Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation

The health of mechanical components can be assessed by analyzing the vibration and acoustic signals they produce. These signals contain valuable information about the component’s condition, often encoded within specific frequency bands. However, extracting this information is challenging due to noise contamination from various sources. Narrow-band amplitude demodulation presents a robust technique for isolating fault-related information within the signal. This work proposes a novel approach based on cluster-based segmentation for demodulating the signal and extracting the frequency band of interest. The segmentation process leverages the criteria of maximum L-kurtosis and minimum entropy. L-kurtosis maximizes impulsiveness in the signal, while minimum entropy signifies a low degree of randomness and high cyclo-stationarity, and both characteristics are crucial for identifying the desired frequency band. Simulations and experimental tests using vibration signals from different gears demonstrate the effectiveness of this technique. The processed envelope of the signal exhibits distinct improvements, highlighting the ability to accurately extract the fault-related information embedded within the complex noise-ridden signals. This approach offers a promising solution for accurate and efficient fault diagnosis in mechanical systems, contributing to enhanced reliability and reduced downtime.