Comparative study of kurtosis and L-kurtosis for bearing fault classification in induction motors

This study investigates the effectiveness of L-kurtosis as a robust alternative to traditional kurtosis for identifying and categorizing rolling bearing faults in vibration signals. By comparing L-kurtosis-energy and kurtosis-energy features derived from wavelet packet decomposition (WPD) coefficients; this research evaluates their performance using a multi-layer perceptron neural network (MLP-NN). Experimental data encompassing various rotating speeds, fault types, and severities were utilized to train and test the MLP-NN on both healthy and defective bearing conditions. The results demonstrate that while kurtosis-energy achieved 95.63% accuracy in defect classification, replacing kurtosis with L-kurtosis significantly enhanced accuracy to 99.92%. This improvement underscores the resilience of L-kurtosis to outliers and its ability to handle non-normally distributed vibration signals effectively. The findings affirm the potential of L-kurtosis-energy features to improve fault detection methodologies, making them more reliable for industrial applications. This study highlights the importance of robust diagnostic tools for advancing predictive maintenance strategies and ensuring operational reliability.