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...
Explore this interactive audit, assessment, reflection, or practical tool. Sign in to save your responses and return to them later.
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...
A practical, ready-to-use template and checklist (with example spreadsheet column schema and evaluation metrics) to collect the sensor, operational, and labeled failure data needed to pilot predictive maintenance models. Includes asset criticality scoring, sensor selection and sampling guidance, event-labeling rules, data quality checks, storage and retention recommendations, and pilot evaluation criteria.
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.
An interactive, saveable worksheet to scope, plan, and evaluate predictive maintenance AI pilots. Captures objectives, data readiness, labeling needs, measurable success criteria, timeline, governance checklist, and operational actionability so teams can run focused, measurable pilots and avoid common AI pitfalls.
A practical, interactive checklist to evaluate asset inventories, sensors, data quality, labeling, event synchronization, governance, and pilot sizing before launching a predictive maintenance or condition-based monitoring pilot.
Interactive checklist to choose sensor types, placement, sampling rates, labeling conventions, storage, and pilot metrics for reliable predictive maintenance pilots. Captures structured answers you can save with a pilot, export, or attach to a Deck.