Predictive Maintenance
Which equipment is about to fail, and what should be done first?
Turns telemetry and logs into early failure warnings, so repairs are planned before a breakdown rather than after. The sample dashboard monitors 12 vessels and 86 components, with 13 active predictive alerts and an estimated $340K of downtime avoided year to date.
- Failure probability by component
- Vibration and wear trends behind each alert
- Fleet-wide component register by signal
- Work orders, scheduling and procurement triggered from the alert
- Duration: 4–6 weeks for an outcome-based POC
How the AI works. Predictive models turn telemetry and logs into a failure probability for each component and show the trend that explains why an alert was raised. Every alert carries its next step: a work order, scheduling, procurement or escalation.
Failure probability is ranked by component, and a trend (here, turbocharger vibration over six weeks) shows why an alert was raised. A fleet-wide register filters by signal (overdue, approaching, OK, and signals missed) and attaches the next step: generate a work order, schedule maintenance, trigger procurement or escalate to the superintendent.