Improved Monitoring and Classification of Engine Oil Condition through Two Machine Learning Techniques

<div>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.</div> <section> <h2>Highlights</h2> <div> <ul> <li> <div>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.</div> </li> <li> <div>Among various training algorithms, Levenberg–Marquardt and the Bayesian regularization demonstrated superior classification accuracy, achieving a 95%–96% range.</div> </li> <li> <div>To assess the generalizability of MLP-NN and ANFIS, the training set size was varied from 90% to 10%.</div> </li> <li> <div>The ANFIS model exhibited greater stability than MLP-NN, with a 50% higher performance.</div> </li> </ul> </div> </section> <section> <h2>Graphical Abstract</h2> <div> <img/> </div> </section>