Abstract: This paper presents an integrated case based and rule
based reasoning method for car faulty diagnosis. The reasoning
method is done through extracting the past cases from the Proton
Service Center while comparing with the preset rules to deduce a
diagnosis/solution to a car service case. New cases will be stored to
the knowledge base. The test cases examples illustrate the
effectiveness of the proposed integrated reasoning. It has proven
accuracy of similar reasoning if carried out by a service advisor from
the service center.
Abstract: One of the robust fault detection filter (RFDF)
designing method is based on sliding-mode theory. The main purpose
of our study is to introduce an innovative simplified reference
residual model generator to formulate the RFDF as a sliding-mode
observer without any manipulation package or transformation matrix,
through which the generated residual signals can be evaluated. So the
proposed design is more explicit and requires less design parameters
in comparison with approaches requiring changing coordinates. To
the best author's knowledge, this is the first time that the sliding
mode technique is applied to detect actuator and sensor faults in a
real boiler. The designing procedure is proposed in a drum boiler in
Synvendska Kraft AB Plant in Malmo, Sweden as a multivariable
and strongly coupled system. It is demonstrated that both sensor and
actuator faults can robustly be detected. Also sensor faults can be
diagnosed and isolated through this method.
Abstract: An electric power system includes a generating, a
transmission, a distribution, and consumers subsystems. An electrical
power network in Tanzania keeps growing larger by the day and
become more complex so that, most utilities have long wished for
real-time monitoring and remote control of electrical power system
elements such as substations, intelligent devices, power lines,
capacitor banks, feeder switches, fault analyzers and other physical
facilities. In this paper, the concept of automation of management of
power systems from generation level to end user levels was
determined by using Power System Simulator for Engineering
(PSS/E) version 30.3.2.
Abstract: This paper presents a new approach for the protection
of Thyristor-Controlled Series Compensator (TCSC) line using
Support Vector Machine (SVM). One SVM is trained for fault
classification and another for section identification. This method use
three phase current measurement that results in better speed and
accuracy than other SVM based methods which used single phase
current measurement. This makes it suitable for real-time protection.
The method was tested on 10,000 data instances with a very wide
variation in system conditions such as compensation level, source
impedance, location of fault, fault inception angle, load angle at
source bus and fault resistance. The proposed method requires only
local current measurement.
Abstract: To comply with the international human right
legislation concerning the freedom of movement, transport systems
are required to be made accessible in order that all citizens, regardless
of their physical condition, have equal possibilities to use them. In
Hungary, apparently there is a considerable default in the
improvement of accessible public transport. This study is aiming to
overview the current Hungarian situation and to reveal the reasons of
the deficiency. The result shows that in spite of the relatively
favourable juridical background linked to the accessibility needs and
to the rights of persons with disabilities there is a strong delay in
putting all in practice in the field of public transport. Its main reason
is the lack of financial resource and referring to this the lack of
creating mandatory regulations. In addition to this the proprietary
rights related to public transport are also variable, which also limits
the improvement possibilities. Consequently, first of all an accurate
and detailed regulatory procedure is expected to change the present
unfavourable situation and to create the conditions of the fast
realization, which is already behind time.
Abstract: Ferroresonance is an electrical phenomenon in
nonlinear character, which frequently occurs in power system due to
transmission line faults and single or more-phase switching on the
lines as well as usage of the saturable transformers. In this study, the
ferroresonance phenomena are investigated under the modeling of the
West Anatolian Electric Power Network of 380 kV in Turkey. The
ferroresonance event is observed as a result of removing the loads at
the end of the lines. In this sense, two different cases are considered.
At first, the switching is applied at 2nd second and the ferroresonance
affects are observed between 2nd and 4th seconds in the voltage
variations of the phase-R. Hence the ferroresonance and nonferroresonance
parts of the overall data are compared with each
others using the Fourier transform techniques to show the
ferroresonance affects.
Abstract: Independent spanning trees (ISTs) provide a number of advantages in data broadcasting. One can cite the use in fault tolerance network protocols for distributed computing and bandwidth. However, the problem of constructing multiple ISTs is considered hard for arbitrary graphs. In this paper we present an efficient algorithm to construct ISTs on hypercubes that requires minimum resources to be performed.
Abstract: COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.
