Vibration Fault Diagnostics for Quality Control and Component Reuse

Quality control of household appliances can be improved by replacing human inspection with automated analysis of vibration signals. This book presents vibration methods for diagnosing and prognosing mechanical faults in electric motors and washing machines. This includes pre-processing of raw vibration data for reducing measurement noise, such as speckle noise occurring when optically rough surfaces are measured by Laser Doppler Vibrometer (LDV). New algorithms for speckle noise reduction are presented, including kurtosis ratio and optical level thresholding. Application to real LDV data (measured on production line by AEA srl, Loccioni) shows that these algorithms improve detectability of mechanical faults by enhancing envelope and order spectra. The book gives practical examples how to successfully combine classical methods (cepstrum, CPB spectrum, order tracking), as well as recent algorithms based on wavelet transform. The book presents findings of accelerated lifetime tests suggesting that motors and gearboxes possess a great potential for reuse. Proposed methods can be integrated into remaining life estimation methodologies.