Operational Guide · Registers & Logbooks

# Predictive Maintenance based on IoT Sensors

IoT sensors mounted on engine components transmit vibration, temperature, pressure and oil quality data every few seconds: a paradigm shift from the calendar/running-hours-based Planned Maintenance System.

predictive maintenance IoT condition monitoring digital twin

## Operational Explanation

Unlike the traditional Planned Maintenance System, which schedules interventions based on calendar or running hours, predictive maintenance relies on IoT sensors that continuously monitor real machinery parameters (vibration, temperature, pressure, engine RPM, exhaust gas temperature, fuel flow, coolant flow, oil quality), transmitting readings every few seconds to detect deterioration patterns before they translate into a failure.

The marine engine monitoring systems market, valued at $1.1 billion in 2024, is projected to grow to $1.96 billion by 2034; according to Lloyd's Register, 70% of new ships delivered by 2030 will be equipped with AI-based maintenance platforms.

## Regulatory Reference

There is not yet a single binding IMO standard for predictive maintenance; adoption remains driven by Class initiatives (optional Classification Society notations for condition monitoring systems) and internal Company policies, in a regulatory context still evolving relative to the technology's maturity in the sector.

## Scope of Application

Engine rooms equipped with condition monitoring sensors on main engines, auxiliary generators and critical machinery, integrated with predictive analytics platforms on board or ashore.

## Procedure / How to Complete It

1. Identify the critical components best suited to predictive monitoring (main engine, generators, critical pumps) based on operational criticality and the cost of an unplanned failure.
2. Verify the calibration and operation of installed IoT sensors during periodic inspections, not only at the time of installation.
3. Integrate alerts generated by the predictive platform with the existing Planned Maintenance System, without entirely replacing calendar-based maintenance for components not yet monitored.
4. Verify alerts generated by the system with an expert human check before scheduling an intervention, to avoid unjustified false positives.
5. Document cases where a predictive alert correctly anticipated a failure, to calibrate operational confidence in the system over time.

## Practical Example

Example: the monitoring system detects a progressive increase in vibration on an auxiliary generator bearing, not yet detectable by calendar-based PMS checks; the Chief Engineer schedules a targeted inspection during the next operating window, anticipating a failure that would otherwise have manifested underway.

## Real Cases

Industry companies report concrete results from adopting predictive maintenance: a reduction in machinery downtime exceeding 20% attributed to AI-based maintenance alerts, and the monitoring of over 5,000 components through predictive analytics on selected fleet units, demonstrating that the advantage is not merely theoretical but already measurable at scale.

## Common Mistakes Mistake Library

Mistake | Consequence | How to avoid it
--- | --- | ---
IoT sensors installed but never checked for calibration during subsequent periodic inspections | Unreliable data generating false positives or, worse, false negatives | Include sensor calibration verification in the periodic inspection programme, not only at installation
Complete replacement of calendar-based PMS with predictive maintenance alone for components not yet adequately monitored | Loss of maintenance coverage on components lacking reliable sensors | Integrate predictive maintenance with the existing PMS, don't replace it entirely without verifying coverage
Predictive alerts automatically translated into scheduled interventions without expert human verification | Unnecessary interventions due to false positives, with avoidable operational cost and downtime | Always maintain an expert human check before translating an alert into a scheduled intervention

## PSC Observations

Predictive maintenance is not typically subject to direct PSC verification, but a well-documented condition monitoring system can support the demonstration of effective maintenance management in the event of a check on the general condition of critical machinery.

## Operational Tips

- Don't blindly trust system alerts: always maintain an expert check before translating them into a scheduled intervention.
- Integrate predictive maintenance with the existing PMS rather than replacing it, especially for components not yet covered by reliable sensors.
- Document cases where the system correctly anticipated a failure: this evidence builds the operational trust needed for effective adoption over time.

## Checklist

- Critical components identified for priority predictive monitoring
- Calibration of IoT sensors verified during periodic inspections
- Predictive alerts integrated with the existing PMS, not substitutive without verifying coverage
- Expert human check applied before every intervention generated by an alert
- Cases of correct failure anticipation documented to calibrate confidence in the system

## FAQ

How does predictive maintenance differ from the traditional Planned Maintenance System?
The traditional PMS schedules interventions based on calendar or running hours; predictive maintenance relies on real condition data continuously collected by IoT sensors, to intervene based on the actual state of the component.
How much has the marine engine monitoring systems market grown?
From a valuation of $1.1 billion in 2024, with growth projected to $1.96 billion by 2034, driven largely by predictive maintenance as the main application.
Does predictive maintenance completely replace the PMS?
No: it should be integrated with the existing PMS, especially for components not yet covered by reliable sensors, keeping calendar-based maintenance as a safety net.
🎬 Additional photos, videos and interactive diagrams for this topic will be available in a future version of the platform.

## Related Topics

Planned Maintenance System (PMS) and Auxiliary Machinery Cyber Risk Management of Automation Systems

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