This study explores the effectiveness of two machine learning models, namely
multilayer perceptron neural networks (MLP-NN) and adaptive neuro-fuzzy
inference systems (ANFIS), in advancing maintenance management based on engine
oil analysis. Data obtained from a Mercedes Benz 2628 diesel engine were
utilized to both train and assess the MLP-NN and ANFIS models. Six indices—Fe,
Pb, Al, Cr, Si, and PQ—were employed as inputs to predict and classify engine
conditions. Remarkably, both models exhibited high accuracy, achieving an
average precision of 94%. While the radial basis function (RBF) model, as
presented in a referenced article, surpassed ANFIS, this comparison underscored
the transformative potential of artificial intelligence (AI) tools in the realm
of maintenance management. Serving as a proof-of-concept for AI applications in
maintenance management, this study encourages industry stakeholders to explore
analogous methodologies.
Highlights
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Two machine learning models, multilayer perceptron neural
networks (MLP-NN) and adaptive neuro-fuzzy inference systems
(ANFIS), were employed to predict and classify the performance
condition of diesel engines.
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Among various training algorithms, Levenberg–Marquardt and the
Bayesian regularization demonstrated superior classification
accuracy, achieving a 95%–96% range.
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To assess the generalizability of MLP-NN and ANFIS, the training
set size was varied from 90% to 10%.
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The ANFIS model exhibited greater stability than MLP-NN, with a
50% higher performance.
