scholarly journals Fault Diagnosis for a Class of Robotic Systems with Application to 2-DOF Helicopter

2020 ◽  
Vol 10 (23) ◽  
pp. 8359
Author(s):  
Luis Alejandro Ramírez ◽  
Manuel Alejandro Zuñiga ◽  
Gerardo Romero ◽  
Efraín Alcorta-García ◽  
Aldo Jonathan Muñoz-Vázquez
Keyword(s):  
Fault Diagnosis ◽  
Original System ◽  
Research Area ◽  
Fault Detection And Isolation ◽  
Specific Class ◽  
Sensors And Actuators ◽  
Challenging Research ◽  
Classic Approach ◽  
Generalized Hamiltonian System ◽  
Two Degree Of Freedom

This paper considers a general approach to fault diagnosis using a generalized Hamiltonian system representation. It can be considered that, in general, nonlinear systems still represent a problem in fault diagnosis because there are results only for a specific class of them. Therefore, fault diagnosis remains a challenging research area despite the maturity of some of the available results. In this work, a type of nonlinear system that admits a generalized Hamiltonian representation is considered; in practice, there are many systems that have this kind of representation. Thereupon, an approach for fault detection and isolation based on the Hamiltonian representation is proposed. First, following the classic approach, the original system is decoupled in different subsystems so that each subsystem is sensitive to one particular fault. Then, taking advantage of the structure, a simple way to design the residuals is presented. Finally, the proposed scheme is validated at the two-degree of freedom (DOF) helicopter of Quanser®, where the presence of faults in sensors and actuators were considered. The results show the efficacy of the proposed scheme.

scholarly journals Estimation and Fault Diagnosis of Lithium-Ion Batteries: A Fractional-Order System Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shulan Kong ◽  
Mehrdad Saif ◽  
Guozeng Cui
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fractional Order ◽  
Sliding Mode ◽  
Estimation Error ◽  
Lithium Ion ◽  
Fault Isolation ◽  
Sliding Mode Observer ◽  
Fault Detection And Isolation ◽  
Order System

This study investigates estimation and fault diagnosis of fractional-order Lithium-ion battery system. Two simple and common types of observers are designed to address the design of fault diagnosis and estimation for the fractional-order systems. Fractional-order Luenberger observers are employed to generate residuals which are then used to investigate the feasibility of model based fault detection and isolation. Once a fault is detected and isolated, a fractional-order sliding mode observer is constructed to provide an estimate of the isolated fault. The paper presents some theoretical results for designing stable observers and fault estimators. In particular, the notion of stability in the sense of Mittag-Leffler is first introduced to discuss the state estimation error dynamics. Overall, the design of the Luenberger observer as well as the sliding mode observer can accomplish fault detection, fault isolation, and estimation. The effectiveness of the proposed strategy on a three-cell battery string system is demonstrated.

scholarly journals Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

2019 ◽  
Vol 9 (4) ◽  
pp. 783 ◽  
Author(s):  
Silvio Simani ◽  
Paolo Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Offshore Wind ◽  
Data Driven ◽  
Offshore Wind Turbine ◽  
Wind Turbine System ◽  
High Degree ◽  
Offshore Plants

Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.

Implementation of Fault Tolerant Control Scheme for Point Kinetic Reactor Model With Temperature Feedback in Matlab

2016 ◽  
Author(s):  
Huan He
Keyword(s):  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Fault Detection And Isolation ◽  
Fault Estimation ◽  
Coolant Temperature ◽  
Fuel Temperature ◽  
Sensors And Actuators ◽  
Reactor Model ◽  
Critical System ◽  
Advanced Control

As a highly safety-critical system, it is insufficient for the Nuclear Power Plant (NPP) Instrumentation and Control (I&C) system to simply rely on a conventional control schemes or controllers which only satisfy stability and performance specification to the perturbation of the nominal plant. Since the current operating or newly-built I&C systems are based on transferring or adapting modern high performance electronic devices, it provides the hardware foundation and possibility to incorporate more advanced control systems into nuclear systems to achieve higher safety and stable performance, even in unexpected faulty situations. Active Fault Tolerant Control (FTC) is one of the choices for such advanced control. Active FTC encompasses the following components: 1. nominal controller design, 2. sensors and actuators fault detection and isolation, and 3. fault estimation and fault accommodation. In this research, approaches for each component are integrated into an active FTC scheme. Following this, the active FTC scheme is applied to a point kinetic rector model with fuel temperature and coolant temperature effect to reactivity. Simulation results show that the active FTC scheme designed in this research can effectively track the global power set point, even under situations with single fault from actuator or sensors.

Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
Z. N. Sadough Vanini ◽  
N. Meskin ◽  
K. Khorasani
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Sensor Data ◽  
Turbine Engines ◽  
Multiple Model ◽  
Jet Engines ◽  
Dynamic Neural Networks ◽  
Autoassociative Neural Networks

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation scheme.

Building Intelligent Robotics Systems with Distributed Components

2006 ◽  
Vol 10 (2) ◽  
pp. 173-180 ◽  
Author(s):  
Federico Guedea-Elizalde ◽  
◽  
Rogelio Soto ◽  
Fakhreddine Karray ◽  
Insop Song ◽  
...  
Keyword(s):  
System Development ◽  
Classical Problem ◽  
Research Area ◽  
Specific Class ◽  
Robot Arm ◽  
Systems Planning ◽  
Distributed Components ◽  
Environment Recognition ◽  
Integration Problem ◽  
Abstract Interface

Building an intelligent robot system has been an extensive research area. There are many advances in components needed to construct the robotic system, such as vision systems, sensory systems, planning systems, among others. Integration of this components represents a big challenge for robot designers, due to they come from different vendors and with different interfaces or operating systems. This is more difficult if the overall system development has to deal with environmental uncertainties or changing conditions. In these cases, new tools and equipment are necessary to adapt the initial configuration to the new changing requirements. Each added component increases the complexity of the system due to the interconnection required with the previous components. In this work, we present an approach to solve this integration problem using concepts of distributed computing areas. We named this concept Wrapper Components. This concept is based on a standard middleware software specification. Wrapper components are object-oriented modules that create an abstract interface for a specific class of hardware or software components. If these components provide “intelligent” functions, the overall system is capable of show some basic smart behavior through specific actions to react under changes in the environment. We tested our approach by solving an experimental classical problem named block-world. The intelligent functions are object recognition, environment recognition, planning, tracking capabilities and robot arm control.

D-Wave and predecessors: From simulated to quantum annealing

2014 ◽  
Vol 12 (03) ◽  
pp. 1430002 ◽  
Author(s):  
Eliahu Cohen ◽  
Boaz Tamir
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Large Scale ◽  
Quantum Computer ◽  
Research Area ◽  
Hill Climbing ◽  
Quantum Annealing ◽  
Challenging Research ◽  
Computer Based ◽  
D Wave

On May 2011, D-Wave Systems Inc. announced "D-Wave One", as "the world's first commercially available quantum computer". No wonder this adiabatic quantum computer based on 128-qubit chip-set provoked an immediate controversy. Over the last 40 years, quantum computation has been a very promising yet challenging research area, facing major difficulties producing a large scale quantum computer. Today, after Google has purchased "D-Wave Two" containing 512 qubits, criticism has only increased. In this work, we examine the theory underlying the D-Wave, seeking to shed some light on this intriguing quantum computer. Starting from classical algorithms such as Metropolis algorithm, genetic algorithm (GA), hill climbing and simulated annealing, we continue to adiabatic computation and quantum annealing towards better understanding of the D-Wave mechanism. Finally, we outline some applications within the fields of information and image processing. In addition, we suggest a few related theoretical ideas and hypotheses.

Robust Unknown Input Observer for Nonlinear Systems and Its Application to Fault Detection and Isolation

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
S. Mondal ◽  
G. Chakraborty ◽  
K. Bhattacharyya
Keyword(s):  
Fault Diagnosis ◽  
Nonlinear System ◽  
Fault Detection ◽  
Nonlinear Function ◽  
Fault Detection And Isolation ◽  
Linear Matrix ◽  
Unknown Input ◽  
Matrix Inequality ◽  
Unknown Input Observer ◽  
Linear Matrix Inequality Approach

A robust unknown input observer for a nonlinear system whose nonlinear function satisfies the Lipschitz condition is designed based on linear matrix inequality approach. Both noise and uncertainties are taken into account in deriving the observer. A component fault detection and isolation scheme based on these observers is proposed. The effectiveness of the observer and the fault diagnosis scheme is shown by applying them for component fault diagnosis of an electrohydraulic actuator.

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