In order to improve work performance of M aglev vibration test systems,the relationships of operating parameters between different components and system were researched. The working principle of photoelectric displace...In order to improve work performance of M aglev vibration test systems,the relationships of operating parameters between different components and system were researched. The working principle of photoelectric displacement sensor was analyzed. The relationship between displacement of transducer and the infrared light area received by sensor was given. The method of expanding the dynamic range of vibrator was proposed,which makes dynamic range of Maglev vibrator doubled. By increasing the amplification of the amplifier,the sensitive photoelectric displacement sensor can be maintained. Two modes of operation of the controller were analyzed. Bilateral work of vibration test system designed can further improve the stability of the system.An object vibration was measured by Maglev vibration test system designed when different vibration exciter frequencies were loaded. Experiments showthat the output frequency measured by Maglev vibration test system and loaded are the same. Finally,the errors of test system were analyzed. These errors of vibration test system designed can meet the requirements of application. The results laid the foundation for the practical application of magnetic levitation vibration test system.展开更多
A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The pa...A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The parameters optimization of the sensor is essential for economic and efficient production.This paper proposes a method to combine an artificial neural network(ANN) and a genetic algorithm(GA) for the sensor parameters optimization.A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS,and then a GA is used in the optimization process to determine the design parameter values,resulting in a desired minimal nonlinearity error of about 0.11%.The calculated nonlinearity error is 0.25%.These results show that the proposed method performs well for the parameters optimization of the GECDS.展开更多
基金supported by the Chinese National Natural Science Foundation under Grant (No. 51377037)
文摘In order to improve work performance of M aglev vibration test systems,the relationships of operating parameters between different components and system were researched. The working principle of photoelectric displacement sensor was analyzed. The relationship between displacement of transducer and the infrared light area received by sensor was given. The method of expanding the dynamic range of vibrator was proposed,which makes dynamic range of Maglev vibrator doubled. By increasing the amplification of the amplifier,the sensitive photoelectric displacement sensor can be maintained. Two modes of operation of the controller were analyzed. Bilateral work of vibration test system designed can further improve the stability of the system.An object vibration was measured by Maglev vibration test system designed when different vibration exciter frequencies were loaded. Experiments showthat the output frequency measured by Maglev vibration test system and loaded are the same. Finally,the errors of test system were analyzed. These errors of vibration test system designed can meet the requirements of application. The results laid the foundation for the practical application of magnetic levitation vibration test system.
文摘A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The parameters optimization of the sensor is essential for economic and efficient production.This paper proposes a method to combine an artificial neural network(ANN) and a genetic algorithm(GA) for the sensor parameters optimization.A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS,and then a GA is used in the optimization process to determine the design parameter values,resulting in a desired minimal nonlinearity error of about 0.11%.The calculated nonlinearity error is 0.25%.These results show that the proposed method performs well for the parameters optimization of the GECDS.
