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Description
Propulsion Analytics is a producer of simulation models and machine learning for vessel/engine performance evaluation and fault diagnosis based in Greece.
The current level of condition monitoring within shipping is very variable and depends on ship type, age and components. In most cases, the processing of monitored data is carried out on the ships themselves. The difficulty of running such complex models and routines in the field, onboard and ashore has prompted Propulsion Analytics to develop the project CHEQUEMATE, that focuses on the diagnostics and prognostics core of vessel performance management. It consists in developing and implementing a novel Condition Based Maintenance (CBM) service, combining thermodynamic modelling and artificial intelligence for diagnosis of marine machinery system faults and prognosis of effective operation of marine engines.
A system for remote diagnostics and predictive maintenance in the marine industry may result in reduction of use for on-call service engineers and reduction in maintenance, through extended maintenance periods, and in associated costs. At the same time, increasing the safety for ship works dedicated to maintenance, and improve the reliability of the service.
Propulsion Analytics will partner with the Classification Society, DNV. They will develop a methodology for model verification and validation of the CBM methods. They will also be an advisor on matters concerning market and reliability issues and Remaining Useful Life assessment – calculating the amount of time a machine can operate before it requires maintenance or needs to be replaced.
Apart from the development of a new solution, Chequemate will lead to the creation of 2 new jobs and increased competitiveness of the company.
Summary of project results
The current level of condition monitoring within shipping is very variable. In most cases, the processing of monitored data is carried out on the ships themselves. The difficulty of running such complex models and routines in the field, onboard and ashore has prompted Propulsion Analytics to develop the project CHEQUEMATE, that focuses on the diagnostics and prognostics core of vessel performance management.
Less than 2% of the world fleet employ a CBM arrangement, but in the next 10 years, 10% of the world’s fleet will use performance optimisation tools and CBM methods. Ship performance evaluation, incorporating efficiency optimization and improved maintenance scheduling result in significant cost reductions for ship owners and operators.
Through the project, an innovative Condition Based Maintenance (CBM) application for ships was developed, with validated methods and accredited tools that merge thermodynamic modelling with artificial intelligence to diagnose machinery system faults and predict performance in marine powerplants. This methodology can diagnose and predict engine component malfunctions or any decreasing performance.
The Greek Partner, DNV, developed a verification, validation and accreditation (VV&A) methodology to assess CBM applications. This methodology will significantly enhance the validity and trustworthiness of CBM approaches.
CBM permits a safer, more reliable and competitive operation of maritime assets. Specifically, it looks at the marine engine of vessels, resulting in safer seagoing journeys.
With DNV''s digital validation and verification methodology, confidence in the use of CBM for critical equipment was built, thus accelerating their deployment in shipping.