Mahdi Aliyari Shoorehdeli

Bio: Mahdi Aliyari Shoorehdeli is an academic researcher from K.N.Toosi University of Technology. The author has contributed to research in topics: Fuzzy control system & Control theory. The author has an hindex of 20, co-authored 157 publications receiving 1812 citations. Previous affiliations of Mahdi Aliyari Shoorehdeli include Islamic Azad University, Science and Research Branch, Tehran & Islamic Azad University.

A novel incipient fault detection based on residual evaluation by using correlation dimension and inverse of largest Lyapunov exponent

Abstract: One of the most significant challenges in fault detection and isolation methods is early detection of faults with very slow development rate. This paper proposes a novel robust residual evaluation to detect an incipient fault. Residual evaluation with largest Lyapunov exponent and correlation dimension is proposed to perform early fault detection. Since the residual in fault free case and independent identical distribution (i.i.d) Gaussian noise are typically similar, an analytical formula for largest Lyapunov exponent of i.i.d Gaussian noise with length of $$ N $$ is presented. The incipient faults of DAMADICS benchmark actuator are used to test the proposed approach; which the achieved simulation results confirm the method could detect incipient faults earlier.

Robust fuzzy fault estimation based on decoupled transform and unknown input sliding mode observer

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.

Estimating CO Conversion Values in the Fischer-Tropsch Synthesis Using LoLiMoT Algorithm

Abstract: In this paper, a new method for estimation of CO conversion in a range of temperatures, pressures and H2/CO molar ratios in the Fischer-Tropsch (FT) synthesis based on Locally Liner Model Tree (LoLiMoT) has been introduced. LoLiMoT is an incremental tree-construction algorithm that partitions the input space by axis-orthogonal splits. In each iteration two new local models as the result of splitting the worst local model has been inserted into the previous structure and result decreasing the total error. The system has been evaluated through two methods and results show estimated CO conversion values by LoLiMoT are in good agreement with experimental data.

Real-time dynamic HIL simulator of gas turbine, governor, generator and grid for static excitation of a 200-MVA synchronous generator

Abstract: A real-time dynamic hardware-in-loop (HIL) simulator of an RTX real-time subsystem (RTSS) was developed by using LabVIEW (G language). The main idea of this work was to determine the feasibility and accuracy of widely available and highly competitive commercial products, such as personal computers on an RTSS, as an alternative to conventional prohibitive real-time simulators in dynamic studies of power systems. The implemented system is a self-contained heavy-duty gas turbine, governor, synchronous 200-MVA, 15.75-kV machine and a simplified electrical network. The HIL simulator was customized to interact with a 1518-kW static exciter. The role of this HIL simulator is to provide real-time digital and analog signals for static exciter systems (SES) and to simulate the mechanical and electrical components in a closed-loop, fixed-step solver applied by a well-known numerical solution method. This sophisticated yet exceptionally economic HIL simulator provides engineers with a safe environment to analyze the dynamic performance of static exciters and investigate their natural restraints and functionalities. It also provides a safe environment to analyze some naturally destructive tests.

Control and synchronization of chaotic spur gear system using adaptive non-singular fast terminal sliding mode controller

Abstract: This study reports a novel adaptive non-singular fast terminal sliding mode controller for the tracking control and synchronization of a chaotic spur gear system. The proposed novel control law attenuates the chattering phenomena of the conventional sliding mode controller. In addition, a non-singular fast terminal sliding mode surface is employed to remove the singularity problem, increase the convergence rate, and guarantee finite-time convergence. An extreme learning machine (ELM) neural network is utilized to estimate the unknown dynamics of the spur gear system and the reaching law coefficients; hence, this control scheme is a combination of the direct and indirect adaptive control. The adaptation rules of the ELM are derived based on the Lyapunov stability theorem to ensure closed-looped stability. Finally, some different numerical simulations are considered to check the validity and efficiency of the proposed control strategy compared with other control methods.

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Learning from class-imbalanced data

Abstract: 527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

A Survey on Evolutionary Computation Approaches to Feature Selection

Abstract: Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.