Infrastructure for Remaining Useful Life (RUL) Estimation

Whether operational knowledge is systematically recorded

• Systematic capture of equipment degradation signals including vibration, temperature, oil analysis, and runtime hours into a single time-series store with asset-level linkage

How data is organized into queryable, relational formats

• Structured classification of asset types, degradation regimes, and failure modes with documented prognostic feature sets per component class

How frequently and reliably information is kept current

• Scheduled retraining cadence for RUL models triggered by new failure events or when prediction error exceeds defined tolerance thresholds

How explicitly business rules and processes are documented

• Formalized asset lifecycle records linking nameplate data, installation date, cumulative operating hours, and overhaul history as queryable structured fields

Whether systems expose data through programmatic interfaces

• API or query layer exposing current condition scores and historical degradation trajectories to the RUL estimation model at inference time

Whether systems share data bidirectionally

• Integration between RUL prediction outputs and procurement or inventory systems to trigger spare part pre-positioning before projected end-of-life