Research And Implementation Of Industrial Equipment Monitoring System Based On IWSN

Traditional industrial equipment monitoring systems using wired networks have a number of disadvantages,such as complex wiring,inflexible systems,severe environmental impact,and high cable maintenance costs.In response to these shortcomings,this thesis designs and develops an industrial equipment monitoring system based on Industrial Wireless Sensor Network(IWSN)to ensure real-time acquisition of industrial equipment monitoring data,which is convenient for users to view industrial equipment parameters in real time.This thesis focuses on the fault prediction algorithm of industrial equipment monitoring system,and then realizes the early warning function of equipment,providing a reliable basis for scientific management of industrial equipment.This thesis focuses on the early warning of industrial equipment failure.The early warning model is BP neural network optimized by dynamic cuckoo search algorithm,and then analyzes and predicts the operating parameters of industrial equipment collected by industrial wireless sensor networks.In order to accelerate the convergence speed of the network and avoid the problem of falling into the local minimum,this thesis uses the dynamic cuckoo search algorithm to optimize the structure of the BP neural network.Aiming at the shortcomings of the optimization algorithm,this thesis proposes a dynamic cuckoo search algorithm.The dynamic cuckoo search algorithm improves the step size and discovery probability,and introduces the change trend of the fitness function value into the step update formula to balance the search speed.And precision,the initial global search of the algorithm is large,and the algorithm searches for small steps in the late stage.At the same time,in the process of discovering the global optimal solution,improve the retention probability of good offspring,improve the uncertainty of the preference random walk,and reduce the discovery probability as the search progresses,making it easier for new individuals to evolve later.Finally,a BP neural network prediction model optimized by dynamic cuckoo search algorithm is established.The simulation results show that the model has faster convergence speed and higher prediction accuracy.On this basis,this thesis designs and implements an industrial equipment monitoring system based on IWSN.The system operation results show that the system can monitor the operating parameters of industrial equipment in real time and display it visually.Through the industrial equipment failure warning function,the equipment administrator can be notified to perform maintenance and repair in time to prevent the occurrence of faults and reduce the economic loss caused by equipment failure.Therefore,the system has certain application value for industrial equipment monitoring.