# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
## 1 Introduction
It is not enough to only collect a single sensor signal and convert it into the SDP image in order to assess the health status, which results in low accuracy and unstable diagnostic results. In order to improve the accuracy of the diagnostic results, this paper adopts a decision-level approach of multi-sensor fusion. Nowadays, most diagnostics are processed by acquiring a single channel vibration signal, which lacks a comprehensive and accurate signal in the acquisition of the raw signal [28]. Many researchers have worked on improving the performance of fault diagnosis models in order to improve the fault diagnosis accuracy but have ignored the limitations of single sensors that are used to describe the fault diagnosis. Multi-sensor decision-level fusion fault diagnosis methods have been introduced in order to improve the accuracy of the fault diagnosis. Compared with single-sensor diagnosis methods, multi-sensor diagnosis methods can obtain more fault information and, therefore, they produce more reliable diagnosis results [29]. The multi-channel signals that are collected by multi-sensors contain more signals than the single-channel signals, therefore, the multi-channel processing method can improve the reliability and the accuracy of the fault diagnosis [30]. With the increase in mechanical detection sensors, [PERSON] et al. [31] selected two multi-channel sensors to detect and to diagnose the motor without using the faults, which saves time when compared to the traditional multiple single-head sensors and allows the separate processing of each sensor channel, increasing the feature extraction and improving the fault identification. [PERSON] et al. [32] designed a fault diagnosis method of multi-channel feature-level fusion in order to detect the problem of sudden bearing failure, where multiple sensors are usually set up in order to evaluate the rolling bearing health status so as to improve the stability and the reliability of the detection results. However, the feature-level fusion is located at the middle level of the fusion theory hierarchy, which requires a certain feature screening capability and is more complicated compared to the decision-level fusion. Inspired by deep learning algorithms and multichannel signals for the automatic diagnosis of fault types in complex operating conditions of rotating machinery, [PERSON] et al. [33] input multi-channel signals into an integrated transport convolutional neural network and used decision-level fusion in order to flexibly fuse the results of each channel, with a higher diagnostic performance than the existing deep migration learning algorithms. [PERSON] et al. [34] addressed the problem that unimodal signals are easily affected by the external environment, which leads to low accuracy, by proposing a convolutional neural network (CNN)-based approach in order to achieve multimodal sensor signal fusion, and the proposed method was experimentally shown to have a higher diagnostic accuracy than that of single sensors. Identifying micro-faults in early rotating machinery is a hot research topic in the field of fault diagnosis, and [PERSON] et al. [35] proposed an improved convolutional neural network-support vector machine (CNN-SVM) method to compensate for the traditional fault diagnosis method that relies on the manual extraction of the fault features by engineers with a priori knowledge. This method is rich in data multi-dimensionality and can automatically and accurately achieve early fault diagnosis. Deep learning algorithms have high requirements on the amount of training data. [PERSON] et al. [36] developed a composite method of multi-channel convolutional neural networks combined with multiscale limit fusion data in order to enhance the sensor data, which can improve the fault classification accuracy and clustering effect. [PERSON] et al.