Gear fault detection using time synchronous average based acoustic emission condition indicators

Acoustic emission (AE) has been studied as a potential information source for machine fault diagnosis for a long time. However, AE based techniques have not yet been applied widely in real health and usage monitoring system (HUMS) applications because of the difficulties and costs associated with AE data analysis. First of all, in comparison with other sensing techniques such as vibration, AE techniques require much higher sampling rate. The characteristic frequency of AE signals generally falls into the range of 100 kHz to several MHz, which requires a sampling rate at least 5MHz for effective AE data acquisition. Second, the storage and computational burden for large volume of AE data is tremendous. Third, AE signals generally contain certain non-stationary behaviors which make traditional frequency analysis ineffective. In this paper, to overcome the challenges in applying AE techniques for effective HUMS application, a frequency reduction technique and a time synchronous average (TSA) based AE signal processing method is developed for gear fault diagnosis. A heterodyne technique is employed to preprocess the AE signals before sampling. Using the heterodyne, the AE signal frequency is down shifted from several hundred kHz to below 50 kHz. Thus a low sampling rate comparable to that of vibration sensors is applied to sample the AE signals. Then, a TSA method is adopted to further analyze the AE signals and extract AE features. Different condition indicators are calculated on the TSA signals. The proposed methods are tested on a notational split torque gearbox. Based on the computed condition indicators, two types of seeded gear faults can be separated and detected. Copyright