## 1 Introduction  
With the rapid development of digital sensor technology and the wide application of the industrial internet, it has become easy to use a variety of sensors to collect the operation state of different parts of mechanical equipment from different views in real time [1]. However, most intelligent fault diagnosis devices are still based on the analysis of single-view data. In practical application, due to the complex structure of mechanical equipment, bad working environments and other factors, single-view data obtained has one sidedness and limitations, resulting in inaccurate and unreliable diagnosis results. Compared with single-view data, multi-view data contains both a lot of consistent information and more complementary information [2]. Comprehensive consideration of the correlation of information of different views helps to improve the accuracy and performance of data analysis [3]. However, how to combine multi-view data to form a consistent representation is still a challenging problem. The purpose of multi-view information fusion is to fuse the consistency and complementarity of information contained in multi-view data according to a certain strategy to obtain a consistent interpretation of the monitoring object [4], so as to improve the fault diagnosis accuracy and improve the fault diagnosis performance. According to the different levels of information fusion, the existing fusion methods can be divided into data level fusion, feature level fusion and decision level fusion [5; 6]. Data level fusion directly fuses raw data from multiple views, and then analyzes them. Feature level fusion extracts a variety of feature expressions from the data of each view, then comprehensively analyzes and processes this feature information, and combines it into a group of comprehensive feature sets. Finally, the method of pattern recognition is used for fault diagnosis. Decision level fusion is fusion processing at the highest level of information representation [7]. First, each view data is analyzed independently, and then the results of each view analysis are fused through Bayesian theory, evidence theory (D-S) and other strategies. Although the existing multi-view information fusion methods can achieve better diagnosis results and are widely used in the fault field, there are still two problems. First, most of the existing multi-view fusion methods assume that each view information has the same importance and contribution. In fact, the degree of correlation between data from different views and fault features is not the same. Some view data changes can closely reflect the fault, while some have no correlation with fault diagnosis [8]. Second, in the existing multi-view information fusion methods, based on the fusion of the feature level and the decision level, the information loss is relatively large [9]. Although the fusion based on the data level can mine the fault features of the data to the greatest extent, with the smallest data loss and the highest reliability, due to the huge amount of data after the fusion of the data layer, the performance requirements of the equipment are very high. At present, the research based on data level fusion is limited. In order to make up for the above deficiencies, this study proposes a multi-view learning model with view level weight (CAM_MCFCNN), which realizes the data level fusion of multi-view information, and provides a new idea for multi-sensor collaborative fault diagnosis. In the proposed approach, a multi-channel dataset is constructed to integrate multi-view data for collaborative fault diagnosis, and the multi-view information data-level fusion is realized with the help of a convolutional neural network. The channel attention mechanism is introduced to learn the weight of each view, and distinguish important views from non-important views in the training process. The rest of this paper is organized as follows.