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 preconditioning

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 nonstationary

data is auto-regressive integrated moving average (ARIMA).