# Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG
## 5 Conclusions  
To achieve health monitoring for the degree of pitting of gear teeth, since the traditional machine learning algorithm cannot visualize gear fault status, this paper proposes combining SDP images with the VGG16 fault diagnosis algorithm in order to gear tooth fault diagnosis monitoring. Additionally, since the signal collected by single sensors lacks comprehensiveness, and the original signal cannot be obtained from multiple dimensions, it is also proposed to achieve multi-sensor decision-level fusion using DS evidence theory with the local diagnostic results of each sensor obtained using the VGG16 fault diagnosis model algorithm.  
Figure 15: Accuracy before and after multi-sensor fusion.  
In this paper, the proposed method is first compared with the traditional BP algorithm and GABP algorithm through the common bearing dataset of Case Western Reserve University, and the results show that the accuracy of the proposed method is higher than those of the traditional BP algorithm and the GABP algorithm. Then, the proposed method is applied to the experimental data, and the SDP images of each sensor are input into the VGG16 fault diagnosis model, and the obtained local diagnosis results are input into the DS evidence theory as the body of evidence in order to realize the decision fusion level fitting of gear teeth. The results of the simulation and experiment show that the method proposed in this paper, with higher diagnostic accuracy after fusion than before fusion, can compensate for the drawback of poor diagnostic results inherent to using a single sensor. The research results achieved in this paper are as follows: (1) The images generated by the SDP algorithm can detect gear fault diagnosis more intuitively than the time-domain vibration signals. (2) The diagnostic accuracy of the VGG16 algorithm is 0.14% and 0.13% higher than those of the BP algorithm and the GABP algorithm, respectively, in the traditional machine algorithm. (3) The experimental results under the decision-level fusion based on VGG16 show that when three sensors are fused simultaneously, the accuracy can reach 99.98%, which is 3.68%, 6.40%, and 7.18% higher than those of single sensors 1, 2, and 3, respectively. The simultaneous fusion of three sensors improves the accuracy by 0.05%, 0.31%, and 0.58% over sensor 1 and 2 fusion, sensor 1 and 3 fusion, and sensor 2 and 3 fusion, respectively. The use of the DS fusion method can compensate for the disadvantage of incomplete data collection inherent to the use of a single sensor, and can improve the results of gear tooth fault diagnosis, which is important for the fusion algorithm in the field of fault diagnosis.  
[PERSON] conceived this research; [PERSON] and [PERSON] performed the calculations; [PERSON], [PERSON] and [PERSON] prepared the original draft; editing and review on this article was completed by [PERSON]; [PERSON] and [PERSON] were responsible for overseeing the progress and visualization of the article. All authors have read and agreed to the published version of the manuscript.  
This research was founded by the National Key R&D Program of China (NO. 2020 YFB2010103), the National Natural Science Foundation of China (NO. 52075062), Ministry of Industry and Information Technology's Industrial Foundation Reconstruction and Manufacturing High-quality Development Project (NO. TC210H02V), and the Graduate Student Innovation Project of Chongqing (NO. CYS21465). The authors declare no conflict of interest. In addition, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.