Study On Multiple Sensor Information Modeling & Dynamic Compensation

Real-time and high precision modeling &dynamic compensation method is a key to realize online and high precision measurement of manufacturing process, it has important academic value and practical significance in promoting the development of advanced manufacture and instrument technology. With the title"Study on Multiple Sensor Information Modeling &Dynamic Compensation", the thesis systematically studies sensing information preprocessing, decoupling, prediction compensation method, and farther develops a networking measurement system, carries through correlative experiments and primary application. The thesis is supported by Guangzhou Science and Technology Planning Project (No.2005Z3-D0341) and Industry-Academy-Research Project of Education Ministry (2007A090302039).The thesis first analyzes the domestic and international researches on sensing information preprocessing &modeling method. It confirms the thesis will use correlation analysis, data reduction and multiple regressions ability of PLS and NPLS method to realize sensing information preprocessing and nolinar-modeling. And then the thesis combines time series analysis method ,curve fitting and wavelet-multiscale method together to develop online-rapid decoupling and prediction compensation method of sensing information The main work includes the following parts:I. It studies a NPLS preprocessing and modeling method based on outer-inner polynomial model. Before outer-inner polynomial NPLS modeling, importing variable selection based PLS can make the outer-inner polynomial NPLS modeling method have more practical significance.II. It studies a multiple sensor information variable selection method based on VIP-PLS regression coefficient. The method considers synthetically VIP index and PLS regression coefficient interpretative action on independent variables, and differing from the former method with single VIP filtration index, it doesn't take place the phenomena of wrong filtration easily. And the method brings forward using error incrementΔEl of PLS model as variable filtering index, it needn't review each independent variable's importance, and it has virtue of small calculation work.III. It studies a double non-linearization PLS regression modeling method based on outer-inner polynomial model. The built model is explicit, steady and can express non-linear relation between explanatory variables and responsive variables, explanatorily latent variables and responsively latent variables, and among responsive variables commendably, it solves problem about the nonlinear terms is hard to confirmed in modeling process of outer polynomial NPLS model.IV. It brings forward a handy scale estimation method of multi-sensing information. The method just processes an N ( N≥6) scale decomposing for all sensing information, and works out resolution errorεij and resolution error thresholdξ, then it can fulfill scale estimation of multi-sensing information variables, its process is relatively simple.V. It brings forward a adaptive interpolation decoupling method of multi-sensing information based on scale approximation. The method select different interpolation method to fulfill decoupling calculation of multi-sensing information by resolution of each sensing information and resolution thresholdδcalculated under preestimating precision target.VI. It brings forward a dynamic prediction model of sensing information based on wavelet calculation to improve dynamic sensing characteristic. Based on multi-resolution approximation tree principle, the model usesàtrous arithmetic to process online wavelet-decomposing calculation, it can restrain noise disturbance effectively in virtue of low-pass filtering effect of wavelet analysis. The model uses Sliding Window Polynomial Model (SWPM) arithmetic, and Recursive Estimator based on Parallel Kalman (REPK) arithmetic of AR prediction model to dynamically predict scale information and detail information respectively, it can make use of each decomposed information's characteristic effectively and improve dynamic performance of sensing system.VII. It systemically studies REPK arithmetic and composite-scroll prediction arithmetic. REPK arithmetic uses two Kalman filter to recursively identify parameters of AR model and optimally estimate true signal in time-varying data, it uses fresh resolution information d j ,tof measurement data to real-time amend parameters of AR model and predict, the arithmetic has good calculation consistency and convergence, and can be extended to prediction of other stationary time series signal. The proposed composite-scroll prediction arithmetic can overcome long interval problem in direct multi-step prediction method about prediction of long delay sensing information, it divides a long-time prediction to several direct multi-step prediction, and it starts from measured data, uses forward prediction data to amend parameters of afterward prediction model, and the final prediction data is scroll-amendatorily calculated from measured data, it decreases prediction error.VIII. Combined with request of measurement generalization, intelligentization and networking, it designs a networking structure model of measurement system based on embedded intelligent agent. The intelligent agent is used for networking measurement of multi-sensing information, it uses DSP and ARM as kernel chip, transforms all sensing information to frequency signal, and it increases antijamming ability of signal. The agent realizes the design of network communication by ARM embedded mini-internet technology, imports IPv6 communication mode in uClinux operation system, and it increases security, reliability and expansibility of communication. It also discusses resolvents of several pivotal technology about soft structure and operational mechanism, cross platform exchange technology based on XML, and real-time database technology of measurement platform based on XML data.The thesis also carries through correlative emulational experiments and applicational trial. The emulational result shows the NPLS preprocessing and modeling method based on outer-inner polynomial model can improve predictional precision(improves 56.2% and 24.7% respectively) with less fitting independent variables. After resolution thresholdδis calculatedδ=2-4 under preestimating precision targetθ=0.1%, the decoupling time of proposed adaptive interpolation decoupling method is 50.4 ms, the decoupling method has good generalization, convergence and rapid calculation speed compared with NN decoupling method. Useing wavelet rapid calculation arithmetic, the dynamic prediction model of sensing information based on wavelet calculation processes one time decomposition need 54.3ms, one time prediction compensation need 127.0ms, it has good real-time characteristic, and its precision is 0.538% when it uses direct three-step prediction method for low delay sensing information. After the decoupling and dynamic prediction compensation technology is used, the primary application result in measurement of ferment liquid and ethanol rectification shows the networking measurement system based on embedded intelligent agent has high measurement precision and good real-time characteristic, the maximal measurement error of liquid ethanol concentration is±1.9%, and improves responsing time of ethanol sensors from 20s to 1.3s. these show the researched theory and methods are correctness and validity, and can be extended to other advanced manufacturing processes.