Predictive Maintenance based on IoT Sensors
Predictive and condition-based maintenance are voluntary class- and Company-driven approaches; they replace time-based tasks only within an approved scheme with adequate data and sensors.
Operational Explanation
Predictive and condition-based maintenance are voluntary class- and Company-driven approaches; they replace time-based tasks only within an approved scheme with adequate data and sensors.
Regulatory Reference
Approved PMS/machinery survey arrangement, class notation, maker recommendations, ISM risk assessment and data governance.
Scope of Application
No market forecast or adoption percentage determines maintenance-scheme compliance.
Procedure / How to Complete It
- Define failure modes and supported decisions.
- Validate sensors, calibration, data quality and baselines.
- Integrate alerts into PMS with responsibility and escalation.
- Obtain approval before replacing time-based tasks.
- Measure performance with documented fleet evidence, not commercial claims.
Practical Example
An anomaly model flags a trend: crew verify sensor and machinery and follow an approved work order; the alert does not automatically cancel PMS.
What Typically Goes Wrong
Citing 70% of new ships, 2024/2034 market values, or presenting predictive maintenance as automatic replacement.
Common Mistakes Mistake Library
| Mistake | Consequence | How to Avoid It |
|---|---|---|
| Citing 70% of new ships, 2024/2034 market values, or presenting predictive maintenance as automatic replacement. | Non-compliant decision or treatment | Follow the approved arrangement and applicable sources |
What the PSCO Checks
Operational Tips
- Separate current requirement, class/maker, SMS and voluntary guidance.
- Do not improvise tests, thresholds or intervals.
- Record deviations and corrective action.
Preparation checklist
- Approved scheme
- Sensor/data validation
- Maker/class alignment
- Human review
- No unsupported market claims
FAQ
Related Topics
Last substantive revision of this page: 31 August 2026 · page fingerprint 0778d8dafc6b