Research On Operating State Evaluation System Of Pneumatic Actuator

Pneumatic actuator with compressed air as the working medium has the advantages of simple structure,small pollution,etc.and is consequently widely used in automobile production line,electronic manufacturing.However,once the pneumatic actuator fails,it will not only affect the stable operation and production efficiency of the system,but also cause economic losses and even jeopardize the personal safety of the staff.Therefore,monitoring and management of pneumatic actuators’ operation state and evaluation of pneumatic actuators’ future state are of great value in theories and engineering application.In order to realize the safe and stable operation of pneumatic actuator,this thesis studies the operating state evaluation of pneumatic actuator and forecast theirs trend of operation in the future.The main research contents include:In this thesis,the fatigue failure test platform of pneumatic actuator is designed,and the test environment for evaluating the operating state of pneumatic actuator is built.In view of the redundancy of operating state data,principal component analysis is used to preprocess the operating state data.In order to extract the fault information in the data after pretreatment,characteristic analysis is carried on the processed operating state evaluation data,characteristics are screened according to their sensitivities to the occurrences of failures;at the same time,combined with the analysis results of the historical operating state of the pneumatic actuator,the selected operating state evaluation characteristic quantities are analyzed to realize the research of the operating state evaluation method.The experimental results verify the effectiveness of the method.In view of the non-linearity and time sequence of the selected characteristics,the long short-term memory neural network algorithm was selected to establish the prediction model of the characteristics of the pneumatic actuator operating state evaluation.Meanwhile,in order to improve the prediction accuracy of the model,the adaptive particle swarm optimization algorithm was selected to optimize the parameters of the long short-term memory neural network.Compared with BP neural network and long short-term memory neural network,the simulation results show that this algorithm has higher prediction accuracy.Finally,combined with the method of operation state evaluation,the operation state evaluation of pneumatic actuator is realized.In order to realize the real-time supervision and remote management of pneumatic actuator’s operating state,visual studio 2013 and SQL sever 2008 database platform were used to design the operating state evaluation system of pneumatic actuator.This system has the functions of operation state real-time monitoring,historical operation state display,fault early warning,remote parameter setting,enhances the security performance of pneumatic actuator in the process of operating.In this thesis,the experimental research is also carried out.The experimental results verify that the operating state evaluation method is effective and the designed operating state evaluation system of pneumatic actuator is feasible.This thesis is of practical engineering significance for stable operation and efficient management of pneumatic actuator and lay a certain foundation for the further in-depth research on the operating state evaluation of pneumatic actuator.