ANFIS Models for Fault Detection and Isolation in the Drive Train of a Wind Turbine

The paper aims to improve the fault detection and isolation process in wind turbine systems by developing intelligent systems that can effectively identify and isolate faults. Specifically, the paper focuses on the drive train part of a horizontal axis wind turbine machine. The proposed fault diagnostic strategy is designed using an adaptive neural fuzzy inference system (ANFIS), which is a type of artificial neural network that combines the advantages of both fuzzy logic and neural networks. The ANFIS is used to generate residuals that occur after faults have been detected, and to determine the appropriate thresholds needed to correctly detect faults. The simulation results show that the proposed fault diagnostic strategy is effective in detecting faults in the drive train part of the wind turbine system. By using intelligent systems such as ANFIS, the fault detection process can be automated and streamlined, potentially reducing maintenance costs and improving the overall performance and efficiency of wind turbine systems

Mobile robot visual navigation based on fuzzy logic and optical flow approaches

This paper presents the design of mobile robot visual navigation system in indoor environment based on fuzzy logic controllers (FLC) and optical flow (OF) approach. The proposed control system contains two Takagi–Sugeno fuzzy logic controllers for obstacle avoidance and goal seeking based on video acquisition and image processing algorithm. The first steering controller uses OF values calculated by Horn–Schunck algorithm to detect and estimate the positions of the obstacles. To extract information about the environment, the image is divided into two parts. The second FLC is used to guide the robot to the direction of the final destination. The efficiency of the proposed approach is verified in simulation using Visual Reality Toolbox. Simulation results demonstrate that the visual based control system allows autonomous navigation without any collision with obstacles.Peer ReviewedPostprint (author's final draft

Application De l’Apprentissage Par Renforcement Pour La Navigation D’un Robot Mobile

Dans l’industrie moderne, les robots mobiles occupent une place primordiale. Ces machines sont souvent appelés à effectuer plusieurs tâches, qui nécessitent l’évolution dans son environnement. Dans certains cas, l’acquisition des connaissances est une tâche difficile à réaliser. L’objectif de ce travail porte sur la commande d’un robot mobile en utilisant la technique d’apprentissage par renforcement. C’est une technique d’apprentissage à partir de l’expérience, en ne nécessitant qu’un signal scalaire comme retour indiquant la performance de l’action appliquée. Le signal de renforcement permet au navigateur d’ajuster sa stratégie pour améliorer ses performances. Tout d’abord, l’algorithme Q-learning avec des espaces d’états et d’actions discret est appliqué, puis des planificateurs locaux à base de la logique floue sont développés pour la navigation d’un robot mobile. Pour combiner les avantages des deux techniques, une stratégie de commande avec une capacité d’apprentissage est utilisée. C’est une extension de Q-learning aux cas continus, ou le système d’inférence floue, permettant l’introduction des connaissances disponibles à priori pour que le comportement initial soit acceptable. Mots clés : Apprentissage par renforcement, Q-learning, robot mobile, navigation, évitement d’obstacles

