Fault prediction for a class of unknown-model multivariate continuous processes with a hidden fault was studied,and a solution was given based on statistical process monitoring(SPM)approach.A principle component analysis(PCA)model using sample data under normal state was built,then the characteristic value for fault prediction was constructed,and time series analysis and prediction were applied to the characteristic value to predict the remaining useful life(RUL)of the system.Aiming at the linear time invariant system,a characteristic value was proposed and the prediction error of RUL was analyzed under some assumptions for system structure and hidden fault.A case study on a CSTR showed the efficiency of the proposed approach.
Robust H-infinity filtering for a class of uncertain discrete-time linear systems with time delays and missing measurements is studied in this paper.The uncertain parameters are supposed to reside in a convex polytope and the missing measurements are described by a binary switching sequence satisfying a Bernoulli distribution.Our attention is focused on the analysis and design of robust H-infinity filters such that,for all admissible parameter uncertainties and all possible missing measurements,the filtering error system is exponentially mean-square stable with a prescribed H-infinity disturbance attenuation level.A parameter-dependent approach is proposed to derive a less conservative result.Sufficient conditions are established for the existence of the desired filter in terms of certain linear matrix inequalities(LMIs).When these LMIs are feasible,an explicit expression of the desired filter is also provided.Finally,a numerical example is presented to illustrate the effectiveness and applicability of the proposed method.
