## 3 The Proposed Method
### Data Augmentation Based on WSC Image Segmentation  
Planetary gearboxes are designed for large loading capacity, high transmission efficiency, strong shock resistance, and vibration resistance. It is also because of its unique performance that its operating environment is worse and the collected vibration signal is more complex. The transmission path of the fault excitation signal, the contact force between components, and the attenuation characteristics caused by load fluctuations will add modulation components to the vibration signal of the planetary bearing. In addition, the planetary gearbox bearing fault signal often contains complex noise components due to the harsh operating environment of mechanical equipment and large external interference. In this case, traditional time-frequency analysis methods fail to reveal fault-related feature information. Most of the planetary bearings' vibration signals in the fault state will cause some form of Cyclostationarity, which leads to the discrete peak of the envelope spectrum in the fault characteristic frequency.  
In this paper, aiming at the problems existing in the research of current fault diagnosis theory and methods, considering the characteristics of the vibration signal collected under actual working conditions, such as small amplitude, low energy, and large noise, the purpose is to explore the fault source information enhancement mechanism of label data based on a CNN deep classification network. In order to further optimize the weight coefficient, a parameter self-adjusting (PSA) method based on multiple error back propagation is applied in this paper.  
It is undoubtedly more representative data that can improve the classification effect of the model. However, it is difficult to meet this requirement in practical application. Small disturbances will bring bias to the diagnostic results based on the data-driven model, which requires the analysis samples to be representative and have relatively clear target characteristics. In this paper, a mechanism for enhancing fault source information based on the cyclic wavelet spectrum is proposed, aiming at the characteristics of the small amplitude, low energy, and strong noise of the planetary bearing vibration signal. The wavelet transform coefficients are regarded as a special cyclic statistic to calculate the cyclic spectrum so that the non-stationary fault-characteristic signal of the planetary gearbox bearing is transformed into a periodic stationary signal, which can reduce the interference of irrelevant information and enhance the impact characteristics. However, it will increase the computational burden of the algorithm. In order to improve the computational efficiency of the algorithm, this paper proposes to further segment the cyclic spectrum to use local features of samples as the matching object. On the one hand, it can reduce the size of a single sample; on the other hand, it can enhance the number of samples.  
Image local feature extraction is realized by overlapping and blocking the image. When dividing the spectrum into blocks, a local overlapping is performed. Suppose the length of the overlapping area is \(q\), it defines the small block as a square with an edge length of \(s\). The visualized graphics are shown in Figure 2.