Advanced Signal Processing and Machine Learning for Water Pump Fault Diagnosis

This study explores advanced vibration signal analysis of water pumps under varying conditions, using an experimental platform and ADXL345 sensor for data collection and preprocessing. Frequency and time-domain features were subsequently extracted to enhance diagnostic accuracy. Frequency-domain features, including those derived from Fast Fourier Transform (FFT) and Power Spectral Density (PSD), were analyzed to identify high-contribution components. Simultaneously, time-domain features were refined using Empirical Mode Decomposition (EMD), selecting characteristics from specific Intrinsic Mode Functions (IMFs) tailored to different diagnostic requirements. The integration of time and frequency-domain features demonstrated superior discriminatory power compared to single-domain features. These advanced signal processing techniques, combined with Support Vector Machine (SVM) classification, significantly improved fault diagnosis accuracy and underscored the importance of feature fusion for robust water pump monitoring.