| Abstrakt: |
The air pressure system (APS) is an integral component of Scania trucks and other heavy machinery. Because the brakes on these vehicles use air pressure, keeping the APS in good working order is crucial. Automakers can save money on repairs and boost vehicle efficiency with predictive maintenance. This can be done manually or using an automated system. Predictive maintenance that is performed manually requires human interaction and, as a result, introduces room for error. When humans are involved, there is always a chance that something may be missed or misunderstood, which might compromise the reliability of the maintenance procedures. Several benefits may be gained by employing automatic predictive maintenance strategies, such as artificial intelligence (AI), to investigate the underlying reasons for failure in the APS of Scania trucks. The company relies heavily on the dataset since it pinpoints the faulty parts. Predicting the root cause of failure is made more difficult if the dataset has missing values and unbalanced class issues. To overcome these issues, the data are preprocessed by many resampling techniques such as under-sampling, over-sampling, and the synthetic minority over-sampling technique (SMOTE), and imputation techniques such as KNNImputer and SimpleImputer for mean, mode, and constant strategies, multivariate imputation by chained equations (MICE), and principal component analysis (PCA), to balance the entire data set. After preprocessing, implementation of eight different machine learning algorithms, namely random forest, decision tree, gradient boosting, logistic regression, k-nearest neighbors classifier, AdaBoost classifier, CatBoost classifier, and XGB classifier, is carried out, and then the cost, accuracy metrics, and confusion matrices are analyzed. The results from the experimental analysis show that the XGB classifier is the best model, with accuracy of 99.6241% along with cost-effectiveness. [ABSTRACT FROM AUTHOR] |
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