Abstract
This paper presents an application of multivariate state estimation technique (MSET), sequential probability ratio test (SPRT) and kernel regression for low speed slew bearing condition monitoring and prognosis. The method is applied in two steps. Step (1) is the detection of the incipient slew bearing defect. In this step, combined MSET and SPRT is used with circular-domain kurtosis, time-domain kurtosis, wavelet decomposition (WD) kurtosis, empirical mode decomposition (EMD) kurtosis and the largest Lyapunov exponent (LLE) feature. Step (2) is the prediction of the selected features’ trends and the estimation of the remaining useful life (RUL) of the slew bearing. In this step, kernel regression is used with time-domain kurtosis, WD kurtosis and the LLE feature. The application of the method is demonstrated with laboratory slew bearing acceleration data.
Highlights
Steelmaking industry has many critical processes which rely on low rotating slew bearings.These bearings are often used in harsh conditions and have high replacement cost with long delivery lead time
Prognosis method has not been applied in the study of low rotational speed slew bearing [4,10,11]. This paper present both condition monitoring and prognosis method
Kernel regression is used to predict the trend of the timedomain kurtosis, wavelet decomposition (WD)
Summary
Steelmaking industry has many critical processes which rely on low rotating slew bearings. MSETthe and SPRT used to the RUL of the slew bearing In this step, kernel regression is used to predict the trend of the timedomain kurtosis, WD kurtosis and the LLE features. In this step, kernel regression is used to predict the trend of the time-domain kurtosis, method.
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