Fourier and wavelet transform features for whirl tower diagnostics

This paper describes the application of signal processing methods to extract key features for detection and prediction of faults in rotating mechanical components of a whirl tower test facility. Procedures are described for processing the vibration signals from critical components of the whirl tower using the Fourier transform and the wavelet transform. The wavelet transform provides localization of signals in both time and frequency, revealing data that is averaged out in the Fourier analysis. The features extraction processes described are based on specific knowledge of the whirl tower equipment. The real-time analysis discussed allow for scheduling of inspection, repair, or replacement of failed and degraded components with minimal impact on production