Gear fault detection using acoustic emission spectrum kurtosis

Even though acoustic emission (AE) has been studied as a potential information source for machine fault diagnosis over many years, applications of AE sensors to gear fault diagnosis have been limited due to the lack of effective and efficient AE data analysis techniques. In this paper, a frequency reduction technique is proposed to preprocess the AE signals. Heterodyne technique commonly used in communication is first employed to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from several hundred kHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. After preprocessing, time synchronous average (TSA) is calculated and spectrum kurtosis (SK) is estimated for the calculated TSA signals as AE features for gear fault detection. An optimal band pass filter based on SK is designed to filter the signals and to extract features for fault detection. The presented method is validated using seeded gear fault tests on a notational split torque gearbox. The method presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.