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All Journal JMES The International Journal of Mechanical Engineering and Sciences
Nurdin, Fadli
Institut Teknologi Sepuluh Nopember
Energy Materials Science & Nanotechnology Mechanical Engineering

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Intelligent Fault Prediction in Diesel Engines: A Comparative Study of SVM and BPNN for Condition-Based Maintenance Nurdin, Fadli; Effendi, Mohammad Khoirul; Mohakul, D
JMES The International Journal of Mechanical Engineering and Sciences Vol 9, No 2 (2025)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v9i2.22724

Abstract

This study discusses the application of Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN) in predicting diesel engine health based on operational data that has been relabeled using K-Means Clustering. Two types of SVM kernels were tested, namely Radial Basis Function (RBF) and Sigmoid, with various parameter combinations. The results indicate that SVM with a Sigmoid kernel achieved an accuracy of 94.06% but was less sensitive in detecting unhealthy engine conditions. In comparison, the BPNN method with a three-hidden-layer configuration (1-2-1 neurons) and the tansig activation function demonstrated superior performance, achieving an accuracy of 97.13%, MSE of 0.03, recall of 94%, precision of 100%, and an F1-score of 97%. These results confirm that BPNN outperforms SVM in capturing complex data patterns and is more accurate in detecting unhealthy engine conditions. Furthermore, dataset relabeling significantly improved prediction accuracy from 72.3% to 97.13%, emphasizing the importance of data balance in modeling. Overall, this study demonstrates that BPNN with an optimal configuration is more effective in predicting diesel engine health than SVM, making it a more reliable approach for engine condition monitoring.Keywords: Diesel Engine; Machine Health Prediction; Support Vector Machine; Backpropagation Neural Network; Condition-Based Maintenance; Artificial Intelligence
Co-Authors Mohakul, D Mohammad Khoirul Effendi