Minimum entropy deconvolution enhanced by KLOF and phase editing for fault diagnosis of rotating machinery

Abstract

Repetitive impulses are critical characteristics in vibration signals of fault rotating machinery and minimum entropy deconvolution (MED) is an effective tool to extract these characteristics for fault diagnosis. However, early fault-induced impulses are usually weak and submerged in heavy background noise when rotating machinery operate in severe environment. Under this circumstance, MED generally fails to isolate useful impulses from contaminated signals. To overcome the shortcoming, this paper proposed a kernel local outlier factor MED enhanced by phase editing method (KPMED) for fault diagnosis of rotating machinery. First, non-Gaussian noise components in fault signals are removed by kernel-based local outlier factor (KLOF) in data cleaning way. Second, the residual Gaussian noise components are eliminated by phase editing (PE) in complex domain with phase perspective. Finally, the fault-induced repetitive periodic impulses are extracted by selecting MED filtered signal with large value of kurtosis for diagnosis. The effectiveness of KPMED is validated by a simulated bearing fault signal and two vibration signals collected from a pinion of a wind turbine and a rolling element bearing, respectively. Compared with original MED and its corresponding variants, KPMED is more accurate and suitable for fault diagnosis of rotating machinery under severe working conditions.