Application of Acoustic Signal Processing Techniques

Abstract

Industrial machinery produces complex acoustic emissions composed of overlapping tonal, broadband, and impulsive components. To diagnose mechanical conditions, predict faults, and guide noise control, one must go beyond simple SPL measurements and apply advanced processing. This work presents a comprehensive methodology combining acoustic calibration, real-time spectral acquisition (Spectra LAB), and post-processing in LabVIEW for waveform reconstruction, power spectrum estimation, autocorrelation analysis, and advanced spectral decomposition. We integrate modern techniques such as time–frequency clustering and model-based signal decomposition to separate overlapping sources under industrial noise conditions. Case studies on lathe, drilling, cigarette, and packaging machines illustrate how these tools uncover subtle acoustic signatures, support fault detection, and enable condition monitoring. The results confirm that properly calibrated, software-based DSP is a powerful, non-intrusive diagnostic method in noisy industrial settings.