator for the aircraft turbofan engines of dataset DS02 and DS06 of N-CMAPSS. The resulting health indicators have a high prognosability of 0.91 for DS02 and of 0.83 for DS06, a mean trendability of 0.86 for DS02 and of 0.87 for DS06, and a mean monotonicity of 0.31 for DS02 and of 0.33 for DS06, and can thus be used to make a reliable assessment of the system's health.