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Proceedings of the 10th International Conference on Rotor Dynamics – IFToMM, 2019
The objective of this work is the fault diagnosis in diesel engines to assist the predictive main... more The objective of this work is the fault diagnosis in diesel engines to assist the predictive maintenance, through the analysis of the variation of the pressure curves inside the cylinders and the torsional vibration response of the crankshaft. Hence a fault simulation model based on a zero-dimensional thermodynamic model was developed. The adopted feature vectors were chosen from the thermodynamic model and obtained from processing signals as pressure and temperature inside the cylinder, as well as, torsional vibration of the engines flywheel. These vectors are used as input of the machine learning technique in order to discriminate among several machine conditions, such as normal, pressure reduction in the intake manifold, compression ratio and amount of fuel injected reduction into the cylinders. The machine learning techniques for classification adopted in this work were the multilayer perceptron (MLP) and random forest (RF).
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Bookmarks Related papers MentionsView impact
Proceedings of the 10th International Conference on Rotor Dynamics – IFToMM, 2019
The objective of this work is the fault diagnosis in diesel engines to assist the predictive main... more The objective of this work is the fault diagnosis in diesel engines to assist the predictive maintenance, through the analysis of the variation of the pressure curves inside the cylinders and the torsional vibration response of the crankshaft. Hence a fault simulation model based on a zero-dimensional thermodynamic model was developed. The adopted feature vectors were chosen from the thermodynamic model and obtained from processing signals as pressure and temperature inside the cylinder, as well as, torsional vibration of the engines flywheel. These vectors are used as input of the machine learning technique in order to discriminate among several machine conditions, such as normal, pressure reduction in the intake manifold, compression ratio and amount of fuel injected reduction into the cylinders. The machine learning techniques for classification adopted in this work were the multilayer perceptron (MLP) and random forest (RF).
Bookmarks Related papers MentionsView impact