Craig Moodie

An application of nonlinear feature extraction - A case study for low speed slewing bearing condition monitoring and prognosis

This paper presents the application of four nonlinear methods of feature extraction in slewing bearing condition monitoring and prognosis: these are largest Lyapunov exponent, fractal dimension, correlation dimension, and approximate entropy methods. Although correlation dimension and approximate entropy methods have been used previously, the largest Lyapunov exponent and fractal dimension methods have not been used in vibration condition monitoring to date. The vibration data of the laboratory slewing bearing test-rig run at 1 rpm was acquired daily from February to August 2007 (138 days). As time progressed, a more accurate observation of the alteration of bearing condition from normal to faulty was obtained using nonlinear features extraction. These findings suggest that these methods provide superior descriptive information about bearing condition than time-domain features extraction, such as root mean square (RMS), variance, skewness and kurtosis.

Degradation Trend Estimation and Prognosis of Large Low Speed Slewing Bearing Lifetime

In many applications, degradation of bearing conditions is usually monitored by changes in time-domain features. However, in low speed (< 10 rpm) slewing bearing, these changes are not easily detected because of the low energy and low frequency of the vibration. To overcome this problem, a combined low pass filter (LPF) and adaptive line enhancer (ALE) signal pre-conditioning method is used. Time-domain features such as root mean square (RMS), skewness and kurtosis are extracted from the output signal of the combined LPF and ALE method. The extracted features show accurate information about the incipient of fault as compared to extracted features from the original vibration signal. This information then triggers the prognostic algorithm to predict the remaining lifetime of the bearing. The algorithm used to determine the trend of the non-stationary data is auto-regressive integrated moving average (ARIMA).