# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
## 1 Introduction
(2018) designed a method to improve the hierarchical learning rate of convolutional neural networks for the existing problems of automatic feature extraction and low diagnostic accuracy, and applied it to bearing fault diagnosis, and through verification, the proposed method is well suited to the field of fault diagnosis, which can automatically and sensitively extract deep fault features without manual screening, therefore, improving the diagnostic accuracy compared to the common methods. In practical industrial applications, the differences in working conditions and data acquisition environments can lead to variability in the collected data, resulting in the poor performance of the diagnostic methods. In order to address these deficiencies, [PERSON] et al. (2018), developed a method that combines CNN with the variational modal decomposition method (VMD) in order to process the original vibration signal directly, in an end-to-end manner, without human intervention, which can make up for the shortcomings of the insufficient features of a single signal and can improve the range capability of the model in order to achieve the fault diagnosis for the bearings under different working conditions. The electro-hydraulic actuator (EHA) is a key component in the equipment that affects the performance of the whole system. In order to improve the reliability of the equipment, data-driven methods have often been used in the past, but it is difficult to build an accurate EHA model due to the complex structure of the system. [PERSON] et al. (2018) made use of a deep learning model-based approach for training the output prediction using monitoring data, which can effectively solve the difficulty of model construction. When studying the rolling body defects, the increased computational effort and the reduced classification progress can cause many statistical algorithms to be ineffective, therefore, [PERSON] et al. (2018) used CNN and residual learning for local feature extraction and dimensionality reduction in order to solve the sequential data in the task of internal combustion engine gearbox health prediction, which has improved the robustness of the diagnostic model and has saved computational time. The complexity of rotating machinery working conditions leads to different distributions of training and testing samples, and the practical applicability of deep learning models has become an urgent task. There are six available CNNs for the transfer and the classification of convolutional neural networks, which are as follows: ShuffleNet, GppgLeNet, ResNet-18, ResNet-50, VGG-16, and DenseNet-201. By comparing these various algorithms for experimental analysis, it can be observed that the VGG16 has the highest accuracy in the experimental results [21]. The traditional fault diagnosis method, based on deep learning algorithms, is time-domain signal processing, with which it is difficult to intuitively see the health status of the monitored object and demands high requirements from the operator. A good method is that which can intuitively monitor the health status and can reduce the professional technical requirements for the operator. The SDP algorithm that is used in this paper can make up for the defect of showing the health status in the form of time-domain signals. It can show the health status of the detected object more intuitively by image. The SDP technique converts the raw signal of the time series into an image, and operators with low expertise can visualize the state of the detection object. The previous detection methods for blades were unable to make a sufficiently rapid response to the onset of a stall and to perceive the method of a stall. [PERSON] et al. [22] proposed the application of SDP technology by using sound visualization for early stall-warning detection for the early fault diagnosis of cooling axial fans, in the context of strong noise.