Gear fault diagnosis under non-stationary operating mode based on EMD, TKEO, and Shock Detector

Abstract

Condition monitoring of gearboxes running under non-stationary operating conditions is a very difficult task. In this study, a signal processing technique is developed for damage detection of a bevel gearbox running under variable load and speed conditions. The proposed technique is applied on simulated vibration data computed through a dynamic model of bevel gearbox. The procedure used in this technique is based on the extraction of the shock related to the defect using the Shock Detector (SD) method. Firstly, vibration signals are decomposed into IMFs using Empirical Mode Decomposition (EMD). Then, the Teager–Kaiser Energy Operator (TKEO) is used to assess the instantaneous energy of the signal. Afterwards, SD is applied to examine and quantify the shock contents of the TKEO signal, which reflect the effect of the defect.