# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
## 1 Introduction
Based on the visual patterns, the researchers can detect the failure location, therefore, solving the problem that the conventional methods cannot achieve a perception of the failure state. The rolling bearing vibration signal is a typical non-smooth signal, and the traditional time-frequency analysis technique is difficult to apply. Therefore, [PERSON] et al. [23] used a composite method of the SDP method and Harmattan distance collection in order to diagnose the bearing faults and classification, which converts the incoming vibration signal into an image by using the SDP technique and monitors the health of the object more intuitively than the traditional method. The vibration signal is complex, nonlinear, and nonstationary, but it is a common fault diagnosis signal. [PERSON] et al. [24] developed a rolling bearing fault diagnosis method (ASDP-DBSCAN) by combining the adaptive symmetric point pattern junction density-based noise application space clustering. The proposed method can improve the diagnosis accuracy and can solve the problem that occurs when the detection object is in a harsh environment, using the traditional time-domain, in that the frequency-domain and the time-frequency domain fault processing methods may lead to error information and the signal, therefore, needs to be denoised in advance when using the traditional methods for fault diagnosis. However, the SDP technique requires no denoising of the signal when the amplitude that is required is lower than the background noise; in addition, the SDP technique is suitable for making subtle differences in the apparent input signal for easy analysis and is, therefore, more effective than the other techniques. The rotational stall is a common fault in centrifugal fans, and many researchers have proposed rotational stall detection methods, but the detection has been seen to be unintuitive. [PERSON] et al. [25] made use of the SDP technique for fault diagnosis by extracting the pressure signal for detecting the rotational stall from a fan, characterizing the signal in a visual form, which is more effective than the other techniques when the effective signal is relatively low compared to the background noise, laying the foundation for centrifuge stall prevention. Transient shock is the main form of rolling bearing failure, and it is usually disturbed by external noise. Therefore, in order to solve this problem, [PERSON] et al. [26] used the SDP method with empirical modal decomposition after the first five eigenmode functions (IMF) improved the Chebyshev distance for fault diagnosis, which is more accurate than the common fault diagnosis. For transient shock, a common solution is to combine the empirical modal decomposition and the principal. The advantages of the component analysis is the analysis of the bearing faults, but the error of this classification effect is larger. In addition, the SDP technique is used to convert the vibration signal into an image, which saves the parameter adjustment time and avoids information loss. The traditional fault diagnosis methods are difficult to deal with in terms of the nonlinear vibration signals. Therefore, [PERSON] et al. [27] designed an SDP technique with a deep convolutional neural network for a variable condition bearing fault diagnosis algorithm; traditional fault diagnosis methods may lead to undesirable or even wrong results for nonlinear signals, therefore, using the proposed method can not only visualize and diagnose the mechanical equipment faults, but can also improve the fault diagnosis results. Although the SDP algorithm can visualize the health status, the fault generation is not generated by a single direction, but rather by multiple directions. The fault signal cannot be fully captured by mounting the sensor at a fixed location, and the accurate diagnosis of the fault cannot be obtained by the data from a single direction sensor.