Health monitoring of automotive clutch system through feature fusion and application of tree-based classifiers

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Clutch systems are crucial components of automotive systems, essential for transferring engine power to the wheels through gear shifts. A failed clutch can halt gear shifts and power transmission, rendering a vehicle immobile. Effective fault diagnosis is vital for ensuring reliability, preventing breakdowns, and addressing root causes promptly. Consequently, condition monitoring of the clutch is essential for maintaining system reliability and minimizing unwanted breakdowns. This paper presents an innovative condition-monitoring technique derived from vibration analysis to detect faults in the clutch system. The study involves extracting statistical, histogram, and autoregressive moving average features from acquired vibration signals. The J48 decision tree algorithm is utilized to select the most significant features, which are then classified using tree-based classifiers across three load conditions (no load, 5 kg, and 10 kg) and six clutch conditions. The experimental results demonstrate that the feature fusion strategy significantly enhances classification accuracy. The obtained results state that the classification accuracies for no load, 5 kg load, and 10 kg load were 98.33%, 100.00%, and 99.16%, respectively, while applying feature fusion strategies. This study highlights the effectiveness of feature fusion in improving fault diagnosis accuracy for clutch systems, presenting a robust method for real-time condition monitoring in automotive applications.