A scoped, collaborative research project to identify practical predictive maintenance use cases, data needs, pilot plans, and governance that reduce downtime while avoiding overhyped AI pitfalls.
Three concrete use-case examples (signals, lead time, actions). 1. Motor bearing faults (manufacturing line). Signals: vibration RMS and spectral bands, bearing housing temperature. Typical lead time: 1–6 weeks depending on failure mode. Action: schedule bearing replacement in maintenance window; perform grease change...
A practical, step-by-step pilot blueprint for scoping, executing, validating, and governing predictive maintenance pilots — with clear success metrics, data requirements, safety controls, a prioritization matrix, and a regulatory/safety checklist.
A practical, structured pilot plan to evaluate a predictive maintenance (PM) use case—defines asset selection, data needs, success metrics, timeline, roles, risk controls, verification steps, and a clear go/no‑go decision gate to run defensible, actionable pilots and avoid overhyped AI pitfalls.