Machine Learning-Enhanced State-Aware Health Assessment of Industrial Assets Under Zero-Label Constraints

Industrial health assessment often faces the challenge of sensor scarcity and a lack of labelled failure datasets, making conventional monitoring difficult to scale. This study addresses these constraints by proposing a state-aware framework that relies exclusively on routinely measured electrical parameters (active power, current, voltage, and power factor). The main challenge lies in distinguishing benign load variations from actual degradation without process-level context. To overcome this, we integrate automated operating-state recognition using XGBoost with per-state regression modelling to estimate the expected active power. A standardized Health Index (HI) is then derived from the residuals to quantify deviations from normal behaviour. Evaluated on a fleet of three-phase injection moulding machines, the framework demonstrates substantial performance improvements: the state-aware approach increased the median coefficient of determination from 0.64 to 0.86 and reduced residual variability by 30% compared to context-agnostic models. These findings show that synergistic system integration provides a stable and interpretable indicator for early degradation detection and fleet-level benchmarking under strict zero-label industrial constraints.