Toothwise Fault Identification for a Planetary Gearbox Based on a Health Data Map

Abstract

Vibration-based fault diagnosis of a planetary gearbox is challenging due to the revolving planet gears inducing modulation of vibration signals. For accurate fault diagnosis, researchers have suggested that vibration signals should be extracted by window function when the planet gears are positioned under the sensor. However, vibration modulation characteristics can be affected by operational and geometrical uncertainties. Thus, fault-related features can be inadvertently discarded when the window function is employed. Alternatively, periodicity analysis of anomalies can be employed with the entire vibration signal without the use of window function. However, it is challenging in a planetary gearbox because the fault-related features are also modulated. This paper proposes a health data map for toothwise fault identification in a planetary gearbox. In the proposed approach, samplewise health data are aligned in the domains of a pair of gear teeth of the planetary gearbox. The proposed approach represents the health state of every pair of gear teeth, while making it possible to isolate the location of any faulty teeth in the gears. For demonstration of the proposed method, two case studies are presented: an analytical model and a 2-kW testbed. The results suggest that the proposed method performs well even under unexpected vibration modulation characteristics.