Predictive Maintenance

Equipment failures triggered unplanned stoppages, rushed repairs, and schedule changes. Calendar-based maintenance helped but did not reflect each asset's actual condition.

• Teams often repaired equipment only after it failed.
• Unexpected downtime disrupted production schedules.
• Fixed schedules replaced some parts before they were needed.
• Other failures still occurred between planned service visits.

Sensors already captured vibration, temperature, and other condition signals. Analyzing those signals could identify emerging risk and shift maintenance from a fixed calendar to equipment condition.

• Monitor asset signals continuously for early warning patterns.
• Prioritize service based on observed equipment condition.
• Reduce unnecessary scheduled work where condition is healthy.
• Give maintenance teams time to plan before failure occurs.

We built a predictive model that analyzes incoming sensor data and creates maintenance recommendations for at-risk assets. Recommendations feed into the existing CMMS as work orders, with thresholds tuned alongside the maintenance team.

• Analyze vibration, temperature, and other asset signals.
• Detect patterns associated with rising failure risk.
• Create work orders through the existing CMMS integration.
• Tune alert thresholds to balance coverage and alert volume.