# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
## 1 Introduction
[37] applied the Dempster-Shafer evidence theory (DS theory) to the fault diagnosis of the interfacial adhesion of honeycomb sandwich structures in order to solve the influence of noise on the diagnosis results, which can improve the diagnostic performance and noise resistance. In fault diagnosis, since it is difficult to detect a single state from a complex state, [PERSON] et al. [38] proposed a composite method of combining random forest and DS evidence theory, which has a higher accuracy compared to methods that use a single sensor. The hydraulic valve occupies an important position in the hydraulic system and, due to its complex structure, it is difficult for the existing methods to detect multiple faults in the hydraulic valve. [PERSON] et al. [39] used an integrated intelligent diagnosis method for hydraulic valve faults based on DS theory. The common integration algorithm integrates multiple information fusion theories into the fault learning algorithm, but the simple information fusion theory cannot effectively solve the unavoidable information source uncertainty problem in the decision support system process. The DS evidence theory algorithm can solve the information source uncertainty problem, and the diagnosis accuracy is, therefore, improved. In order to substitute the use of two or three single-channel sensors to compensate for the low accuracy of a single sensor, we directly use the multi-channel sensors. The multi-channel sensors can ensure the real-time data acquisition and the accuracy, can facilitate the acquisition of the experimental data, and can reduce the error of the original data. In addition, arranging multiple multi-channel sensors at different locations can allow the collection of the health status data of multiple locations of the test object, which can more carefully ensure the comprehensiveness of the data and the accuracy of the diagnosis results. Therefore, multiple multi-channel sensors are used in order to collect vibration signals in this paper. Nowadays, deep learning is very popular in the field of fault identification. In traditional fault diagnosis methods, it is difficult to visualize the gear fault status. However, the SDP-based processing methods can convert the time series signals into visual images, which can realize the directness of the gear fault diagnosis. Researchers are committed to improving the performance of the fault diagnosis models in order to improve the fault diagnosis accuracy, but they ignore the limitations of individual sensors that are used to describe the fault diagnosis. The SDP image processing method can achieve the feature-level fusion of multi-channel sensor data, but does not consider decision-level fusion. The decision-level fusion is located at the highest level in the fusion theory, which can make the diagnosis results retain the original data state, can reduce the missing data, can ensure that the diagnosis results are comprehensive, and can improve the accuracy of the fault diagnosis. Therefore, this paper proposes a multi-sensor multi-channel decision-level fusion fault diagnosis method by combining the SDP image method with the VGG16 for the current fault diagnosis method that only performs multi-channel feature-level fusion.