# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 1 Introduction  
Monitoring the running state and accurately diagnosing faults of the motor has great significance for the safe operation of the transmission system [1]. The types of motor faults are numerous and complex, and they can be divided into stator faults, rotor faults, and bearing faults according to the location of the faults [2]. Due to the complex nonlinear mapping relationship between these fault types and fault signals, fault diagnosis is rather complicated [3]. Among traditional methods for motor fault diagnosis, they are generally based on single-sensor signal analysis or intelligent learning. In this literature study [4], to increase effective information on the fault features to diagnose the motor, wavelet transform is used to extract the features of the motor stator current, and the adaptive filter eliminated the fundamental component in the stator current. In this literature study [5], a research method is proposed to convert three stator currents of a motor into images with three different resolutions. In this method, the obtained image is used as the input of the multi-layer artificial neural network, and the network is trained and optimized by many samples. Finally, the optimized model with higher diagnostic accuracy is obtained. In this literature study [6], a deep belief network (DBN) is proposed. By training the network using the real-time acquired image-based vibration signals as input, tool fault diagnosis and identification of tool fault changes during milling are achieved. In this literature study [7], the deep learning method of the convolutional neural network (CNN) is adopted to extract multi-scale features from the original vibration signals, using the adaptive convolution operation to reduce each feature map to the same size for fusion. The fused fault featureswere finally classified using the Softmax function. In this literature study [8], different convolutional neural network architectures (such as AlexNet, ResNet-50, LeNet-5, and VGG-16) are designed and adapted to monitor the health of milling cutters, respectively. In addition, this article analyzed and discussed the classification accuracy, recall, advantages, and disadvantages of convolutional neural networks with different architectures. It has important implications for the design of our network architecture. As seen from the above research methods, which play an important guiding role in this paper, the research on fault diagnosis methods is deepening with technology development. However, all the above studies are based on a single sensor for fault diagnosis. Although a single sensor has good stability and data complexity, it has a low fault tolerance rate and limited fault information. To improve the diagnostic accuracy rate, Multi-sensor Data Fusion (MDF) technology has become an important research direction of fault diagnosis. In this literature study [9], a multi-channel one-dimensional convolutional neural network is proposed. It used two vibration sensors in different directions to detect and diagnose six types of motor faults. Compared to a single vibrational signal, its diagnostic accuracy is improved. In this literature study [10], a spatiotemporal multi-correlation fusion method for multi-source vibration fault signals is proposed. This method utilized multiple interrelationships of spatial positions to explore the correlation between vibration sensors at different places, effectively improving the connection between sensor data. In this literature study [11], a fault diagnosis method is proposed, which is based on the feature layer fusion of vibration and acoustic signals of 1D-CNN. In this method, two types of dissimilarity information are taken as the input of 1D-CNN simultaneously, and the features are extracted, fused, and output. The cited literature study [12] is a further optimization of the previous study [11]. Firstly, the multi-sensor information is divided into homologous and heterogeneous categories.