# Multi-Sensor GA-BP Algorithm Based Gearbox Fault Diagnosis
## 1 Introduction  
Gearbox have been widely used in nuclear power gearboxes, wind turbines, cranes, and other fields. The gear faults will lead to low productivity and even serious injury and property damage, but it is difficult to detect due to the complex working environment. How to improve the ability of monitoring of gear health is important in industrial applications. In terms of researches using traditional fault diagnosis methods, they tend to adopt data collected by a single sensor to perform time domain, frequency domain, and wavelet analysis on a physical quantity. A composite diagnosis method combined resonance-based sparse signal decomposition (RB-SSD) and maximum correlated kurtosis deconvolution (MCKD) for improvement of vibration signal fault recognition rate is proposed by [PERSON] [1]. Based on the multi-channel fault identification method, [PERSON] et al. [2] made use of the Empirical Mode Decomposition (EMD) method to convert the signal into a multi-channel one-dimensional signal, and then extracted and classified its features to realize the gearbox fault diagnosis and solve the fault. This has made up for the lack of fault identification features. [PERSON] et al. [3] monitored the health of the pick gear artificially and proposed to use the BP neural network algorithm to detect the faults of various characteristic signals collected. Due to the long iteration time and low accuracy of the traditional BP neural network, [PERSON] et al. [4] proposed Genetic Algorithm (GA) to perfect the traditional BP algorithm. Furthermore, the accuracy was improved after the improvement. The detection status obtained by the classifier from a single sensor cannot comprehensively represent the health status. To this end, in recent years, Dempster-Shafer evidence theory (DS) multi-source information fusion technology has been widely used in the field of fault diagnosis [5; 6; 7; 8; 9]. [PERSON] et al. [10] came up with a fusion method of random forest mixed classifiers, which realized the diagnosis of a single fault in a complex fault environment and significantly promoted the reliability of the diagnosis. In addition, [PERSON] et al. [11] came up with a multi-source information fusion method that combines cloud theory and D-Sevidence theory for the ambiguity and uncertainty of multi-source fault signals. Furthermore, it has been verified by the fault diagnosis test of the rolling bearing of the Computer Numerical Control (CNC) machine tool. [PERSON] et al. [12] made use of the combination of D-S theory and support vector machine to diagnose the fault range and type of a structural beam. [PERSON] et al. [13] thought out a decision-level fusion of the DS theory and the diagnostic results obtained by Support Vector Machines (SVM) to reduce the possibility of errors. Deep learning algorithms are also widely used in the field of fault diagnosis. [PERSON] et al. [14] proposed to combine deep learning algorithms and SVM algorithms for fault detection on the surface of 3D printed products, and the method enabled real-time monitoring in both offline and online states. Deep learning can detect squirrel cage induction motors (SCIMs) in early, [PERSON] et al. [15] proposed a novel fault detection technique for bearing faults and broken rotor bar detection in SCIM using the dilated convolutional neural network-based model. The propounded approach accomplished an average accuracy of more than 99.50%. In addition, [PERSON] et al. [16] proposed deep learning algorithm for estimating remaining bearing life, and it achieved an accuracy of over 90% in anomaly detection and Remaining Useful Life (RUL) prediction. [PERSON] et al. [17] proposed multi-feature fusion of the features obtained from the EMD method with some time-domain features as an input to the GA-BP algorithm to achieve fault diagnosis of bearings, which has a higher identification rate than using one feature vector. [PERSON] et al.