Semisupervised Health Index Monitoring With Feature Generation and Fusion

Abstract

The health index (HI) is crucial for evaluating system health, important for tasks, such as anomaly detection and remaining useful life prediction of safety-critical systems. Real-time, meticulous monitoring of system conditions is essential, especially in manufacturing high-quality and safety-critical components, such as spray coating. However, acquiring accurate health status information (HI labels) in real scenarios can be difficult or costly because it requires continuous, precise measurements that fully capture the system's health. As a result, using datasets from systems run-to-failure, which provide limited HI labels at just the healthy and end-of-life phases, becomes a practical approach. We employ the deep semisupervised anomaly detection (DeepSAD) embeddings to tackle the challenge of extracting features associated with the system's health state In addition, we introduce a diversity loss to further enrich the DeepSAD embeddings. We also propose applying an alternating projection algorithm with isotonic constraints to transform the embedding into a normalized HI with an increasing trend. Validation on the PHME2010 milling dataset, a recognized benchmark with ground truth HIs, confirms the efficacy of our proposed HIs estimations. Our methodology is further applied to monitor wear states of thermal spray coatings using high-frequency voltage. Our contributions facilitate more accessible and reliable HI estimation, particularly in scenarios where obtaining ground truth HI labels is impossible.