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.
Begin Here: How predictive maintenance pilots actually deliver value. Many teams hear that "AI can predict failures" and think the hardest part is the model. In practice, the hardest part is making predictions useful, trustworthy, and actionable for operations. This research project exists to help maintenance and...
Practical guide to scoping a minimally viable predictive maintenance pilot. Predictive maintenance pilots succeed when they are small, measurable, and operationally actionable. This guide helps you choose the right use case, identify the minimum data and analytics needed, design a pilot that operations can act on, and...
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Explore this interactive audit, assessment, reflection, or practical tool. Sign in to save your responses and return to them later.
Operational integration & governance checklist. Use this checklist to reduce risk and ensure the pilot is operationally useful and trustworthy. Stakeholder alignment Sponsor identified and committed to pilot resources. Maintenance, reliability, IT/OT, and data owners named. Data governance Sensor ownership and access...
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...