Abstract
The fault signals of planetary gears are nonstationary and nonlinear signals. It is difficult to extract weak fault features under strong background noise. This paper adopts a new filtering method, fractional Wavelet transform (FRWT). Compared with the traditional fractional Fourier transform (FRFT), it can improve the effect of noise reduction. This paper adopts a planetary gear fault diagnosis method combining fractional wavelet transform (FRWT) and two-dimensional convolutional neural network (2D-CNN). Firstly, several intrinsic mode component functions (IMFs) are obtained from the original vibration signal by AFSA-VMD decomposition, and the two components with the largest correlation coefficient are selected for signal reconstruction. Then, the reconstructed signal is filtered in fractional wavelet domain. By analyzing the wavelet energy entropy of the filtered signal, a two-dimensional normalized energy characteristic matrix is constructed and the two-dimensional features are input into the two-dimensional convolution neural network model for training. The simulation results show that the training effect of this method is better than that of FRFT-2D-CNN. Through the verification of the test set, we can know that the fault diagnosis of planetary gears can be realized accurately based on FRWT and 2D-CNN.
Highlights
As an important part of rotating machinery and equipment, planetary gears usually operate in a high-speed and highpower environment. ey are widely used in aircraft manufacturing, coal mining machinery, wind power generation, ship manufacturing, and other industries
Gao Hongying and others proposed a planetary gear fault identification method combining complementary set empirical mode decomposition (CEEMD) and chaotic particle swarm kernel extreme learning machine (CPSO-ELM), which reduces the influence of external disturbances on planetary gear fault diagnosis [2]
Wang Zhenya and others proposed a fault diagnosis method based on optimized variational modal decomposition and multidomain manifold learning of the salvia group, which solved the problem of difficult feature extraction and identification of planetary gears [3]
Summary
As an important part of rotating machinery and equipment, planetary gears usually operate in a high-speed and highpower environment. ey are widely used in aircraft manufacturing, coal mining machinery, wind power generation, ship manufacturing, and other industries. Wang Zhenya and others proposed a fault diagnosis method based on optimized variational modal decomposition and multidomain manifold learning of the salvia group, which solved the problem of difficult feature extraction and identification of planetary gears [3]. Li Yuheng proposed a fault diagnosis method that combines the ensemble empirical mode (EEMD) and the symmetrical differential energy operator to achieve accurate diagnosis of planetary gears and accurately obtain the fault characteristic frequency value of planetary gears [5]. In order to realize the planetary gear fault diagnosis under strong background noise, this paper adopts the planetary gear fault diagnosis method combining fractional wavelet transform and two-dimensional convolutional neural network. Use a two-dimensional convolutional neural network to establish a fault diagnosis model to achieve accurate identification of different faults under different working conditions
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call