IConIC – Autonomous Engine Health Monitoring

Healthy engines save money. A small to medium-sized ship can typically save £100k a year in fuel and 580 tonnes of CO2 simply by running at maximum efficiency. Whilst unexpected engine faults can cause catastrophic failure, resulting in unplanned downtime and repairs that can cost in excess of £350k.

However, keeping them at optimum health is not easy. Even the very best condition monitoring systems lack sufficient granularity of detail, fail to give early enough indications and rarely adapt to the specific engine and its environment. After five year’s research and development, we now offer a solution that USES Artificial Intelligence to accurately monitor the health of any engine in real time and predict future faults long before any other form of health monitoring

IConIC is a new and revolutionary approach to engine health monitoring that enables maritime systems to be operated and maintained at optimum levels, reducing emissions and fuel consumption, and avoiding expensive downtime caused by unexpected engine faults.

System Overview

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IConIC learns the ‘signature’ of what a healthy system looks like in a specific environment, with the potential to pinpoint specific faults depending on how the signature changes.

IConIC’s health score is based on analysing how close the system is to a healthy/unhealthy boundary, generated during a brief training phase. A score of zero represents this boundary, with a positive score indicating good health, and a negative score indicating poor health.

This can then be portrayed visually to engineers so they can quickly determine the health of specific cylinders, pumps or drive-trains.

The IConIC Advantage

IConIC resolves the three key issues faced by traditional condition monitoring systems


By using sophisticated signal processing and advanced machine learning, IConIC provides significantly earlier indication of impending fault conditions than any other detection method.

With high-frequency sampling on each cylinder, IConIC provides more granularity, allowing for deeper fault diagnosis and analysis.

Rather than using equipment manufacturers published information, IConIC develops its baseline health data from the actual engine it is monitoring, taking into account its age and local environment.

Traditional CBM IConIC Advantages
Sensors Existing sensor suite (low number of measurement points) Externally-mounted vibration sensor on each cylinder Easier to fit, with greater depth of analysis possible
Sampled features Online: 1-2 per second
Hand-held: 4 per year
> 500 per second Extends analysis even further by capturing vibration harmonics
Analysis technique Provides an alert once a measured parameter has been exceeded Self-learning Artificial Intelligence, based on specific engine and vessel Learns about the system and then reduces crew burden
Processing speed Analysis of results offline, with reports taking several days to become available Real-time instant notifications of changes in health Immediate action from live information and constant performance prediction
Transmission overhead High dependency on cloud computing and shore-side processing Low, with Edge computing allowing all processing to occur on-board Reduces need to transmit large amounts of data

Supporting all vessel classes

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IConIC has been installed on a wide range of engines and vessel classes, and has detected various faults that have affected engine health.

Typical faults detected by IConIC include:
• Fuel flow
• Blocked air filters
• Loose engine mounts
• Worn piston rings
• Big end bearing slack
• Leaking valves
• Injector faults
• Maladjusted cam followers

Key Features

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    Simple to implement

    Non-invasive, easy to install sensors, with compact, powerful processing units installed on board, rather than relying on remote processing

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    Delivers performance insight in real time to both ship’s crew and shore-side operations

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    Informs system-wide maintenance

    Combines intelligence from across platforms, enabling fleet-wide optimisation

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    Identifies failures earlier than any other platform-based techniques

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    Measures system health

    Quantifies the system’s health in its particular environment, rather than just a fault alarm (on/off)

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    Complements maintenance operations

    Delivers visual diagnostics to the crew via portable devices

Contact us to find out more

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