The Health Index (HI) is crucial for evaluating system health, aiding tasks like anomaly detection and predicting remaining useful life for systems demanding high safety and reliability. Tight monitoring is crucial for achieving high precision at a lower cost, with applications such as spray coating. Obtaining HI labels in real-world applications is often cost-prohibitive, requiring continuous, precise health measurements. Therefore, it is more convenient to leverage run-to failure datasets that may provide potential indications of machine wear condition, making it necessary to apply semi-supervised tools for HI construction. In this study, we adapt the Deep Semi-supervised Anomaly Detection (DeepSAD) method for HI construction. We use the DeepSAD embedding as a condition indicators to address interpretability challenges and sensitivity to system-specific factors. Then, we introduce a diversity loss to enrich condition indicators. We employ an alternating projection algorithm with isotonic constraints to transform the DeepSAD embedding into a normalized HI with an increasing trend. Validation on the PHME 2010 milling dataset, a recognized benchmark with ground truth HIs demonstrates meaningful HIs estimations. Our methodology is then applied to monitor wear states of thermal spray coatings using high-frequency voltage. Our contributions create opportunities for more accessible and reliable HI estimation, particularly in cases where obtaining ground truth HI labels is unfeasible.

翻译：健康指标对于评估系统健康状态至关重要，可辅助完成异常检测、高安全性和高可靠性系统的剩余使用寿命预测等任务。在喷涂等应用中，低成本实现高精度监测的关键在于紧密监控。现实应用中获取健康指标标签往往成本高昂，需要持续精确的健康测量。因此，利用可能提供机器磨损状态潜在指示的从运行到失效数据集更为便捷，这使得必须应用半监督工具进行健康指标构建。本研究将深度半监督异常检测方法（DeepSAD）应用于健康指标构建。我们采用DeepSAD嵌入作为状态指标，以解决可解释性难题和对系统特定因素的敏感性。随后引入多样性损失函数以丰富状态指标。通过等值约束下的交替投影算法，将DeepSAD嵌入转化为具有上升趋势的归一化健康指标。在具有真实健康指标标注的PHME 2010铣削数据集这一公认基准上进行的验证表明，该方法能生成有意义的健康指标估计。最后将该方法应用于高频电压监测热喷涂涂层磨损状态。我们的研究为更易获取且更可靠的健康指标估计创造了机遇，尤其适用于无法获取真实健康指标标签的场景。