Current Issue : April - June Volume : 2018 Issue Number : 2 Articles : 5 Articles
In this paper, a fault detection and diagnosis (FDD) scheme is developed for a class of intensified HEX/reactor, inwhich faults caused\nby sensor, actuator, and process are taken into account in the unified framework. By considering overall heat transfer coefficient\nas a function of fouling and fluid flow rate, a dynamic model which is capable of identifying these two faults simultaneously is\nderived. Sensor measurements, together with estimation by adaptive high gain observers, are processed, aimed at identifying sensor\nfaults and providing adequate estimation to substitute faulty measurements. Then reliablemeasurements are fed to several banks of\ninterval filters to generate several banks of residuals; each bank of residuals is sensitive to a particular process parameter/actuator.\nBy evaluating these residuals, process/actuator fault isolation and identification are achieved. The proposed strategy is applied to\nactual data retrieved from a new intensified heat exchanger reactor. Simulation results confirm the applicability and robustness of\nthe proposed methodology...
This paper presents the method of synthesis of faults identification systems for electric servo actuators of multilink manipulators.\nThese actuators are described by nonlinear equations with significantly changing coefficients. The proposed method is based on\nlogic-dynamic approach for design of diagnostic observers for fault detection and isolation. An advantage of this approach is that\nit allows studying systems with nonsmooth nonlinearities by linear methods only. For solving the task of faults identification, a\nresidual signal feedback was proposed to be used for observers. The efficiency of the proposed fault identification system was\nconfirmed by results of simulation....
Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method\nfor support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the\nmixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients,\nkernel function parameters, and regression parameters are combined together as the parameters of the state vector.Thus, themodel\nselection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter\nto estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the\nkernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support\nvector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better\ngeneralization ability and higher prediction accuracy....
A fault detection, isolation, and estimation approach is proposed in this paper based on Interactive Multimodel (IMM) fusion\nfiltering and Strong Tracking Filtering (STF) for asynchronous multisensors dynamic systems. Time-varying fault is considered\nand a candidate fault model is built by augmenting the unknown fault amplitude directly into the system state for each kind of\npossible faultmode. By doing this, the dilemma of predetermining the fault extent asmodel design parameters in traditional IMMbased\napproaches is avoided. After that, the time-varying fault amplitude is estimated based on STF using its strong ability to track\nabrupt changes and robustness against model uncertainties. Through fusing information from multiple sensors, the performance\nof fault detection, isolation, and estimation is approved. Finally, a numerical simulation is performed to demonstrate the feasibility\nand effectiveness of the proposed method...
In order to diagnose sensor fault of aeroengine more quickly and accurately, a double redundancy diagnosis approach based on\nWeighted Online Sequential Extreme LearningMachine (WOS-ELM) is proposed in this paper.WOS-ELM, which assigns different\nweights to old and new data, implements weighted dealing with the input data to get more precise training models. The proposed\napproach contains two series of diagnosismodels, that is, spatialmodel and timemodel.The application of double redundancy based\non spatial and time redundancy can in real time detect the hard fault and soft fault much earlier.The trouble-free or reconstructed\ntime redundancy model can be utilized to update the training model and make it be consistent with the practical operation mode\nof the aeroengine. Simulation results illustrate the effectiveness and feasibility of the proposed method....
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