Abstract

Acoustic Signal Analysis is a cutting-edge technique in industrial automation that leverages sound and vibration patterns to monitor and evaluate machine performance. Each mechanical component emits a distinct acoustic signature during normal operation. Deviations from this signature often indicate faults such as bearing wear, misalignment, or fluid leaks. By capturing these signals and applying Digital Signal Processing techniques—such as Fast Fourier Transform and spectrogram analysis—industries can detect anomalies at an early stage. This non-invasive approach supports predictive maintenance, minimizes downtime, and enhances overall system efficiency. To operationalize this analysis, Python-based signal processing frameworks are employed. Waveform Visualization is a Time-domain plots of machine sound. FFT Analysis is a Frequency-domain transformation to identify dominant frequencies. Spectrogram Generation is a Time-frequency mapping to visualize evolving acoustic patterns. Filtering and Feature Extraction are Isolating relevant frequency bands for fault classification.