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. 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 contributions create opportunities for more accessible and reliable HI estimation, particularly in cases where obtaining ground truth HI labels is unfeasible.

翻译：健康指数（Health Index, HI）对评估系统健康状态至关重要，可用于异常检测和预测高安全高可靠性系统的剩余使用寿命。为实现低成本高精度的系统监测，实时监控不可或缺。在实际应用中获取HI标签成本高昂，需要连续精确的健康测量。因此，更便捷的方式是利用可能反映机器磨损状况的运行至失效数据集，这使得必须采用半监督工具进行HI构建。本研究将深度半监督异常检测方法（Deep Semi-supervised Anomaly Detection, DeepSAD）应用于HI构建，通过DeepSAD嵌入表征条件指标来解决可解释性挑战和系统特异性敏感问题。随后引入多样性损失函数来丰富条件指标，并采用带等渗约束的交替投影算法将DeepSAD嵌入转换为具有递增趋势的标准化HI。在包含真实HI标签的权威基准数据集——PHME 2010铣削数据集上的验证表明，该方法能生成有意义的HI估计。本研究的贡献为更具普适性和可靠性的HI估计创造了条件，尤其适用于无法获取真实HI标签的场景。