An Intelligent Fire Fan Monitoring System: Threshold Alerting and Bayesian Inference Based on IoT

The emergency start of the fire fan in a fire is crucial for ensuring personnel evacuation and containing the fire situation. To popularize the fault detection of fire fans, develop an Internet of Things(IoT) based fan fault diagnosis system and conduct mechanical and circuit fault testing. The experimental results show that the system can achieve fault prediction of fire fans by collecting current, voltage, and motor temperature, and transmitting them to the cloud platform through the Internet of Things for threshold judgment and Bayesian inference. This method can strengthen the precise maintenance and repair sequence of fire fans, and help build a tripartite intelligent fire protection model managed by the government, manufacturers, and enterprises.