The Health Index (HI) is crucial for evaluating system health and is important for tasks like anomaly detection and Remaining Useful Life (RUL) 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 coatings. 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 only at the healthy and end-of-life phases, becomes a practical approach. We employ Deep Semi-supervised Anomaly Detection (DeepSAD) embeddings to tackle the challenge of extracting features associated with the system's health state. Additionally, 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 HI estimations. Our methodology is further applied to monitor the wear states of thermal spray coatings using high-frequency voltage. These contributions facilitate more accessible and reliable HI estimation, particularly in scenarios where obtaining ground truth HI labels is impossible.

翻译：健康指标（HI）对于评估系统健康状况至关重要，在异常检测和安全关键系统（如喷涂涂层）的剩余使用寿命（RUL）预测等任务中具有重要意义。对系统状态进行实时精细监测尤为必要，特别是在制造高质量安全关键部件时。然而，在实际场景中获取准确健康状态信息（HI标签）往往困难且成本高昂，因为这需要持续精确的测量以完整捕捉系统健康状态。因此，使用系统全寿命周期数据集（仅在健康阶段和寿命终止阶段提供有限HI标签）成为一种实用方法。我们采用深度半监督异常检测（DeepSAD）嵌入来解决提取系统健康状态相关特征的挑战，并引入多样性损失函数以进一步丰富DeepSAD嵌入特征。此外，我们提出应用具有等张约束的交替投影算法，将嵌入特征转换为具有单调递增趋势的归一化HI。在PHME2010铣削数据集（具备真实HI标签的公认基准数据集）上的验证证实了所提HI估计方法的有效性。我们进一步将该方法应用于通过高频电压监测热喷涂涂层的磨损状态。这些贡献促进了更易获取且可靠的HI估计，特别是在无法获得真实HI标签的场景中。