Real-time data is transforming the maritime industry by enabling predictive maintenance and optimised navigation. By leveraging high-frequency data (HFD), vessel operators can detect and prevent machinery failures, improve operational efficiency, and enhance safety. This article explores how real-time monitoring supports predictive maintenance strategies and navigation, ultimately leading to cost savings and reduced emissions.
Real-Time Monitoring For Predictive Maintenance
Predictive maintenance is an advanced strategy that leverages machine learning algorithms, real-time asset monitoring, and data analytics to proactively identify potential issues in equipment or machinery. In shipping, vessel losses have been linked to machinery failure. One study that examined the leading causes of serious and total vessel losses (above 500 GT) between 2000 and 2014 found machinery failure to be a dominant trigger, accounting for 35% of total vessel losses in 14 years. [18] More recent statistics show that between 2014 and 2023, there were 11,506 incidents on vessels (100 GT and above) triggered by damage or failure of ship machinery. [19]
Transitioning from reactive to proactive maintenance can help to reduce the risk of incidents related to machinery failure. Early fault detection of a vessel’s critical machinery and equipment is necessary to minimise the risk of unscheduled downtime, but this relies on continuous monitoring of vessel sensors and the provision of real-time data. HFD is used to monitor equipment status to detect anomalies. Once an anomaly has been identified, the root cause can be uncovered by analysing the data and the system behaviour. This can be done with techniques such as Fault Tree Analysis (FTA), which starts with the outcome and shows the train of events that led to the incident. This is useful in understanding ship maintenance needs as incidents are often the result of a series of high-risk events culminating in complete system failure. [20]
These methods enable equipment faults to be classified based on type, severity, duration, and impact on performance. This is critical for timely detection and system performance optimisation. Moreover, it helps to prevent the incident from reoccurring. [21] Both LFD and HFD can provide historical trends and therefore help to detect developing faults. However, the low resolution of LFD means it is most effective when used in conjunction with more advanced analytics offered by HFD. Artificially intelligent (AI) algorithms can be deployed to further improve the accuracy of predictive maintenance. ML is used to better analyse data and understand faults in complex systems to ascertain their Remaining Useful Life. [22]
Hull performance is one example of how predictive maintenance based on real-time data can optimise vessel efficiency and fuel consumption. In extreme cases, a fouled hull can increase fuel consumption and carbon emissions by as much as 30%, according to maritime data collection and analytics experts at Danelec. [24]
Real-Time Monitoring For Navigation
Another benefit of real-time monitoring delivered by HFD is the support it can give to navigation. Better navigational decision-making enables more efficient and safer shipping. HFD-enabled real-time insights significantly improve Just-in-Time (JIT) arrivals. JIT helps vessels adjust speed to reach ports exactly when berths are available, reducing fuel use and port congestion. By capturing real-time sensor data on speed, draught, and engine power, ship operators can optimise the speed profile for various segments of a voyage. HFD-fueled, machine learning-based digital twins enable accurate voyage recommendations.
18 IUMI (2015) Casualty and World Fleet Statistics as at 01.01.2015. Data attributed to LLI, total losses as reported by Lloyds List
19 Allianz (2024) Safety and Shipping Review
20 Simion, D et al., (Sep, 2024) AI-driven predictive maintenance in modern maritime transport—enhancing operational efficiency and reliability
21 Simion, D et al., (Sep, 2024) AI-driven predictive maintenance in modern maritime transport—enhancing operational efficiency and reliability
22 Lee, J et al., (Jan 2014) Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications, Mechanical Systems and Signal Processing, volume 42, 1–2, p.314-334
23 UMAS and UCL (Dec, 2024) Port congestion, waiting times and operational efficiency
24 Danelec (Jan, 2023) Enhanced voyage profitability and sustainability
For further insight into the areas discussed in this article, download our latest thought leadership report, From Data To Action, created in partnership with Danelec.
