Nowadays, naval engineers and shipping companies try to constantly minimise fixed and operational costs, as well as their impact on the maritime and generic environment. Amongst the main reasons that tend to significantly increase costs are unpredicted damages and/or mechanical failures, as well as fuel consumption. By providing a simple overview of the effect of such phenomena, one can report high costs for fuels and repairs / spare parts (especially when requested in as close to real-time as possible), loss of earnings affecting financial viability due to lack of fuel consumption strategy, immobilisation of vessels and/or due to failure to comply with Service Level Agreements (SLAs), or even port state control (PSC) detention. Indirect costs can be foreseen as well; loss of reputation being amongst the most important. The same (unwanted) phenomena can lead to environmental pollution, as leakages and increased emissions are a significant result of unpredicted damages and/or mechanical failures (although modern technologies have limited the chances of these types of side effects to a large extent).
Vessel fault prediction
Along these lines, in order to minimise such phenomena, contemporary ships and vessels in general, are equipped with a plethora of sensors and monitoring utilities, constantly collecting operational and performance data on every critical aspect of the ship’s operation (e.g. engines’ strain, emissions, fuel consumption, load), regardless of the nature of the ship (e.g. cargo ships, transport ships). However, although sensors are present, their full potential is not frequently exploited. Not all incoming data is stored, mainly due to space (memory) shortage, and maybe most importantly, the sensors’ data is not integrated or correlated with historical or external data streams.
Challenges: The previously described process has undeniably benefited the timely identification and prediction of upcoming damages or mechanical failures, as well as the establishment of a fuel consumption strategy. However, one core aspect remains unsolved: processes and systems like the previously described, are vessel-centric. All models, calculations and estimations are based solely on data coming from the vessel itself. Even if the utilized systems support real-time data collection, the process needs to take into account past events and entries from external sources in order to provide an early warning or verdict.
The question that remains unanswered is whether there is a way to embed additional relevant parameters into the process, for the investigation of the impact of the environmental conditions and the operational decisions taken on the vessel’s fuel consumption, also making proactive confrontation of damages or mechanical failures more effective and accurate; and thus providing important financial and environmental benefits.
Expected Benefits: Data from every available sensor will be collected and formulate a knowledge base that each owner and/or operator will exploit towards the effort of being proactive rather than reactive and operationally efficient. These data, after being cleaned and integrated, will feed a complex prediction model for fault/damage/failure prediction and a decision support tool for fuel consumption. The companies envision minimizing repairs and maintenance, as well as minimizing loss of earnings due to the vessels’ inability to operate and a more efficient fuel consumption strategy.. In addition, environmental impact is also envisioned to be reduced, since lower fuel consumption leads to greenhouse gas emissions reduction, fewer damages or mechanical failures during operation will result in practically eradicating the occurrence of unpredicted spills, emissions etc. Moreover, indirect benefits could include building a reputation around reliability and innovation.
