Multi-scale deep coupling convolutional neural network with heterogeneous sensor data for intelligent fault diagnosis

With the innovation and development of detection technology, various types of sensors are installed to monitor the operating status of equipment in modern industry. Compared with the same type of sensors for monitoring, heterogeneous sensors can collect more comprehensive complementary fault information. Due to the large distribution differences and serious noise pollution of heterogeneous sensor data collected in industrial sites, this brings certain challenges to the development of heterogeneous data fusion strategies. In view of the large distribution difference in the feature spatial of heterogeneous data and the difficulty of effective fusion of fault information, this paper presents a multi-scale deep coupling convolutional neural network (MDCN), which is used to map the heterogeneous fault information from different feature spaces to the common spaces for full fusion. Specifically, a multi-scale convolution module (MSC) with multiple filters of different sizes is adopted to extract multi-scale fault features of heterogeneous sensor data. Then, the maximum mean discrepancy (MMD) is applied to measure the distance between different spatial features in the coupling layer, and the common failure information in the heterogeneous data is mined by minimizing MMD to fuse effectively in order to identify the failure state of the device. The validity of this method is verified by the data collected on a first-level parallel gearbox mixed fault experiment platform.