Digital Engine Rooms: Benefits of Shore-based Machinery Monitoring

Advanced engine health analysis leveraging digital technologies in the engine room is a critical capability that can drive significant operational and financial benefits across industries like aviation and shipping. By providing early detection of issues, quantitative health scoring, and predictive maintenance recommendations, these systems help maximise engine availability and performance while minimising unplanned downtime and maintenance costs. Digital engine health management solutions can help ship operators avoid costly breakdowns and unscheduled downtime, optimise engine performance, and reduce fuel consumption and emissions. Collaboration between ship operators, engine manufacturers, and service providers is key to unlocking the full potential of these technologies

 

1. Engine Health

As digital twins and remote sensing become more accessible in the industry, methodologies and frameworks will surge to monitor engine health efficiently. A framework for health analysis of ship engines can be established using digital twins. This involves setting up and calibrating the digital twin to correct engine performance measurements. By simulating both current and ideal operating conditions, the framework can assess engine health by analysing parameters like brake-specific fuel consumption (BSFC), exhaust gas temperatures (EGT), and indicated mean effective pressure (IMEP). This method identifies underperforming cylinders and potential thermal overloads, enabling precise life cycle cost assessments. The framework operates with minimal connectivity, relying on typical measurements, and can be adapted to various engine types and configurations by adjusting the thermodynamic model. Similar research-backed frameworks could allow ship owners and fleet operators to obtain quantifiable assessment reports of their engine’s health.

 

2. Real-Time Monitoring

Real-time monitoring of ship machinery is quickly becoming the industry standard. The global machine condition monitoring market is forecasted to grow from $3.1 billion in 2024 to $4.7 billion by 2029, with a CAGR of 8.7%. Vibration monitoring, which aids in early fault detection, is expected to lead the market share in 2021. Cloud-based monitoring solutions are projected to grow fastest due to their superior accessibility, scalability, and potential cost savings. The Asia Pacific region will see the fastest growth, driven by industrialization, increased predictive maintenance adoption, and expanding manufacturing activities. Key factors include rising fuel prices and emissions regulations like the IMO’s Carbon Intensity Indicator (CII), necessitating machinery performance optimization, the need to extend the life of ageing fleets through condition-based maintenance, and the growing complexity of modern ship systems, making real-time monitoring essential.

3. Regulatory Compliance

Centralizing machinery data on a cloud-based platform simplifies data collection and reporting for regulatory bodies. Automated reporting can generate the necessary documentation for compliance with EEDI, EEXI, and CII regulations, reducing administrative burdens on crew and shore-based management. Shore-based experts could then provide real-time monitoring and support, allowing faster troubleshooting and optimization of machinery performance without needing a dedicated chief engineer onboard. This would ensure the timely implementation of necessary software updates or configuration changes to adhere to regulatory requirements, facilitating continuous compliance. 

4. Predictive Maintenance

The global market for predictive digital twins in the maritime industry is expected to grow at a CAGR of 22.5% from 2022 to 2030. As sensor technologies and data analytics capabilities advance, we could see increasing adoption of digital twin-enabled predictive maintenance across the shipping industry. Machine learning models can be trained on the data from these digital twins’ sensor data to predict when components might fail. This allows maintenance to be scheduled proactively, rather than reactively.

 

5. Early Warning Systems

The development of Early Warning Systems for ship machinery requires data collection from sensors, preprocessing data to remove noise, and feature extraction.

Real application use-cases (backed by research):

a) use of Enhanced Domain Adversarial Neural Networks (DANN) for bearing lifespan prediction in propulsion shaft systems, significantly reducing prediction errors compared to other methods by aligning features across different operational conditions​.

b) combined CNN and Bidirectional Gated Recurrent Units (BiGRU) to monitor and predict exhaust gas temperatures in marine diesel engines, achieving real-time fault detection with a high degree of accuracy​.

            Actionable steps:

• data collection from sensors
• preprocessing data to remove noise
• feature extraction
• Implement CNNs and LSTM networks for pattern recognition and time-series prediction
• Train the models using historical fault data
• validate and test them to ensure accuracy
• deploy the system for real-time monitoring

(Disclaimer: The photos used are part of below articles/papers, and we do not claim ownership of the same. The images are used for purely informative purposes. We thank and appreciate the hard work of the original authors. )

References:

1. Liu, B., Gan, H., Chen, D., & Shu, Z. (2022). Research on fault early warning of marine diesel engines based on CNN-BiGRU. Journal of Marine Science and Engineering, 11(1), 56.
2. Ren, F., Du, J., & Chang, D. (2023). Research on the Bearing Lifespan Prediction Method for Ship Propulsion Shaft Systems Based on an Enhanced Domain Adversarial Neural Network. Journal of Marine Science and Engineering, 11(11), 2128.
3. Singh, R. R., Bhatti, G., Kalel, D., Vairavasundaram, I., & Alsaif, F. (2023). Building a digital twin-powered intelligent predictive maintenance system for industrial AC machines. Machines, 11(8), 796.
4. Tsitsilonis, K. M., Theotokatos, G., Patil, C., & Coraddu, A. (2023). Health assessment framework of marine engines enabled by digital twins. International Journal of Engine Research, 24(7), 3264-3281.