In this paper a novel process monitoring scheme is proposed because of the importance of fault detection and identification in industrial processes. In this method, process dynamic and effect of outliers are considered concurrently. First, the proposed approach uses CVA More
In this paper a novel process monitoring scheme is proposed because of the importance of fault detection and identification in industrial processes. In this method, process dynamic and effect of outliers are considered concurrently. First, the proposed approach uses CVA method to implement the process dynamic. Then ICA method is performed for dimension reduction of data. The outliers elimination and control limit calculation are based on the Local Outlier Factor algorithm. This algorithm doesn’t consider a special distribution for process variables, thus conforming to data in real industrial processes. The proposed method is applied to fault detection in the Tennessee Eastman process. Results clearly indicate better performance of the proposed scheme compared to the alternative methods.
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Energy hub (EH) concept is widely proposed for integrating different types of energy infrastructures. EH physically consists of some storage systems and converters receiving energy from multiple sources immediately from its upper grids and provides energy services for u More
Energy hub (EH) concept is widely proposed for integrating different types of energy infrastructures. EH physically consists of some storage systems and converters receiving energy from multiple sources immediately from its upper grids and provides energy services for ultimate consumers. In this paper a state space model for EH system is proposed. Due to the dynamic behavior loads and the price uncertainties, a Model Predictive Control approach is suggested for optimal performance. The proposed method is studied on a EH that consists of transformer, boiler, CHP, electrical and heat storages considering demand side management. Finally, the simulation results depicts to demonstrate the effectiveness of the proposed method for optimal operation of the EH.
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Fault occurrence in real operating systems usually is inevitable and it may lead to performance degradation or failure and requires to be meddled quickly by making appropriate decisions, otherwise, it could cause major catastrophe. This gives rise to strong demands for More
Fault occurrence in real operating systems usually is inevitable and it may lead to performance degradation or failure and requires to be meddled quickly by making appropriate decisions, otherwise, it could cause major catastrophe. This gives rise to strong demands for enhanced fault tolerant control to compensate the destructive effects and increase system reliability and safety in the presence of faults. In this paper, an approach for estimation and control of simultaneous actuator and sensor faults is presented by using integrated design of a fault estimation and fault tolerant control for time-varying linear systems. In this method, an unknown input observer-based fault estimation approach with both state feedback control and sliding mode control was developed to assure the closed-loop system's robust stability via solving a linear matrix inequality formulation. The presented method has been applied to a linear parameter varying system and the simulation results show the effectiveness of this method for fault estimation and system stability.
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