Research On Equipment Fault Diagnosis Method Based On Industrial Wireless Sensor Networks

With the development and advancement of science and technology,modern machinery and equipment are gradually becoming more complex and diversified.Long-running equipment is extremely prone to failure.In order to avoid failures,equipment condition monitoring and fault diagnosis systems are essential.The equipment fault diagnosis system based on wireless sensor network can effectively compensate for the shortcomings of the wired diagnostic system.This paper starts with wireless sensor network technology and signal processing method,selects motor bearing as the research object,and studies the equipment fault diagnosis method based on industrial wireless sensor network.The main work of the thesis is as follows:(1)The Hilbert-Huang transform is used to extract the fault characteristics of the equipment,and the simulation experiment is carried out in MATLAB.The experimental results show that the Hilbert-Huang transform can effectively extract the fault characteristics of the equipment.(2)The support vector machine model for multi-classification is constructed,and the simulation experiment of motor bearing fault diagnosis is carried out by using this model.The experimental results show that the device fault diagnosis with support vector machine has higher accuracy.(3)Using C language programming,device fault classification based on support vector machine is implemented on the wireless sensor network terminal node(Jennic JN5139).Using the existing data,the effectiveness of equipment fault feature extraction through Hilbert-Huang transform and fault diagnosis through support vector machine on the node is verified.