Fault Diagnosis of Planet Gear Using Continuous Vibration Separation and Minimum Entropy Deconvolution  
[PERSON]  
1 College of Intelligence Science and Technology, National University of Defense Technology,  
Changsha 410072, China; [EMAIL_ADDRESS] (J.S. ); [EMAIL_ADDRESS] (N.H.) Laboratory of Science and Technology on Integrated Logistics Support,  
National University of Defense Technology, Changsha 410072, China  
12  
a coupled dynamic model to analyze the coupling vibration mechanism of planetary gear system. [PERSON] [10] established a model to study the load sharing behavior; then, the model is used for reliability prediction purpose. [PERSON] [11] built a vibration signal model by considering amplitude modulations and frequency modulations, as well as vibration transfer path. In past decades, especially from 2010, diagnostic and prognostic research on planetary gearbox has attracted lots of attention. Scholars have tried various signal processing techniques to detect damages in planetary gearbox. [PERSON] [12] applied stochastic resonance method on fault diagnosis of planetary gearbox focusing on weak signal detection. [PERSON] [13] proposed an autocorrelation-based time synchronous averaging method for fault diagnosis of wind turbines. [PERSON] [14] developed a feature selection algorithm for fault level diagnosis of planetary gearbox. [PERSON] [15] applied adaptive optimal kernel time-frequency analysis on fault diagnosis of wind turbine planetary gearbox mainly considering the nonstationary working conditions, and then, another diagnosis method was proposed using time-frequency demodulation analysis based on iterative generalized demodulation method [16]. [PERSON] [17] proposed a fault diagnosis method based on entropy feature fusion of ensemble empirical mode decomposition. [PERSON] [18] proposed an approach to detect planetary gear damage quantitatively using a physical model, as well as a hybrid method to predict the remaining useful life of planetary gearbox with a local defect [19]. [PERSON] [20] investigated a phase angle-based diagnostic scheme for fault diagnosis of planetary gear by extracting phase angle data from vibration signals under nonstationary conditions. [PERSON] [21] proposed a diagnosis method using step by step fuzzy logic for equipment; the method was based on symptom extraction and trivalent logic fuzzy theory. [PERSON] [22] proposed an integrated varying-load approach for predicting wind turbine gearbox remaining useful life by considering instantaneously varying external loads. Recently, there are two emerging tendency in diagnostics and prognostics of planetary gearbox, namely, neural network-based methods and vibration separation-based methods. Neural network-based methods tends to construct a mapping relationship from raw signal to fault condition directly [23] by training a neural network, instead of trying various signal processing algorithms including FFT (fast Fourier transform), STFT (short-time Fourier transform), WT (wavelet transform), and so on. [PERSON] [24] developed a feature learning method based on deep conditional variational neural networks. [PERSON] [25] diagnosed planetary gearbox fault under nonstationary running conditions using time-frequency analysis and deep residual networks. [PERSON] [26] proposed an enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes. [PERSON] [27] proposed a deep convolutional neural network-based data fusion method for health state identification of planetary gearboxes. On the contrast, vibration separation-based methods try to separate out dynamic behavior of each gear components inside the planetary gearbox to minimize dynamic interferences between each other, rather than regarding the planetary gearbox as a black box without considering its inside structures. Vibration separation was firstly proposed by [PERSON] [28] based on [PERSON]'s works [29; 30] on planetary gearbox local defects detection.