Abstract: Simulation is a very powerful method used for highperformance
and high-quality design in distributed system, and now
maybe the only one, considering the heterogeneity, complexity and
cost of distributed systems. In Grid environments, foe example, it is
hard and even impossible to perform scheduler performance
evaluation in a repeatable and controllable manner as resources and
users are distributed across multiple organizations with their own
policies. In addition, Grid test-beds are limited and creating an
adequately-sized test-bed is expensive and time consuming.
Scalability, reliability and fault-tolerance become important
requirements for distributed systems in order to support distributed
computation. A distributed system with such characteristics is called
dependable. Large environments, like Cloud, offer unique
advantages, such as low cost, dependability and satisfy QoS for all
users. Resource management in large environments address
performant scheduling algorithm guided by QoS constrains. This
paper presents the performance evaluation of scheduling heuristics
guided by different optimization criteria. The algorithms for
distributed scheduling are analyzed in order to satisfy users
constrains considering in the same time independent capabilities of
resources. This analysis acts like a profiling step for algorithm
calibration. The performance evaluation is based on simulation. The
simulator is MONARC, a powerful tool for large scale distributed
systems simulation. The novelty of this paper consists in synthetic
analysis results that offer guidelines for scheduler service
configuration and sustain the empirical-based decision. The results
could be used in decisions regarding optimizations to existing Grid
DAG Scheduling and for selecting the proper algorithm for DAG
scheduling in various actual situations.
Abstract: For several high speed networks, providing resilience against failures is an essential requirement. The main feature for designing next generation optical networks is protecting and restoring high capacity WDM networks from the failures. Quick detection, identification and restoration make networks more strong and consistent even though the failures cannot be avoided. Hence, it is necessary to develop fast, efficient and dependable fault localization or detection mechanisms. In this paper we propose a new fault localization algorithm for WDM networks which can identify the location of a failure on a failed lightpath. Our algorithm detects the failed connection and then attempts to reroute data stream through an alternate path. In addition to this, we develop an algorithm to analyze the information of the alarms generated by the components of an optical network, in the presence of a fault. It uses the alarm correlation in order to reduce the list of suspected components shown to the network operators. By our simulation results, we show that our proposed algorithms achieve less blocking probability and delay while getting higher throughput.
Abstract: This paper presents an approach for the design of
fuzzy logic power system stabilizers using genetic algorithms. In the
proposed fuzzy expert system, speed deviation and its derivative
have been selected as fuzzy inputs. In this approach the parameters of
the fuzzy logic controllers have been tuned using genetic algorithm.
Incorporation of GA in the design of fuzzy logic power system
stabilizer will add an intelligent dimension to the stabilizer and
significantly reduces computational time in the design process. It is
shown in this paper that the system dynamic performance can be
improved significantly by incorporating a genetic-based searching
mechanism. To demonstrate the robustness of the genetic based
fuzzy logic power system stabilizer (GFLPSS), simulation studies on
multimachine system subjected to small perturbation and three-phase
fault have been carried out. Simulation results show the superiority
and robustness of GA based power system stabilizer as compare to
conventionally tuned controller to enhance system dynamic
performance over a wide range of operating conditions.
Abstract: FlexRay, as a communication protocol for automotive
control systems, is developed to fulfill the increasing demand on the
electronic control units for implementing systems with higher safety
and more comfort. In this work, we study the impact of
radiation-induced soft errors on FlexRay-based steer-by-wire system.
We injected the soft errors into general purpose register set of FlexRay
nodes to identify the most critical registers, the failure modes of the
steer-by-wire system, and measure the probability distribution of
failure modes when an error occurs in the register file.
Abstract: This study examines the design and construction of AC Electronics load surge protection in order to carry electric surge load arisen from faults in low voltage electricity system (single phase/220V) by using the principle of electronics load clamping voltage during induction period so that electric voltage could go through to safe load and continue to work. The qualification of the designed device could prevent both transient over voltage and voltage swell. Both will work in cooperation, resulting in the ability to improve and modify the quality of electrical power in Thailand electricity distribution system more effective than the past and help increase the lifetime of electric appliances, electric devices, and electricity protection equipments.
