A Deep Gaussian Process Approach for Predictive Maintenance

In the era of digitalization, ubiquitous sensing technologies have paved the way for predicting the remaining useful life (RUL) of assets or systems. In both practical and theoretical fields, enabled by machine learning algorithms, predictive maintenance (PdM) has attracted significant attention. Among machine learning algorithms, deep learning benefits from its multilayer architecture for performing feature engineering. It provides high-quality results in an efficient manner and has become a prevalent approach. However, only predicting the expected RUL is insufficient. For practically implementing PdM approaches, both the overestimating and underestimating prediction risks should also be analyzed and mitigated before making maintenance decisions. In this article, we propose a deep Gaussian process approach to predict the expected RUL and estimate the associated variance. The approach adopts the multilayer architecture such that the predicted result is robust against the selection of kernel functions. Several novel evaluation metrics are introduced to evaluate the predicted RUL distribution. To realize a complete framework of PdM, enabled by the RUL distribution, we propose a distribution-based cost minimization algorithm to dynamically optimize the predicted maintenance thresholds. The overall approach is tested with two practical datasets.