Machine learning approaches for improving condition-based maintenance of naval propulsion plants

Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment

Volumn 230, Issue 1, 2016, Pages 136-153

  Coraddu, Andrea ^a   Oneto, Luca ^a   Ghio, Aessandro ^a   Savio, Stefano ^a   Anguita, Davide ^b   Figari, Massimo ^a  

^a UNIVERSITY OF GENOA   (Italy)

^b UNIVERSITY OF GENOA   (Italy)

Author keywords

asset decay forecast; COmbined Diesel eLectric And Gas propulsion plant; Condition based maintenance; gas turbine; machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; BENCHMARKING; GAS TURBINES; GASES; LEARNING SYSTEMS; MAINTENANCE; PROPULSION; SHIP PROPULSION; SHIPBUILDING;

CONDITION BASED MAINTENANCE; ECONOMIC SUSTAINABILITY; HETEROGENEOUS SENSORS; MACHINE LEARNING APPROACHES; MACHINE LEARNING METHODS; MAINTENANCE TECHNOLOGIES; PROPULSION PLANTS; SHIP PROPULSION SYSTEM;

PREVENTIVE MAINTENANCE;

EID: 84953876479     PISSN: 14750902     EISSN: 20413084     Source Type: Journal    
DOI: 10.1177/1475090214540874     Document Type: Article

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