Operational Guide · Registers & Logbooks

# Digital Twin for the Engine Room

The maritime digital twin market is estimated to grow from $0.59 to $2.40 billion by 2032: Classification Societies foresee the first genuine operational digital twins on complex ships between 2025 and 2030.

digital twin engine room digital replica simulation

## Operational Explanation

A digital twin of the engine room is a virtual replica of the engine and its auxiliary systems, continuously fed by data from real sensors (the same ones used for IoT predictive maintenance), but which goes beyond simple trend monitoring: it allows operational scenarios to be simulated, the residual life of components to be estimated through physical/hybrid models, and the impact of an operational change to be virtually tested before applying it to the real ship.

The leading Classification Societies expect genuine operational digital twins on complex assets such as ships to appear concretely between 2025 and 2030, and already offer frameworks for assessing the suitability of a digital twin for specific onboard systems. A cross-industry collaboration project launched in 2026, involving among others major Japanese shipping companies, aims to create a secure data-sharing framework between shipyards and owners to accelerate the technology's adoption.

## Regulatory Reference

There is no IMO requirement mandating adoption of the digital twin; Classification Societies are developing suitability assessment frameworks (optional notations/dedicated guidelines, such as the 2025 CIMAC guidance document on digital twins in the maritime industry) for owners intending to adopt the technology on a voluntary basis.

## Scope of Application

Every ship with a sufficiently extensive sensor infrastructure (already used for IoT predictive maintenance) for which the owner is evaluating adoption of a digital engine room simulation platform.

## Procedure / How to Complete It

1. Verify the maturity of the existing sensor infrastructure as a prerequisite, since the digital twin is built on top of the same data used for predictive maintenance.
2. Evaluate the applicable suitability framework with the Classification Society before investing in a specific digital twin platform.
3. Clearly define the operational scenarios the digital twin will need to simulate (e.g. impact of an engine load change, residual life estimate of a critical component).
4. Establish a secure data-sharing agreement with the platform supplier and, where relevant, with the building shipyard.
5. Periodically validate the digital twin's predictions by comparing them with real maintenance data, to progressively calibrate the model's accuracy.

## Practical Example

Example: an owner with an already mature IoT predictive maintenance platform on a ship evaluates extending it to a digital twin of the main engine, to simulate the impact of prolonged low-load operation on injector residual life before deciding on a commercial route change.

## Real Cases

Classification Societies acknowledge that, although the maritime digital twin market is growing strongly, genuine operational digital twins on complex assets such as ships are still maturing: today the technology is predominantly focused on applications for monitoring engine condition, fuel consumption and structural performance, rather than on complete and validated predictive simulations.

## Common Mistakes Mistake Library

Mistake | Consequence | How to avoid it
--- | --- | ---
Investment in a digital twin platform without a sufficiently mature sensor infrastructure as a data foundation | Simulation model fed by insufficient or low-quality data, with unreliable predictions | Always verify the maturity of the existing sensor infrastructure before investing in a digital twin
Data-sharing agreement with the platform supplier not clearly defined in terms of security and data ownership | Risk of exposing sensitive operational data to third parties | Always establish a clear and secure data-sharing agreement before implementation
Digital twin predictions not periodically validated against real maintenance data | Progressive loss of model accuracy without anyone noticing | Periodically validate the model's predictions by comparing them with real data

## PSC Observations

The digital twin is not subject to direct PSC verification at this stage of industry adoption, being a voluntary technology not yet governed by a specific statutory requirement.

## Operational Tips

- Don't consider the digital twin a substitute for existing IoT predictive maintenance: it is built on top of it, not a replacement for it.
- Always evaluate the Classification Society's suitability framework before choosing a specific platform.
- Progressively calibrate the model by comparing its predictions with real data, don't trust initial predictions without validation.

## Checklist

- Existing sensor infrastructure verified as sufficiently mature
- Classification Society suitability framework consulted before investment
- Operational scenarios to be simulated clearly defined
- Secure data-sharing agreement established with the platform supplier
- Model predictions periodically validated against real maintenance data

## FAQ

What is the difference between a digital twin and IoT predictive maintenance?
IoT predictive maintenance monitors real parameters and analyzes their trend; the digital twin goes further, building a virtual replica of the system that allows operational scenarios to be simulated and component residual life to be estimated before the problem materializes for real.
Are digital twins already a mature technology in the maritime sector?
No: Classification Societies expect genuine operational digital twins on complex assets such as ships to appear concretely between 2025 and 2030; today the technology is predominantly at the development and suitability assessment stage.
Is an IMO regulatory requirement needed to adopt a digital twin on board?
No, adoption is on a voluntary basis; Classification Societies offer suitability assessment frameworks, but there is no dedicated IMO statutory obligation at this stage.
🎬 Additional photos, videos and interactive diagrams for this topic will be available in a future version of the platform.

## Related Topics

Predictive Maintenance based on IoT Sensors Operational Energy Efficiency in the Engine Room

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