A large motor fleet cannot be managed effectively by reacting to every individual alarm. A fleet programme needs consistent asset identity, comparable condition metrics, operating context and a prioritisation method that directs maintenance effort to the machines with the highest risk and consequence.
Standardise the data model
Capture device code, hardware and firmware versions, manufacturing date and serial number with each asset record. Store motor rating, speed, duty, bearing details, mounting position and baseline state. Consistent metadata is essential when comparing machines across lines or sites.
Prioritise with multiple indicators
- Use per-axis severity and fault state as the first screening layer.
- Review trend rate, harmonics, temperature and advanced indices before escalation.
- Use RUL, failure probability and degradation index as planning aids, not guaranteed dates.
- Weight the condition result by production, safety and spare-parts consequence.
Plan interventions
Group work by shutdown opportunity, required skill and spare availability. A stable warning may justify planned inspection, while a rapidly increasing trend or combined thermal and vibration alarm may require accelerated action.
Measure programme effectiveness
Track avoided failures, false alarms, inspection findings, lead time, maintenance cost and post-repair condition. Feedback from completed work should refine thresholds and improve confidence in the monitoring programme.
Technical basis: PyXis Smart 3-Axis Vibration Sensor datasheet TTN-DS-2026-002, issued 19 July 2026. Confirm the current hardware, firmware and controlled document revision before implementation.
