首页|Studies Conducted at Politecnico di Bari on Machine Learning Recently Published (A Machine Learning Framework for Condition-Based Maintenance of Marine Diesel E ngines: A Case Study)
Studies Conducted at Politecnico di Bari on Machine Learning Recently Published (A Machine Learning Framework for Condition-Based Maintenance of Marine Diesel E ngines: A Case Study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsFresh data on artificial intelligence are present ed in a new report. According to news originating from Bari, Italy, by NewsRx co rrespondents, research stated, "The development of artificial intelligence-based tools is having a big impact on industry. In this context, the maintenance oper ations of important assets and industrial resources are changing, both from a th eoretical and a practical perspective." Financial supporters for this research include European Union-nextgenerationeu. Our news reporters obtained a quote from the research from Politecnico di Bari: "Namely, conventional maintenance reacts to faults and breakdowns as they occur or schedules the necessary inspections of systems and their parts at fixed times by using statistics on component failures, but this can be improved by a predic tive maintenance based on the real component's health status, which is inspected by appropriate sensors. In this way, maintenance time and costs are saved. Impr ovements can be achieved even in the marine industry, in which complex ship prop ulsion systems are produced for operation in many different scenarios. In more d etail, data-driven models, through machine learning (ML) algorithms, generate th e expected values of monitored variables for comparison with real measurements o n the asset, for a diagnosis based on the difference between expectations and ob servations. The first step towards realization of predictive maintenance is choo sing the ML algorithm. This selection is often not the consequence of an in-dept h analysis of the different algorithms available in the literature. For that rea son, here the authors propose a framework to support an initial implementation s tage of predictive maintenance based on a benchmarking of the most suitable ML a lgorithms. The comparison is tested to predict failures of the oil circuit in a diesel marine engine as a case study."
Politecnico di BariBariItalyEuropeAlgorithmsCyborgsEmerging TechnologiesMachine Learning
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