Extensive prior studies aimed at the development of diagnostic methods for planetary gearboxes have mainly examined acceleration and acoustic emission signals. Recently, due to the relationship between gear mesh stiffness and transmission error (TE), TE-based techniques have emerged as a promising way to analyze dynamic behavior of spur and helical gears. However, to date, TE has not been used as a measure to detect faults in planetary gears. In this paper, we propose a new methodology for model-based fault diagnostics of planetary gears using TE signals. A lumped parametric model of planetary gear dynamics was built to extract simulated TE signals, while accounting for the planet phasing effect, which is a peculiar characteristic of the planetary gear. Next, gear dynamic analysis was performed using TE signals, and TE-based damage features were calculated from the processed TE signals to quantitatively represent health conditions. The procedures described aforesaid were then applied to a case study of a planetary gear in a wind turbine gear train. From the results, we conclude that TE signals can be used to detect the faults, while enhancing understanding of the complex dynamic behaviors of planetary gears.

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.

A deep learning model trained under a specific operating condition of the gearbox often experiences an overfitting problem, which makes it impossible to diagnose faults under different operating conditions. To solve this problem, this paper proposes an ensemble of deep domain adaptation approaches with a health data map. As a fundamental approach to alleviate the domain shift problem due to inhomogeneous operating conditions, the vibration signal is transformed into an image-like simplified health data map that visualizes a tooth-wise fault of the gearbox. The simplified health data map enables the use of a conventional convolutional neural network (CNN) model. To solve the remaining domain shift problem even with the simplified health data map, this study employs a maximum classifier discrepancy (MCD), which is a typical domain adaptation method. To further enhance its performance, a discrepancy-scale factor-based MCD and its ensemble approach are proposed. The proposed method is demonstrated with a 2 kW planetary gearbox testbed operated under stationary and non-stationary speed conditions. The results present that the proposed method outperforms conventional CNN and MCD even under the inhomogeneous operating condition of the gearbox.