Research On Key Techniques Of Self-validating Multifunctional Sensor

The self-validating multifunctional sensor is a novel sensor, which can detectmultiple measurands as well as validate its own status. Its main functions are asfollows: detect and isolate multiple faults of sensors; replace the incorrectmeasurements by an optional estimated value to implement the data recovery underfaults; indicate the accurate range of the validated mearurements value by means ofon-line validated uncertainty (VU); evaluate the health levels of the multifunctionalsensors. Our research is supported by National Natural Science Foundation of China,and it centers on the status self-validation approaches to solve several key issues ofself-validating multifunctional sensors. Major work has been done as follows:To verify the feasibility of the status self-validation methods, a self-validatingmultifunctional sensor experimental system is designed. The faults mechanism andmodes of all the sensitive units are analyzed; the faults simulation and additioncircuits are designed to produce and simulate real failures. The status self-validationfunctions are then tested and demonstrated under multiple faults.Aiming at the on-line multi-fault detection and recovery of self-validatingmultifunctional sensors, the principal component analysis (PCA) coupled withwavelet relevance vector machine (WRVM) strategy is studied to implement thedata validation, which has been expanded from traditional single fault to multipleones. The inner relationship among sensitive units is analyzed deeply by using PCAtheory, and then multi-faults detection issue as well as the PCA-based faultsdetection ability is stuied by means of monitoring the projection changes of testsamples in residual space. Compared with the predictive performance of relevancevector machine (RVM) under different kernel types such as radical basis function,Mexican Hat, and Morlet wavelet, the Mexican Hat-based WRVM predictor isselected to improve the generalization ability, training speed and performance ofsuppressing noise. The WRVM is emplyed to implement on-line multiple faultsisolation and recovery. Compared with neural network methods, WRVM canachieve higher accuracy and fewer burdens, and it is suitable for data recoveryunder small sample.Aiming at the signal reconstruction and on-line VU estimation of self-validating multifunctional sensors, the multivariate RVM (MVRVM) and validatedrandom fuzzy variables (VRFV) are propsoed to compute the valiated measurmentvalues and VU respectively. Under the small sample and non-linear problem ofmultifunctional sensor reconstruction, the status self-validation efficiency is stuied by using the MVRVM-based reconstruction mode, which has the goodgeneralization ability and can output multiple values with one model simultaneously.Compared with the compound multi-RVM model, MVRVM-based reconstructiontechnology has a30%reduction in computation burden. To solve the on-lineuncertainty estimation, the relationship between α cutsof the proposed VRFVand measurement uncertainty is deeply analyzed and the negative effects fromdifferent faults types are also studied. The VRFV-based VU estimation methodsunder both fault-free and faults situation are studied. The fuzzy logic rules are quitesimple; therefore, the computation burden among VRFVs is low enough to allow anonline estimation of the uncertainty. The experiment results have verified thatVRFV is very suitable for on-line VU estimation and the validity has been provenby the traditional GUM method under normal situation.Aiming at the novel health evaluation function of self-validatingmultifunctional sensors, the health reliability degree (HRD) is proposed to describethe health level in a quantitative way. The measurement value status estimation ofsingle sensitive unit in traditional self-validating technology has been expanded tothe comprehensive health estimation of multifunctional sensor, in which therelationship among multiple sensitive units is deeply analyzed. From views of singlesensitive unit and overall multifunctional sensor, the HRD concept and methodologyare emphasized to express the health level in a more direct way. Experimentalresults show that HRD is quite suitable for the quantitative health evaluation, and ithas a rapid response to the health changes of multifunctional sensor on differenthealth levels.