An adaptive data fusion strategy for fault diagnosis based on the convolutional neural network

Abstract

Abstract Intelligent diagnosis algorithms can monitor faults with industrial production of a timely manner via their powerful learning ability. Multi-sensor diagnosis systems can more comprehensively describe the state of equipment and avoid the influence of incorrect data acquisition locations, which is beneficial to fault diagnosis. The fusion of the original data is a difficult problem, and it is hard to express effective information via traditional algorithms. This paper presents an adaptive data fusion strategy based on deep learning called the convolutional neural network with atrous convolution for the adaptive fusion of multiple source data (FAC-CNN). Specifically, an adaptive-sized convolution kernel that matches the channel of data sources is constructed to capture multi-source data without tedious preprocessing, and the channel of data sources is not limited. The atrous convolution kernel is introduced to expand the field of view of the FAC-CNN and extracts fusion sequence features without repeated computation, resulting in improved stability. The 1D-CNN is added to extract features after atrous convolution. In addition, batch normalization optimizes the distribution of fusion data and the structure of the model. The parametric rectified linear unit activation function and global average pooling are also introduced to improve network performance. The proposed method is validated on an industrial fan system with non-manufacturing faults and a centrifugal pump. Compared with other fusion methods and diagnosis algorithms based on feature engineering, namely CNN, ANN, and SVM, the FAC-CNN model is found to exhibit superior performance.