Abstract: This paper presents a new method to detect high impedance faults in radial distribution systems. Magnitudes of third and fifth harmonic components of voltages and currents are used as a feature vector for fault discrimination. The proposed methodology uses a learning vector quantization (LVQ) neural network as a classifier for identifying high impedance arc-type faults. The network learns from the data obtained from simulation of a simple radial system under different fault and system conditions. Compared to a feed-forward neural network, a properly tuned LVQ network gives quicker response.
Abstract: Authentication plays a vital role in many secure
systems. Most of these systems require user to log in with his or her
secret password or pass phrase before entering it. This is to ensure all
the valuables information is kept confidential guaranteeing also its
integrity and availability. However, to achieve this goal, users are
required to memorize high entropy passwords or pass phrases.
Unfortunately, this sometimes causes difficulty for user to remember
meaningless strings of data. This paper presents a new scheme which
assigns a weight to each personal question given to the user in
revealing the encrypted secrets or password. Concentration of this
scheme is to offer fault tolerance to users by allowing them to forget
the specific password to a subset of questions and still recover the
secret and achieve successful authentication. Comparison on level of
security for weight-based and weightless secret recovery scheme is
also discussed. The paper concludes with the few areas that requires
more investigation in this research.
Abstract: A model based fault detection and diagnosis
technique for DC motor is proposed in this paper. Fault detection
using Kalman filter and its different variants are compared. Only
incipient faults are considered for the study. The Kalman Filter
iterations and all the related computations required for fault detection
and fault confirmation are presented. A second order linear state
space model of DC motor is used for this work. A comparative
assessment of the estimates computed from four different observers
and their relative performance is evaluated.
Abstract: One of the problems in fault diagnosis of transformer
based on dissolved gas, is lack of matching the result of fault
diagnosis of different standards with the real world. In this paper, the
result of the different standards is analyzed using fuzzy and the result
is compared with the empirical test. The comparison between the
suggested method and existing methods indicate the capability of the
suggested method in on-line fault diagnosis of the transformers. In
addition, in some cases the existing standards are not able to
diagnose the fault. In theses cases, the presented method has the
potential of diagnosing the fault. The information of three
transformers is used to the show the capability of the suggested
method in diagnosing the fault. The results validate the capability of
the presented method in fault diagnosis of the transformer.
Abstract: The objective of this paper is to develop a neural
network-based residual generator to detect the fault in the actuators
for a specific communication satellite in its attitude control system
(ACS). First, a dynamic multilayer perceptron network with dynamic
neurons is used, those neurons correspond a second order linear
Infinite Impulse Response (IIR) filter and a nonlinear activation
function with adjustable parameters. Second, the parameters from the
network are adjusted to minimize a performance index specified by
the output estimated error, with the given input-output data collected
from the specific ACS. Then, the proposed dynamic neural network
is trained and applied for detecting the faults injected to the wheel,
which is the main actuator in the normal mode for the communication
satellite. Then the performance and capabilities of the proposed
network were tested and compared with a conventional model-based
observer residual, showing the differences between these two
methods, and indicating the benefit of the proposed algorithm to
know the real status of the momentum wheel. Finally, the application
of the methods in a satellite ground station is discussed.
Abstract: Using maximal consistent blocks of tolerance relation
on the universe in incomplete decision table, the concepts of join block
and meet block are introduced and studied. Including tolerance class,
other blocks such as tolerant kernel and compatible kernel of an object
are also discussed at the same time. Upper and lower approximations
based on those blocks are also defined. Default definite decision rules
acquired from incomplete decision table are proposed in the paper. An
incremental algorithm to update default definite decision rules is
suggested for effective mining tasks from incomplete decision table
into which data is appended. Through an example, we demonstrate
how default definite decision rules based on maximal consistent
blocks, join blocks and meet blocks are acquired and how optimization
is done in support of discernibility matrix and discernibility function
in the incomplete decision table.
Abstract: In this paper test generation methods and appropriate fault models for testing and analysis of embedded systems described as (extended) finite state machines ((E)FSMs) are presented. Compared to simple FSMs, EFSMs specify not only the control flow but also the data flow. Thus, we define a two-level fault model to cover both aspects. The goal of this paper is to reuse well-known FSM-based test generation methods for automation of embedded system testing. These methods have been widely used in testing and validation of protocols and communicating systems. In particular, (E)FSMs-based specification and testing is more advantageous because (E)FSMs support the formal semantic of already standardised formal description techniques (FDTs) despite of their popularity in the design of hardware and software systems.