# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
###### Abstract  
Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy. planetary gearbox; ICEEMD; time-frequency information entropy; VPMCD; fault diagnosis 1  
[PERSON] 1,2  
[PERSON] 1,2  
domain analysis do not reflect the fault information of planetary gearboxes and the diagnostic accuracy of fault diagnosis is unsatisfactory. For this reason, time-frequency analysis has emerged as an important signal processing method and has a wide range of applications in the field of vibration signals. Many time-frequency methods, such as wavelet transform and empirical mode decomposition (EMD), are applied to analyze nonlinear and nonstationary signals of planetary gearboxes [9]. However, the basis function of wavelet transform has no self-adaptability. It needs to be a preselected fixed-basis function according to the characteristics of the different signals. The empirical mode decomposition algorithm can adaptively decompose the vibration signal into a series of intrinsic mode functions (IMFs) from high frequency to low frequency [10], but usually generates problem of mode mixing. Later, a series of derived methods came into being such as ensemble empirical mode decomposition (EEMD), local characteristic-scale decomposition (LCD), and complementary ensemble empirical mode decomposition (CEEMD), and so on. However, low-speed and heavy-duty operation conditions with high noise environments frequently cause faults in the planetary gearboxes. It is more difficult to diagnose a fault than to fix the axis gearboxes, in which accurate diagnosis is very important. Due to the interference of noise, the problem of mode mixing is more severe, which leads to difficulty in completing the subsequent feature extraction. The above methods do not solve the problem caused by high noise. It is necessary to find a new method of signal features analysis with noise reduction to suppress the influence of noise on the vibration signal. The permutation entropy algorithm, a useful tool in amplifying slight changes of signals, has advantages of generality and easy computing in nonlinear and nonstationary signals [11]. Based on the permutation entropy, [PERSON] et al. proposed multi-scale permutation entropy (MPE) to depict tiny changes of signals from different local scales rather than a single scale [12]. Therefore, the MPE algorithm was utilized to effectively distinguish components between noise and original vibration signals of planetary gearboxes.