Blind source separation for vibration-based diagnostics of rotorcraft bearings

The vibration signals from sensors monitoring the activity of individual bearings in a power train unit may be linear instantaneous mixtures of vibrations generated by various dynamic components. Generally, an exact physical model describing the mixing process and the contribution of each dynamic component to the received sensor signal is not available. Vibration source signals from defective bearings often overlap in time and frequency, and, as such, the direct use of time- and frequency-domain methods may result in erroneous diagnostic information. This paper implements blind source separation (BSS) to demix sensor signals into correctly identifiable vibration source signals without the need of the vibration path property and sensor layout. Experimental vibration data from spalled, corroded, and healthy rotorcraft bearings are used with five representative BSS algorithms. The separation accuracy of these algorithms is then compared using various performance metrics. Results show that despite the inherent statistical dependence and near Gaussianity, it is possible to isolate vibration sources from mixed sensor signals using second- and higher-order statistics of the signals. The paper also identifies the limitations of the BSS technique and provides a remedy and recommendation for its implementation in rotorcraft bearing fault detection.