1,721,040 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
Detection of Drift Sensor Faults in a Class of Nonlinear Uncertain Systems
No full textThe paper presents a drift sensor fault detection scheme for a class of nonlinear uncertain system with partially measurable state variables. A rigorous fault detectability and detection-time interval analysis is illustrated. Moreover, the set of detectable sensor faults is characterized using off-line information and an upper bound on the fault detection interval is derived. Finally, a monotonicity property of the detection-time interval upper bound with respect to the magnitude of sensor faults is presented
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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
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