A Novel Fault Feature Extraction Method for Planet-Bearing Based on Generalized Total Variation Model

Planet-bearing plays a critical role in supporting planet gear and transmitting power, yet their fault-induced vibrations are inherently modulated by time-varying transmission paths, and thus it brings significant challenges to fault feature extraction. Therefore, this means that it is imperative to develop a robust fault analysis methodology. For one thing, this paper adopts minimum noise amplitude deconvolution (MNAD) to deal with the fault signal of planet-bearing inner ring. It is different from the traditional blind deconvolution method, mainly by suppressing the marked area noise components, which indirectly makes the fault feature prominent. For another, the first-order total variation model is prone to fall into the gradient effect problem. It takes into account the regular term of higher order total variational to achieve the purpose of minimizing approximation. In addition, two gradient weight parameters are introduced to balance the difference operator with different orders. For this reason, the generalized total variational denoising model (GTVDM) is constructed, and then the angular domain signal filtered by MNAD is input into this model. Meanwhile, it utilizes the Split Bregman algorithm to acquire the optimal solution of the constructed model. The core analysis process transforms an objective function optimization problem into several sub-problems, significantly improving computational efficiency. Finally, the experimental validation using fault test data of planet-bearing inner ring demonstrates that the combined MNAD algorithm and GTVDM framework effectively, and the fault feature order and modulation sideband caused by time-varying transmission path are observed in the squared envelope order spectrum.