Joint Self-Attention Mechanism and Residual Network for Automated Monitoring of Intelligent Sensor in Consumer Electronics

The popularity of the Internet of Things has led to the rapid development of the consumer electronics industry and the interconnection of digital devices in various scenarios. The effective utilization of sensors can provide accurate data support for the development of IoT. With the construction of intelligent IoT, general sensors have been unable to meet the actual demand of system operation, it is necessary to rely on intelligent sensors to fully promote the intelligent and modern construction of IoT. In particular, intelligent sensors are usually employed in complex environments, which require automated monitoring of operating status to ensure the stability of electronic devices. Meanwhile, intelligent sensors are applied in industrial production, and its status monitoring is of great significance for social safety. For this purpose, we propose an automated monitoring method for intelligent sensors in consumer electronics by a joint self-attention mechanism and residual network. We design residual feature extraction and self-attention mechanism for mining salient representations of sensor faults, respectively, and fuse time-domain contextual information using long- and short-term memory network. The proposed methodology is able to capture and analyze the characteristics of sensor faults more effectively and understand the temporal nature of sensor data more comprehensively, which further improves the accuracy and reliability of operation state monitoring and fault diagnosis. The qualitative and quantitative experiments in two public datasets demonstrate the effectiveness of the proposed method.