Improvements to Single-Fault Isolation Using Estimated Parameters

Authors:
A. K. Samantaray;S. K. Ghoshal;S. Chakraborty;A. Mukherjee
Affiliations:
Department of Mechanical Engineering, Indian Institute of Technology, 721302 Kharagpur, India;-;-;Department of Mechanical Engineering Indian Institute of Technology 721302 Kharagpur, India
Venue:
Simulation
Year:
2005

Citing 10
Cited 1

Fault diagnosis in dynamic systems: theory and application

Fault diagnosis in dynamic systems: theory and application
Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results

Automatica (Journal of IFAC)
Detection of abrupt changes: theory and application

Detection of abrupt changes: theory and application
Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis

Automatica (Journal of IFAC)
Robust model-based fault diagnosis for dynamic systems

Robust model-based fault diagnosis for dynamic systems
Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques

Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques
Diagnosis of continuous valued systems in transient operating regions

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Brief Paper: Robust fault detection based on observers for bilinear systems

Automatica (Journal of IFAC)
Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems

Automatica (Journal of IFAC)
Brief Robust fault detection in uncertain dynamic systems

Automatica (Journal of IFAC)

Full Length Article: An evidential approach for detection of abnormal behaviour in the presence of unreliable sensors

Information Fusion

Quantified Score

Hi-index	0.00

Visualization

Abstract

A method for finer fault isolation or localization in the model-based fault detection and isolation (FDI) paradigm is developed using parallely computed bond graph models. Many of the existing modelbased FDI methods are based on the evaluation of model consistency expressed in terms of analytical redundancy relations (ARR). These evaluations lead to residuals, and a number of sensors are to be installed in the plant to generate independent signatures needed for fault isolation. However, all the possible faults may not be isolable with the available instrumentation, and it is sometimes expensive or technically impossible to install necessary sensors in the plant to physically measure each and every state. In such situations, all component faults may not be uniquely isolated. However, a unique fault parameter subspace can be identified. One of the possible solutions, as proposed in this article, is to estimate parameters of that subspace from the ARR by assuming a single-fault hypothesis and then to incorporate the estimated values in separate models to run parallel with the plant during the fault. Thereafter, comparison of model behaviors leads to localization of the faulty parameters. This method is applied to an example system.