Research on equipment safety fault diagnosis method based on multi-sensor fusion deep network in mechatronics equipment environment

Abstract

The safety performance and stability of mechatronics equipment play an important role in modern industrial production. However, using a single-sensor signal cannot effectively ensure robustness in complex scenarios. Moreover, the efficient collection and real-time transmission of information can improve the real-time performance of fault detection. To this end, this article proposes a novel deep network based on multi-sensor information fusion for mechatronics equipment fault detection. Firstly, three sensors are used to collect status information. We use the CC2420 model to transmit the collected signals to the server for storage and analysis. Secondly, we designed a multi-sensor information fusion deep network. To better model local and global features, we introduced convolutional operations and the multi-head attention mechanism to form the backbone of the network. The results on the self-built dataset indicate that the proposed model fully utilizes the advantages of multimodal information and deep networks to achieve the optimal fault detection results.