Application Of Wavelet Transform Fault Diagnosis Method In Wireless Sensor Networks

Wireless sensor networks are widely used in climate and environmental monitoring,intelligent transportation,biomedical and military fields due to their low cost,easy installation and maintenance,and unattended operation.In recent years,the application of wireless sensor network technology in the field of industrial equipment fault diagnosis has gradually gained attention.Existing research methods mostly transmit raw data directly to the host computer or other nodes through the wireless sensor network,which causes the wireless nodes network bandwidth to fail to meet the requirements,and will increase the node energy consumption.In order to solve the problem of limited network bandwidth and energy,it is the key to the current research to explore how to reduce the amount of data transmission,make full use of the computing power of nodes,and design reasonable and efficient algorithms.This paper presents a device fault diagnosis method based on wavelet transform and wireless sensor network.The method directly performs data processing and fault classification on the device vibration data on the WSNs node,and only transmits the classification result to the coordinator node and the upper computer.The method fully utilizes the computing power of the node itself,can reduce the data transmission amount and transmission time,reduce the energy consumption of the WSNs node,and reduce the requirement on the network bandwidth.The specific research contents of this paper are as follows:1.The research uses wavelet transform,tunable-Q wavelet transform and multi-classification support vector machine to extract equipment fault features and fault diagnosis,and carries out simulation verification.2.A device fault diagnosis system based on wireless sensor network is designed.Using C language programming,equipment fault feature extraction and fault diagnosis based on wavelet transform and multi-classification support vector machine are implemented on the wireless sensor network terminal node.3.The effectiveness of fault feature extraction and fault diagnosis based on wavelet transform and support vector machine on the node is verified by using existing data.The experimental results show that the proposed method can effectively reduce the amount of data transmission and accurately diagnose equipment faults.