Diesel Engine Fault Prediction Using Artificial Intelligence Regression Methods

نویسندگان

چکیده

Predictive maintenance has been employed to reduce costs and production losses prevent any failure before it occurs. The framework proposed in this work performs diesel engine prognosis by evaluating the absolute value of severity using random forest (RF) multilayer perceptron (MLP) neural networks. A database was implemented with 3500 scenarios overcome problem inducing destructive failures engines. Diesel signals were developed zero-dimensional thermodynamic model inside a cylinder coupled crankshaft torsional vibration model. Artificial networks regression models for classifying quantifying failures. methodology applied alongside an simulator assess effectiveness accuracy. best-fitting performance obtained regressor RMSE 0.10 ± 0.03%.

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ژورنال

عنوان ژورنال: Machines

سال: 2023

ISSN: ['2075-1702']

DOI: https://doi.org/10.3390/machines11050530