A New Fault Diagnosis Algorithm that Improves the Integration of Fault Detection and Isolation

2006 ◽  
Author(s):  
V. Puig ◽  
F. Schmid ◽  
J. Quevedo ◽  
B. Pulido
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Diagnosis Algorithm

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.

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.

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.

scholarly journals Fault Diagnosis for Actuators in a Class of Nonlinear Systems Based on an Adaptive Fault Detection Observer

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Runxia Guo ◽  
Kai Guo ◽  
Quan Gan ◽  
Junwei Zhang ◽  
Jiankang Dong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Control Systems ◽  
Asymptotical Stability ◽  
Measurement Noise ◽  
Diagnostic Strategy ◽  
State Variables ◽  
External Disturbances ◽  
Practical Engineering ◽  
Diagnosis Algorithm

The problem of actuators’ fault diagnosis is pursued for a class of nonlinear control systems that are affected by bounded measurement noise and external disturbances. A novel fault diagnosis algorithm has been proposed by combining the idea of adaptive control theory and the approach of fault detection observer. The asymptotical stability of the fault detection observer is guaranteed by setting the adaptive adjusting law of the unknown fault vector. A theoretically rigorous proof of asymptotical stability has been given. Under the condition that random measurement noise generated by the sensors of control systems and external disturbances exist simultaneously, the designed fault diagnosis algorithm is able to successfully give specific estimated values of state variables and failures rather than just giving a simple fault warning. Moreover, the proposed algorithm is very simple and concise and is easy to be applied to practical engineering. Numerical experiments are carried out to evaluate the performance of the fault diagnosis algorithm. Experimental results show that the proposed diagnostic strategy has a satisfactory estimation effect.

Fault Detection and Isolation for Systems in Aerospace: An Experience Report

2020 ◽  
Vol 12 (10) ◽  
pp. 1309-1314
Author(s):  
Kaustav Jyoti Borah
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Small Satellites ◽  
Limited Availability ◽  
Diagnosis And Prognosis ◽  
Aerospace Systems ◽  
Computational Budget ◽  
Fault Diagnosis And Prognosis ◽  
Great Damage

The paper throws light on the review of detection of fault and isolation for aerospace systems. Developing detection framework for small satellites is critical task due to limited availability of on board sensors and computational budget. In aerospace operations there are many subsystems and or components which can fail anytime. Once the fault has occurred, it can cause uncoverable losses and pollution of the environment and so forth. It is important to detect, isolate and find the remaining usefulness of that defective component and enable a suitable conclusion making before such faults make a great damage. Hence the fault detection is very important aspect for improving the economy as well as the safety of the system. The following research is intended to introduce an overview for fault diagnosis and prognosis techniques.

Sensor and Actuator Fault Diagnosis Based on Soft Computing Techniques

2015 ◽  
Vol 24 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Mohamed Salah Khireddine ◽  
Kheireddine Chafaa ◽  
Noureddine Slimane ◽  
Abdelhalim Boutarfa
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Sliding Mode ◽  
Fault Detection And Isolation ◽  
Robot Arm ◽  
Sensor Fault ◽  
Reconfigurable Control ◽  
Scara Robot ◽  
Tolerance Strategy ◽  
Soft Computing Techniques

AbstractComputational intelligence techniques are being investigated as an extension of the traditional fault diagnosis methods. This article presents, for the first time, a scheme for fault detection and isolation (FDI) via artificial neural networks and fuzzy logic. It deals with the sensor fault of a three-link selective compliance assembly robot arm (SCARA) robot. A second scheme is proposed for fault detection and accommodation via analytical redundancy, and it deals with the sensor fault of a three-link SCARA robot. These proposed FDI approaches are implemented on Matlab/Simulink software and tested under several types of faults. The results show the importance of this process. Then, the sensor faults are detected and isolated successfully. Also, the actuator faults are detected and a fault tolerance strategy is used for reconfigurable control using a sliding-mode observer.

