Slew bearing early damage detection based on multivariate state estimation technique and sequential probability ratio test

This paper presents the application of multivariate state estimation technique (MSET) and sequential probability ratio test (SPRT) for early damage detection of low speed slew bearing. This paper also investigates the appropriate and reliable features for slew bearing condition monitoring. It is found that largest Lyapunov exponent (LLE), approximate entropy, margin factor (MF) and impulse factor (IF) are able to monitor the slew bearing condition. The aim of present study is to calculate single condition monitoring parameter from multiple features. Combined MSET and SPRT were used to analyse the recorded reliable features obtained from a previous work. The result shows that the method can clearly picked up the sign of early bearing damage.