LEADI: Operating-Mode-Aware Machine Condition Monitoring for Leak-Related Energy Anomalies—A Before-and-After Maintenance Study of a Single Production Asset

Compressed-air leaks create persistent parasitic demand, but machine-level condition monitoring is difficult because air consumption changes strongly with operating mode. LEADI (Leak Energy Anomaly Detection Index) was developed as an operating-mode-aware procedure that evaluates the deviation of directly measured flow rate from a local reference baseline derived from a stable post-repair condition with maintained pressure and low within-window variability. The method was developed on days 1–5 and evaluated on held-out days 6–7 from two one-week campaigns conducted before and after implementation of the prescribed corrective actions. With 60 min windows, LEADI flagged 19/19 evaluable pre-repair and 0/17 post-repair windows, with diagnostic coverage of 39.6% and 35.4%, respectively. A simple fifth-percentile flow comparator without operating-mode selection flagged 47/48 versus 1/48 windows. This shows that the low-flow region itself contains strong discriminatory information for separating the two periods. The role of the operating-mode layer is to restrict engineering interpretation to pre-specified eligible operating conditions. The flow-rate difference within the diagnostic operating condition was 122.0 L/min (95% CI 114.6–132.3). Over a common 168 h basis, measured volume decreased by 1370.8 m3 (28.90%), while a separate check normalized by pressurized time gave 28.12%. Because the specific energy consumption of the compressor station was not measured, the energy effect is reported only as a scenario for the same 168 h. The results support the applicability of LEADI as a selective decision-support layer for the investigated asset and the two observed conditions, without establishing universal leak detection or causal attribution of the observed change to individual defects.