scholarly journals A model-based approach to fault-tolerant control

2012 ◽  
Vol 22 (1) ◽  
pp. 67-86 ◽  
Author(s):  
Hans Niemann
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Theoretical Basis ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Fault Detection And Isolation ◽  
Systematic Technique ◽  
Model Based ◽  
Controller Parameterization

A model-based approach to fault-tolerant controlA model-based controller architecture for Fault-Tolerant Control (FTC) is presented in this paper. The controller architecture is based on a general controller parameterization. The FTC architecture consists of two main parts, a Fault Detection and Isolation (FDI) part and a controller reconfiguration part. The theoretical basis for the architecture is given followed by an investigation of the single parts in the architecture. It is shown that the general controller parameterization is central in connection with both fault diagnosis as well as controller reconfiguration. Especially in relation to the controller reconfiguration part, the application of controller parameterization results in a systematic technique for switching between different controllers. This also allows controller switching using different sets of actuators and sensors.

scholarly journals A CASE STUDY OF VIBRATION FAULT DIAGNOSIS APPLIED AT ROLLS-ROYCE T-56 TURBOPROP ENGINE

Aviation ◽  
2020 ◽  
Vol 23 (3) ◽  
pp. 78-82
Author(s):  
Christos Skliros
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Root Cause ◽  
Significant Challenge ◽  
Turboprop Engine

Gas turbine engines include a plethora of rotating modules, and each module consists of numerous components. A component’s mechanical fault can result in excessive engine vibrations. Identification of the root cause of a vibration fault is a significant challenge for both engine manufacturers and operators. This paper presents a case study of vibration fault detection and isolation applied at a Rolls-Royce T-56 turboprop engine. In this paper, the end-to-end fault diagnosis process from starting system faults to the isolation of the engine’s shaft that caused excessive vibrations is described. This work contributes to enhancing the understanding of turboprop engine behaviour under vibration conditions and highlights the merit of combing information from technical logs, maintenance manuals and engineering judgment in successful fault diagnosis.

scholarly journals EKF-Based Actuator Fault Detection and Diagnosis Method for Tilt-Rotor Unmanned Aerial Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jiaxin Gao ◽  
Qian Zhang ◽  
Jiyang Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Unmanned Aerial Vehicles ◽  
Fault Detection And Diagnosis ◽  
Actuator Fault ◽  
Tilt Rotor ◽  
Aerial Vehicles ◽  
Diagnosis Method ◽  
Diagnosis Algorithm ◽  
Detection And Diagnosis

Flight safety is of vital importance for tilt-rotor unmanned aerial vehicles (UAVs), which can take off and land vertically as well as cruise at high speed, especially in different kinds of complex environment. As being the executor of the flight control, the actuator failure will directly affect the controllability of the tilt-rotor UAV, and it has high probability of causing fatal personal injury and financial loss. However, due to the limitation of weight and cost, small UAVs cannot be equipped with redundant actuators. Therefore, there is an urgent need of fault detection and diagnosis method for the actuators. In this paper, an actuator fault detection and diagnosis (FDD) method based on the extended Kalman filter (EKF) and multiple-model adaptive estimation (MMAE) is proposed. The actuator deflections are added to the state vector and estimated using EKF. The fault diagnosis algorithm of MMAE could assign a conditional probability to each faulty actuator according to the residual of EKF and diagnose the fault. This paper is structured as follows: first, the structure and model of tilt-rotor UAV actuator are established. Then, EKF observers are introduced to estimate the state vector and to calculate residual sequences caused by different faulty actuators. The residuals from EKFs are used by fault diagnosis algorithm to assign a conditional probability to each failure condition, and fault type can be diagnosed according to the probabilities. The FDD method is verified by simulations, and the results demonstrate that the FDD algorithm could accurately and efficiently diagnose actuator fault without any additional sensor.

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