AI & Automation for Predictive Maintenance — Research Project

This research project helps maintenance and operations teams design realistic predictive maintenance pilots: identify high-value use cases, understand data and integration needs, set measurable pilot goals, and build governance to manage false positives, data quality, and human oversight.

What visitors will understand and be able to do

After exploring this project you will be able to: pick practical predictive-maintenance use cases that match your operational priorities; assess whether your sensors, logs, and CMMS data are sufficient; design a small, measurable pilot; define acceptance criteria and human-in-the-loop rules; and plan governance to limit false alarms and preserve operator trust.

Who benefits

This research is aimed at maintenance leaders, reliability engineers, plant and facility managers, field-service teams, service-company owners, and technical managers in manufacturing, utilities, healthcare facilities, commercial real estate, fleets, and small-to-midsize service businesses. For example: a packaging line manager wanting fewer unplanned stops, an HVAC service provider improving preventative replacement schedules, or a fleet manager reducing roadside failures.

Key topics covered

Practical topics include: selecting pilot assets and failure modes; sensor and data requirements; data cleaning and labeling; integrating predictions with work management systems; pilot metrics (lead time, precision, actionable alerts); human-in-the-loop workflows; managing false positives and missing data; vendor and model evaluation; change management; and governance, privacy, and safety considerations.

Practical examples and pilot patterns

Examples show how to scope low-risk, high-value pilots: monitoring bearing vibration and motor current on a packaging line to predict bearing wear; using runtime, pressure, and energy trends to schedule HVAC compressor service before failures; and leveraging telematics and charge-cycle data to predict battery faults in light commercial fleets. Each example explains minimal data needs, simple success criteria, and how to make alerts actionable for technicians.

Boundaries and common pitfalls

This research emphasizes avoiding overhyped or poorly scoped pilots that create noise, false confidence, or alerts teams can't act on. It warns against relying on poor-quality data, skipping human oversight, or scaling before pilots prove actionable value. For safety- or compliance-critical equipment, the guidance recommends involving domain experts and following applicable regulations.

How this connects to Operational Excellence

Predictive-maintenance practices should fit into daily problem-solving and standard work: use pilot learnings to update maintenance standard work, feed insights into huddles and continuous-improvement cycles, and add successful patterns to your organizational memory so teams can repeat and scale what works.