Predictive Maintenance

Introduction: Predictive Maintenance

Predictive Maintenance uses data to estimate when an asset will need service. The aim is to fix equipment just in time, not too early and not after a failure. It reduces unplanned downtime, improves safety, and lowers total maintenance cost.

Background

Traditional maintenance was reactive or time based. Teams repaired after breakdowns or on fixed intervals. Sensors, cheap computing, and machine learning made a new path possible. By monitoring condition and spotting patterns that precede failure, organisations can plan service at the right moment and avoid waste.

Key Elements/Features

• Critical assets. Select assets where failure is costly or dangerous.
• Data sources. Vibration, temperature, pressure, current, sound, oil analysis, control logs, and operator notes.
• Data pipeline. Edge collection, storage, quality checks, and features such as RMS, kurtosis, and trend rates.
• Models. Rules and thresholds, anomaly detection, classification, and Remaining Useful Life estimation.
• Failure modes. Link signals to known failure mechanisms using FMEA and history.
• Workflow. Alerts routed into the CMMS with priority, spare parts, and planned downtime.
• Governance. Versioned models, feedback loops, and validation against real outcomes.
• Metrics. Downtime avoided, forecast accuracy, lead time to act, maintenance cost, and OEE.

Applications/Examples

• Rotating equipment. Bearings and gearboxes monitored with vibration and temperature.
• Utilities and energy. Transformers, pumps, and wind turbines tracked for early warning.
• Fleets. Vehicle health scored from telematics and engine diagnostics.
• Buildings. HVAC units monitored for coil fouling and compressor wear.
• Process lines. Early detection of sensor drift that harms quality.

Relevance/Impact

Predictive Maintenance improves reliability with fewer scheduled stops. It supports lean flow and stable quality. Benefits depend on good data and tight integration with planning. Start with a small set of high value assets. Prove value, then scale. Combine models with expert judgment, and update thresholds as conditions change.

See also

• Preventive Maintenance
• Total Productive Maintenance
• Downtime
• OEE