Research And Design Of Double Tuning Fork Temperature And Humidity Sensor

With the development of China’s aerospace industry,EXTRAVehicular spacesuit is an important life support device for astronauts during their walking activities.Real-time monitoring of the temperature and humidity changes of Eva space suit plays an important role in the life support system of space suit.Due to the influence of the space of the cabin,the sensor volume,power consumption,precision and other characteristics put forward higher requirements.In this paper,based on quartz tuning fork resonator combined with GO material,a double-tuning fork resonant temperature and humidity sensor was studied,and a BP neural network temperature compensation model was established and optimized to realize the temperature compensation of humidity response of the dual-tuning fork temperature and humidity sensor combined with FPGA.Firstly,according to the relevant theories of quartz piezoelectric materials,the mode simulation analysis of the quartz tuning fork resonator was carried out by using ANSYS software to analyze the physical and chemical properties of go materials and the moisture sensing characteristics of water molecules.The GO was coated on the AT-cut quartz tuning fork arm to make humidity sensitive elements.According to the XY-cutting quartz tuning fork temperature sensitive characteristics,the manufacture of temperature sensitive elements;The temperature resonator and humidity resonator are encapsulated with ABS shell and resin material,and the prototype design of the sensor is completed.Secondly,temperature compensation of BP neural network model is set up,implement the humidity measuring temperature compensation,from fish scale,vision step length,improved AFSA algorithm in three aspects: the selection of BP neural network is optimized,the compensation error reduced from 5.50% RH to0.11% RH,the training time reduced to 91.4 s,the BP neural network to make use of the FPGA implementation,compared with PC,the FPGA computing speed is120 μs.Finally,a sensor testing platform was built to test the temperature and humidity sensor with double tuning fork.The test results showed that the temperature range of the sensor was-20℃～100℃,the temperature sensitivity was-1.61Hz/℃,the nonlinear error was 1.87%,the minimum temperature resolution was 0.0067℃,and the temperature response time & LT.2s at room temperature,in the range of 11.3%～98.4%RH,the sensitivity of the sensor is about-1.102Hz/(%RH),the nonlinear error is about 4.2%,the minimum resolution is 0.6926%RH,the hysteretic error is 6.08%,the response time and recovery time are around 80 s and 110 s respectively,and the sensor has good repeatability.In the range of temperature change from 20℃ to 51℃,after temperature compensation by BP neural network,the maximum humidity response error of the humidity sensor is 0.45%RH,which verifies the effectiveness of temperature compensation by BP neural network.