Gas Recognition And Detection Of Thin Film Bulk Acoustic Sensor Arrays

All kinds of toxic and harmful gases emitted from industrial production and living are a great threat to people's healthy work and life.The study of toxic and hazardous gases and identification technologies are of great significance in creating a healthy,healthy,secure and comfortable environment.The technology requires accurate prediction of the type and concentration of the gas to be measured,so that humans can respond quickly and effectively to relevant problems,ensuring that people live in a safe and healthy living space.In this paper,the multi-sensor array system composed of FBAR quality sensitive devices is used to predict the five gas types and concentrations,and the collected gas signals are analyzed by principal component analysis and neural network.It mainly includes the following points:1.Gas test of film bulk acoustic wave resonator:The FBAR is used as the core detection system to test the relevant gas,analyze and process the collected gas response data,and evaluate the relevant performance(such as sensitivity,selectivity and detection limit)of the sensor.2.Qualitative identification of gases based on principal component analysis and image methods:a basic introduction to principal component analysis,including its basic principles,mathematical derivation,and processing,and using this method to qualitatively identify a single gas.The pattern formed by the gas shift and sensitivity parameters is then used to visually identify the gas species.3.Quantitative Recognition of Neural Network Qualitative Recognition and Curve Fitting:Explain the relevant content of neural network and qualitatively identify a single gas based on BP neural network,and then use the relevant curve fitting method to judge the concentration of the corresponding gas.The conclusion shows that the FBAR sensor used in this experiment has high performance(high sensitivity,good selectivity,low detection limit,etc.).The identification method used can effectively predict the gas to be tested(acetone,acetic acid,n-octane,toluene,ethanol):the classification gas based on principal component analysis and pattern recognition is simple to visualize,the accuracy of neural network qualitative prediction is higher than 90%,the curve fitting quantitative identification error is less than 8%.