1,720,964 research outputs found
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
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
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
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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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