  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.
