Experimental System Design for Industrial Equipment Status Monitoring Based on Environmental Sound Recognition
A test system for industrial equipment status monitoring based on environmental sound recognition is designed to achieve low-cost equipment fault detection.The RFL-MobileNet model is developed,and deployed in Arduino open-source hardware to implement low-cost intelligent sensors.By performing intelligent data processing at the edge,the system provides real-time and accurate status monitoring for factory equipment,reduces the latency and data leakage risks brought by data transmission to the cloud in traditional methods.Experimental results based on public datasets and actual datasets demonstrate that the system's functionality can work well,with the detection accuracy of the operating status of direct-current motors reaching 95.4％.
embedded machine learningequipment status monitoringaudio classificationexperimental system
万方数据
维普
