Outliers Identification From Instantaneous Angular Speed Signal Generated by Zebra Tapes

Instantaneous angular speed (IAS) measurement using zebra tape has been presented as an advantageous tool for rotating machinery surveillance. Unfortunately, both butt joint and mounting imperfection of the zebra tape result in stripes with different widths, which cause the outliers appear in the acquired signal. This paper proposed a solution to accurately identify the outliers and define the angular displacement values represented by outliers in the condition of periodic fluctuation of speed. The procedure is based on K-means clustering method and Fourier series model. Specially, a series of algorithms take K-means clustering partitioning method as core, and is used for outliers identification; the Fourier series fitting model is used to analyze the angular displacement values represented by outliers. Experimental study was performed on a test rig to validate the method under different measurement conditions. The result of error analysis shows the effectiveness of the algorithm.