Condition-Based Maintenance for medium speed diesel engines used in vessels in operation

Abstract

Condition-Based Maintenance for diesel engines has contributed to reliability, energy-efficiency, and cost reduction. Both, the modelling of engine performance and fault detection require large amounts of data; usually, these are obtained on a test bench. In contrast, in operative engines, provoking faults onboard is not a viable proposition. Condition-Based Maintenance, fault detection and diagnosis need to be solved on engines installed in commercial vessels: the present contribution answers this need. A medium-speed diesel engine was monitored using thermocouples, pressure sensors, a propeller shaft torque meter and fuel oil flow-meters, during more than 10,000 running hours. Monitored data were used to train a three-layer feed-forward neural network, to generate the engine performance model; thus, determine the engine's fuel consumption and faulty conditions. The faulty conditions considered were: (1) a polluted turbine; (2) a dirty air filter/compressor; (3) a dirty air cooler; (4) and bad fuel injection, i.e. bad combustion. The sensor's precision and the experience gained by monitoring the engine served as a baseline to define the fault threshold values. The results proved the feasibility of installing a Condition-Based Maintenance, for vessels in operation, by monitoring engine performance and analysing the data with the aid of artificial neural networks. (C) 2015 Elsevier Ltd. All rights reserved.