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Showing papers by "Mahdi Aliyari Shoorehdeli published in 2017"


Journal ArticleDOI
TL;DR: A recurrent neural network coupled with Kalman filter is proposed to identify dynamic terms of robotic manipulator, and disturbance rejection and robustness tests admit capability of the method for online dynamic identification in the presence of output and dynamic perturbation.

31 citations


Journal ArticleDOI
TL;DR: In this article, a robust fault diagnosis scheme is developed for a class of nonlinear systems when both fault and disturbance are considered, which includes both component and sensor fault with nonlinear system that transferred to nonlinear Takagi-Sugeno (T-S) model.
Abstract: In this study, a novel robust fault diagnosis scheme is developed for a class of nonlinear systems when both fault and disturbance are considered The proposed scheme includes both component and sensor fault with nonlinear system that transferred to nonlinear Takagi-Sugeno (T-S) model It considers a larger category of nonlinear system when fuzzification is used for only nonlinear distribution matrices In fact the proposed method covers nonlinear systems could not transform to linear T-S model This paper studies the problem of robust fault diagnosis based on two fuzzy nonlinear observers, the first one is a fuzzy nonlinear unknown input observer (FNUIO) and the other is a fuzzy nonlinear Luenberger observer (FNLO) This approach decouples the faulty subsystem from the rest of the system through a series of transformations Then, the objective is to design FNUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method; meanwhile, FNLO is designed for faulty subsystem to generate fuzzy residual signal based on a quadratic Lyapunov function and some matrices inequality convexification techniques FNUIO affects only the fault free subsystem and completely removes any unknown inputs such as disturbances when residual signal is generated by FNLO is affected by component or sensor fault This novelty and using nonlinear system in T-S model make the proposed method extremely effective from last decade literature Sufficient conditions are established in order to guarantee the convergence of the state estimation error Thus, a residual generator is determined on the basis of LMI conditions such that the estimation error is completely sensitive to fault vector and insensitive to the unknown inputs Finally, an numerical example is given to show the highly effectiveness of the proposed fault diagnosis scheme

14 citations


Proceedings ArticleDOI
01 May 2017
TL;DR: This study introduced a novel extended adaptive thresholding based on mean-change point detection algorithm and shows that it is more efficient than other existing thresholding algorithm in the literature.
Abstract: Decision-making systems are known as the main pillar of industrial alarm systems, and they can directly effect on system's performance. It is evident that because of hidden attributes in the measurements such as correlation and nonlinearity, thresholding systems faced wrong separation defining by Missed Alarm Rate (MAR) and False Alarm Rate (FAR). This study introduced a novel extended adaptive thresholding based on mean-change point detection algorithm and shows that it is more efficient than other existing thresholding algorithm in the literature. Number hypothetical and industrial examples are given to delineate the capabilities and limitation of proposed method and prove its effectiveness in an industrial alarm system.

3 citations


Proceedings ArticleDOI
01 May 2017
TL;DR: In this paper, a new sensor fault detection approach based on nonlinear parity technique in presence of sensor noise is presented, which can be applied to detect sensor fault in the nonlinear affine systems with mentioned class.
Abstract: In This study, we present a new sensor fault detection approach based on nonlinear parity technique in presence of sensor noise. Conventionally analytical redundancy (AR) was used to fault detection and isolation in linear systems. The proposed parity space approach with nonlinear analytical redundancy (NLAR) technique can be applied to detect sensor fault in the nonlinear affine systems with mentioned class. The proposed approach will be implemented in pH neutralization system. At the end nonlinear fault detection and identification algorithm will be successfully implemented, examined and reported.

3 citations


Proceedings ArticleDOI
01 May 2017
TL;DR: In this paper, a model based fault detection of gas turbine using linear and non-linear methods (multilayer perceptron and radial basis function neural network models) is studied.
Abstract: In this paper, model based fault detection of gas turbine using linear and non-linear methods (multilayer perceptron and radial basis function neural network models) is studied. We contemplate IGV positions and gas flow as input and sensors related to compressor as outputs. Then residual signals will be obtained based on system model. In addition, by these signals and exert the fixed and adaptive thresholds, the fault occurred in the V94.2 gas turbine which is pollution of vane compressor (Fouling detection) has identified and diagnosed. Consequently, by comparing the obtained results from different fault detection methods, we determine the most appropriate signal output that led to better and reliable result. All simulations have been carried out by using real data taken from an V94.2 industrial gas turbine 927 power plant in Fars.

2 citations


DOI
01 Jan 2017
TL;DR: This study introduced a novel extended adaptive thresholding based on mean-change point detection algorithm and shows that it is more efficient than other existing thresholding algorithm in the literature.
Abstract: Decision-making systems are known as the main pillar of industrial alarm systems, and they can directly effect on system's performance. It is evident that because of hidden attributes in the measurements such as correlation and nonlinearity, thresholding systems faced wrong separation defining by Missed Alarm Rate (MAR) and False Alarm Rate (FAR). This study introduced a novel extended adaptive thresholding based on mean-change point detection algorithm and shows that it is more efficient than other existing thresholding algorithm in the literature. Number hypothetical and industrial examples are given to delineate the capabilities and limitation of proposed method and prove its effectiveness in an industrial alarm system.

2 citations


Proceedings ArticleDOI
01 May 2017
TL;DR: In this article, a novel fuzzy unknown input observer for robust fault estimation scheme is developed when both faults and unknown input are considered, which decouples the faulty subsystem from the rest of the system through a series of linear transformations.
Abstract: In this study, a novel fuzzy unknown input observer for robust fault estimation scheme is developed when both faults and unknown input are considered. The proposed scheme includes component fault with nonlinear distribution matrix in state equation, unknown input signal in state and output equations. After that, Takagi-Sugeno (T-S) model is used to create multiple models. While T-S model is used for only the nonlinear distribution matrix of the fault signal, a larger category of nonlinear system will be included. Two set of observers are considered, the first one is extended fuzzy unknown input observer (EFUIO) and the other one is fuzzy sliding mode observer (FSMO). The approach decoupled the faulty subsystem from the rest of the system through a series of linear transformations. Then, the objective is to design EFUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method. Unknown input is removed; meanwhile, FSMO is designed for faulty subsystem to guarantee estimation of fault. Sufficient conditions are established in order to guarantee the convergence of the state estimation error and the results are formulated in the form of linear matrix inequalities (LMIs). Finally, a simulation study on an electromagnetic suspension system (EMS) is presented to demonstrate the performance of the results compared with a pure SMO.

1 citations


Proceedings ArticleDOI
01 May 2017
TL;DR: These two methods, Genetic Algorithms and biological model of neuron, merge together for designing a novel structure of hidden layers and weights by using biological neuron model of Izhikevich.
Abstract: In the recent years, artificial neural network have been used to improvement of system identification. The performance of neural network directly depends on the hidden layer, which include weights and activation functions of the network. In addition Genetic Algorithms are used to learn of neural network as a type of evolutionary computing algorithms. In this paper, the structure of hidden layers and weights are modified by using biological neuron model of Izhikevich. These two methods, Genetic Algorithms and biological model of neuron, merge together for designing a novel structure.

1 citations