Navigation Autonome d’un Robot Mobile par des Techniques Neuro-Floues

Cette thèse traite le problème de navigation autonome d'un robot mobile par les techniques hybrides neuro-floues. L’objectif de travail présenté est d’étudier et développer des architectures de commande efficaces pour une navigation réactive d'un robot mobile autonome dans un environnement inconnu, en utilisant d’une part l’approche comportementale et d’autre part les méthodes de l’apprentissage. Les techniques employées pour aborder ce problème sont basées sur les systèmes d'inférence flous, les réseaux de neurones artificiels et l’apprentissage par renforcement. On a utilisé premièrement, les systèmes basés sur les comportements flous pour la planification locale et réactive, puis pour l'ajustement des paramètres des comportements flous, on a introduit les modèles hybrides neuro-flous pour la navigation autonome. La deuxième méthode d’apprentissage est particulièrement adaptée à la robotique, qui permet de trouver, par un processus d’essais et d’erreurs, l’action optimale à effectuer pour chacune des situations que le robot va percevoir afin de maximiser ses récompenses. Pour combiner les avantages de la logique floue et l'apprentissage par renforcement, une stratégie de commande avec une capacité d’apprentissage est utilisée, c’est une extension de Q-learning aux cas continus et une méthode d'optimisation des systèmes flous. L'avantage des systèmes flous est l’introduction des connaissances disponibles à priori pour que le comportement initial soit acceptable. L’efficacité des architectures proposées et étudiées sont démontrées par diverses applications de navigation autonome d'un robot mobile. This thesis deals with the autonomous navigation problem of a mobile robot using hybrid neurofuzzy techniques. The objective of the presented work is to study and develop effective architectures for a reactive navigation of an autonomous mobile robot in unknown environment, by using on the one hand the behavior based approach, and on the other hand the learning paradigm. The techniques employed to tackle this problem are based on the fuzzy inference systems, artificial neural networks and the reinforcement learning. Firstly, we used fuzzy behavior based navigation approach for the reactive and local planning, then for tuning the fuzzy behaviors parameters; we introduced the hybrid neuro-fuzzy models for autonomous navigation. The second type of learning method is particularly adapted to mobile robotic, which makes it possible to find by a process of tests and errors, the executed optimal action for each situation which the robot will perceive in order to maximize its rewards. To combine the advantages of fuzzy logic and reinforcement learning, a control strategy with a learning capacity is used, it is an extension of Q-learning to the continuous spaces and considered as and optimization method of fuzzy systems. The advantage of the fuzzy systems is the introduction of a priori knowledge in order to make the first behavior is acceptable. The effectiveness of the proposed and the studied architectures are demonstrated by various applications of the autonomous mobile robot navigation

Hybrid type-2 fuzzy logic obstacle avoidance system based on horn-schunck method

This paper is concerned with a visual navigation method based on type-2 fuzzy logic controllers (T2FLC) and optical flow (OF) approach. A Takagi-Sugeno fuzzy logic controller is used for obstacle avoidance task based on video acquisition and image processing algorithm. To extract information about the environment, the captured image is divided into two parts, the control system uses optical flow values calculated by a Horn-Shunk algorithm to detect and estimate the positions of obstacles. The efficiency of the proposed structure is simulated using Visual Reality Toolbox. The obtained simulation results demonstrate the effectiveness of this autonomous visual navigation systemPeer ReviewedPostprint (author's final draft

Hybrid type-2 fuzzy logic obstacle avoidance system based on horn-schunck method

This paper is concerned with a visual navigation method based on type-2 fuzzy logic controllers (T2FLC) and optical flow (OF) approach. A Takagi-Sugeno fuzzy logic controller is used for obstacle avoidance task based on video acquisition and image processing algorithm. To extract information about the environment, the captured image is divided into two parts, the control system uses optical flow values calculated by a Horn-Shunk algorithm to detect and estimate the positions of obstacles. The efficiency of the proposed structure is simulated using Visual Reality Toolbox. The obtained simulation results demonstrate the effectiveness of this autonomous visual navigation systemPeer Reviewe

Hybrid type-2 fuzzy logic obstacle avoidance system based on horn-schunck method

This paper is concerned with a visual navigation method based on type-2 fuzzy logic controllers (T2FLC) and optical flow (OF) approach. A Takagi-Sugeno fuzzy logic controller is used for obstacle avoidance task based on video acquisition and image processing algorithm. To extract information about the environment, the captured image is divided into two parts, the control system uses optical flow values calculated by a Horn-Shunk algorithm to detect and estimate the positions of obstacles. The efficiency of the proposed structure is simulated using Visual Reality Toolbox. The obtained simulation results demonstrate the effectiveness of this autonomous visual navigation system

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

This paper aims to present a robust fault diagnosis structure-based observers for actuator faults in the pitch part system of the wind turbine benchmark. In this work, two linear estimators have been proposed and investigated: the Kalman filter and the Luenberger estimator for observing the output states of the pitch system in order to generate the appropriate residual between the measured positions of blades and the estimated values. An inference step as a decision block is employed to decide the existence of faults in the process, and to classify the detected faults using a predetermined threshold defined by upper and lower limits. All actuator faults in the pitch system of the horizontal wind turbine benchmark are studied and investigated. The obtained simulation results show the ability of the proposed diagnosis system to determine effectively the occurred faults in the pitch system. Estimation of the output variables is effectively realized in both situations: without and with the occurrence of faults in the studied process. A comparison between the two used observers is demonstrated