# A Two-Stage Framework for Time-Frequency Analysis and Fault Diagnosis of Planetary Gearboxes
## 1 Introduction
[19] combined K-means clustering with local Renyi entropy to distinguish the basic structural differences between helpful information content and noise component in the T-F plane, developing an unsupervised adaptive T-F analytical algorithm. In fact, the T-F analysis task of the planetary gearbox is particularly challenging. Because of the complex structure and time-varying characteristic of the planetary gearbox, the characteristic regions in the T-F spectrum are often intertwined with the background, or the contrast and sharpness may be very low. Therefore, traditional image processing methods are difficult for achieving satisfactory performance of such tasks. Previous works have applied deep learning to planetary gearbox fault diagnosis based on T-F analysis. However, these studies usually regard T-F analysis and fault diagnosis as two separate tasks. Other works focus on developing an indivisible intelligent system to accomplish the gearbox fault diagnosis task based on T-F information [20; 21; 22; 23; 24]. This paper aims to incorporate T-F spectrum analysis and fault diagnosis based on T-F feature information into a continuous but stage-by-stage diagnosis framework. This is more similar to manual diagnosis processes. The specific indications of fault feature regions and the high-accuracy fault classification diagnosis can improve confidence in the fault diagnosis conclusion for operators because it provides richer and more comprehensive diagnosis information about the health status of equipment, making the diagnosis results more reliable. Based on the above views, this paper proposes a two-stage diagnosis framework for planetary gearboxes. Because the traditional threshold-based algorithms are error prone for high-resolution T-F spectrum, we implement a fully convolutional network U-net for semantic segmentation; that is, to label the regions of interest (ROI) pixel by pixel. The U-net model highlights the pixels belonging to fault feature regions from the background. Then, the shape features are utilized to extract valuable information from the feature region distribution. Finally, the decision tree algorithm is used to determine the health status of the planetary gearbox. The main contributions of this paper are summarized as follows:  
1. Compared with previous studies that considered T-F analysis or fault diagnosis alone, a more comprehensive two-stage framework is proposed to combine the two tasks. To the best of our knowledge, we are the first paper to discuss planetary gearbox fault diagnosis based on T-F information from the perspective of an analysis and diagnosis two-stage task. 2. The modified U-net model is adopted to process T-F image, which can utilize large-range context information to improve the segmentation accuracy and enhance the robustness against environmental variations. 3. The effectiveness of the proposed method is verified by using the real data of in-service wind turbines. T-F images and ground truth labels are available online [25]. The remainder of this paper is organized as follows. Section 2 briefly introduces the U-net, feature extraction method, and decision tree algorithm used in the research. Section 3 introduces the two-stage gearbox fault diagnosis framework proposed in this paper. Section 4 describes the experimental dataset and analyses the results of running our method on this dataset. Conclusions are outlined in Section 5.