1,720,988 research outputs found
Fault diagnosis of a class of nonlinear uncertain systems with Lipschitz nonlinearities using adaptive estimation
No full textThis paper presents a fault detection and isolation (FDI) scheme for a class of Lipschitz nonlinear systems with nonlinear and unstructured modeling uncertainty. This significantly extends previous results by considering a more general class of system nonlinearities which are modeled as functions of the system input and partially measurable state variables. A new FDI method is developed using adaptive estimation techniques. The FDI architecture consists of a fault detection estimator and a bank of fault isolation estimators. The fault detectability and isolability conditions, characterizing the class of faults that are detectable and isolable by the proposed scheme, are rigorously established. The fault isolability condition is derived via the so-called fault mismatch functions, which are defined to characterize the mutual difference between pairs of possible faults. A simulation example of a single-link flexible joint robot is used to illustrate the effectiveness of the proposed sche
An Algebraic Approach for Robust Fault Detection of Input-Output Elastodynamic Distributed Parameter Systems
No full textThis paper deals with the problem of designing a
robust fault detection methodology for a class of input-output,
uncertain dynamical distributed parameter systems, namely
mechanical elastodynamic systems, which are representative of
a whole class of problems related to on-line health monitoring
of mechanical and civil engineering structures. The proposed
approach does not require full state measurements and is
robust to measuring, modeling and numerical errors, thanks
to a time varying detection threshold. In order to avoid the
problems associated with classical discretization techniques for
distributed parameter systems, which can lead to numerical
errors difficult to bound a priori, and thus higher thresholds,
a suitable structure-preserving algebraic approach, called Cell
Method, will be employed. This method consists in writing the
equations of a distributed parameter system directly in discrete
form, avoiding the usual discretization process and leading to
a symplectic, that is energy preserving, numerical scheme
A Unified Fault Diagnosis Approach Utilizing Filtering and Adaptive Approximation for Process and Sensor Faults in a Class of Continuous-Time Nonlinear Systems
No full textThis paper develops an integrated filtering and
adaptive approximation-based approach for fault diagnosis of
process and sensor faults in a class of continuous-time nonlinear
systems with modeling uncertainties and measurement noise. The
proposed approach integrates learning with filtering techniques
to derive tight detection thresholds, which is accomplished in two
ways: 1) by learning the modeling uncertainty through adaptive
approximation methods and 2) by using filtering for dampening
measurement noise. Upon the detection of a fault, two estimation
models, one for process and the other for sensor faults, are
initiated in order to identify the type of fault. Each estimation
model utilizes learning to estimate the potential fault that has
occurred, and adaptive isolation thresholds for each estimation
model are designed. The fault type is deduced based on an
exclusion-based logic, and fault detectability and identification
conditions are rigorously derived, characterizing quantitatively
the class of faults that can be detected and identified by
the proposed scheme. Finally, simulation results are used to
demonstrate the effectiveness of the proposed approach
A Distributed Fault Diagnosis Approach Utilizing Adaptive Approximation for a Class of Interconnected Continuous-Time Nonlinear Systems
No full textThis paper develops an adaptive approximation
based approach for distributed fault diagnosis for a class of interconnected
continuous-time nonlinear systems with modeling
uncertainties and measurement noise. The proposed approach
integrates learning with filtering techniques and allows the
derivation of tight detection thresholds. This is accomplished
in two ways: at first by learning the modeling uncertainty
through adaptive approximation methods, so that the learned
function is used for the derivation of the residual signal, and
then by using filtering for dampening measurement noise. The
required signals for both tasks are derived through a two-stage
filtering process, by exploiting the properties of the filtering
framework. Finally, simulation results are used to demonstrate
the effectiveness of the proposed approac
Distributed Fault Diagnosis for Process and Sensor Faults in a Class of Interconnected Input-Output Nonlinear Discrete-Time Systems
Full text linkThis paper presents a distributed fault diagnosis scheme able to deal with process and sensor faults in an integrated way for a
class of interconnected input–output nonlinear uncertain discrete-time systems. A robust distributed fault detection scheme
is designed, where each interconnected subsystem is monitored by its respective fault detection agent, and according to the
decisions of these agents, further information regarding the type of the fault can be deduced. As it is shown, a process fault
occurring in one subsystem can only be detected by its corresponding detection agent whereas a sensor fault in a subsystem
can be detected by either its corresponding detection agent or the detection agent of another subsystem that is affected by the
subsystem where the sensor fault occurred. This discriminating factor is exploited for the derivation of a high-level isolation
scheme.Moreover, process and sensor fault detectability conditions characterising quantitatively the class of detectable faults
are derived. Finally, a simulation example is used to illustrate the effectiveness of the proposed distributed fault detection
scheme
Distributed Fault Diagnosis of Large-scale Discrete-time Nonlinear Systems: New Results on the Isolation Problem
No full textA Deadbeat Estimator-Based Fault Isolation Scheme for Nonlinear Systems
No full textThis paper deals with a fault isolation scheme based on a deadbeat estimation methodology for abrupt faults occurring in nonlinear uncertain dynamic systems. In principle,
the deadbeat estimator allows the parameter estimates to converge within an arbitrarily small finite-time for a class of nonlinear system with full-state measurements. The corresponding
adaptive isolation thresholds are designed based on an a priori known bound on the system uncertainty. A fault isolation decision is made if the residual associated with the
matched isolation estimator remains below its corresponding adaptive threshold, whereas at least one of the components of the residuals associated with all the other estimators exceeds its threshold at some finite time. Fault isolability conditions are illustrated and simulation trials are given to assess the expected improvement of the fault isolation methodology in terms of fault isolation time
A Distributed Networked Approach for Fault Detection of Large-scale Systems
Full text linkNetworked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The
proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available.
This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units.
The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection
dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem.
Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique
Distributed Adaptive Fault-Tolerant Control of Uncertain Multi-Agent Systems
Full text linkThis paper presents an adaptive fault-tolerant control (FTC) scheme for a class of nonlinear uncertain multi-agent systems. A local FTC scheme is designed for each agent
using local measurements and suitable information exchanged between neighboring agents. Each local FTC scheme consists of a fault diagnosis module and a recongurable controller module comprised of a baseline controller and two adaptive fault-tolerant controllers activated after fault detection and after fault isolation, respectively. Under certain assumptions, the closedloop system's stability and leader-follower consensus properties are rigorously established under dierent modes of the FTC system, including the time-period before possible fault detection, between fault detection and possible isolation, and after fault isolatio
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