Abstract: Recently, the issue of machine condition monitoring
and fault diagnosis as a part of maintenance system became global
due to the potential advantages to be gained from reduced
maintenance costs, improved productivity and increased machine
availability. The aim of this work is to investigate the effectiveness
of a new fault diagnosis method based on power spectral density
(PSD) of vibration signals in combination with decision trees and
fuzzy inference system (FIS). To this end, a series of studies was
conducted on an external gear hydraulic pump. After a test under
normal condition, a number of different machine defect conditions
were introduced for three working levels of pump speed (1000, 1500,
and 2000 rpm), corresponding to (i) Journal-bearing with inner face
wear (BIFW), (ii) Gear with tooth face wear (GTFW), and (iii)
Journal-bearing with inner face wear plus Gear with tooth face wear
(B&GW). The features of PSD values of vibration signal were
extracted using descriptive statistical parameters. J48 algorithm is
used as a feature selection procedure to select pertinent features from
data set. The output of J48 algorithm was employed to produce the
crisp if-then rule and membership function sets. The structure of FIS
classifier was then defined based on the crisp sets. In order to
evaluate the proposed PSD-J48-FIS model, the data sets obtained
from vibration signals of the pump were used. Results showed that
the total classification accuracy for 1000, 1500, and 2000 rpm
conditions were 96.42%, 100%, and 96.42% respectively. The results
indicate that the combined PSD-J48-FIS model has the potential for
fault diagnosis of hydraulic pumps.
Abstract: During recent years wind turbine technology has
undergone rapid developments. Growth in size and the optimization
of wind turbines has enabled wind energy to become increasingly
competitive with conventional energy sources. As a result today-s
wind turbines participate actively in the power production of several
countries around the world. These developments raise a number of
challenges to be dealt with now and in the future. The penetration of
wind energy in the grid raises questions about the compatibility of the
wind turbine power production with the grid. In particular, the
contribution to grid stability, power quality and behavior during fault
situations plays therefore as important a role as the reliability. In the
present work, we addressed two fault situations that have shown their
influence on the generator and the behavior of the wind over the
defects which are briefly discussed based on simulation results.
Abstract: Many studies have been conducted for derivation of
attenuation relationships worldwide, however few relationships have
been developed to use for the seismic region of Iranian plateau and
only few of these studies have been conducted for derivation of
attenuation relationships for parameters such as uniform duration.
Uniform duration is the total time during which the acceleration is
larger than a given threshold value (default is 5% of PGA). In this
study, the database was same as that used previously by Ghodrati
Amiri et al. (2007) with same correction methods for earthquake
records in Iran. However in this study, records from earthquakes with
MS< 4.0 were excluded from this database, each record has
individually filtered afterward, and therefore the dataset has been
expanded. These new set of attenuation relationships for Iran are
derived based on tectonic conditions with soil classification into rock
and soil. Earthquake parameters were chosen to be
hypocentral distance and magnitude in order to make it easier to use
the relationships for seismic hazard analysis. Tehran is the capital
city of Iran wit ha large number of important structures. In this study,
a probabilistic approach has been utilized for seismic hazard
assessment of this city. The resulting uniform duration against return
period diagrams are suggested to be used in any projects in the area.
Abstract: The operational behavior of a six-phase squirrel cage
induction machine with faulted stator terminals is presented in this
paper. The study is carried out using the derived mathematical model
of the machine in the arbitrary reference frame. Tests are conducted
on a 1 kW experimental machine.
Steady-state and dynamic performance are analyzed for the
machine unloaded and loaded conditions. The results shows that with
one of the stator phases experiencing either an open- circuit or short
circuit fault the machine still produces starting torque, albeit the
running performance is significantly derated.
Abstract: An on-line condition monitoring method for transmission line is proposed using electrical circuit theory and IT technology in this paper. It is reasonable that the circuit parameters such as resistance (R), inductance (L), conductance (g) and capacitance (C) of a transmission line expose the electrical conditions and physical state of the line. Those parameters can be calculated from the linear equation composed of voltages and currents measured by synchro-phasor measurement technique at both end of the line. A set of linear voltage drop equations containing four terminal constants (A, B ,C ,D ) are mathematical models of the transmission line circuits. At least two sets of those linear equations are established from different operation condition of the line, they may mathematically yield those circuit parameters of the line. The conditions of line connectivity including state of connecting parts or contacting parts of the switching device may be monitored by resistance variations during operation. The insulation conditions of the line can be monitored by conductance (g) and capacitance(C) measurements. Together with other condition monitoring devices such as partial discharge, sensors and visual sensing device etc.,they may give useful information to monitor out any incipient symptoms of faults. The prototype of hardware system has been developed and tested through laboratory level simulated transmission lines. The test has shown enough evident to put the proposed method to practical uses.
Abstract: In this paper, genetic algorithm (GA) opmization technique is applied to design Flexible AC Transmission System (FACTS)-based damping controllers. Two types of controller structures, namely a proportional-integral (PI) and a lead-lag (LL) are considered. The design problem of the proposed controllers is formulated as an optimization problem and GA is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. The proposed controllers are tested on a weakly connected power system subjected to different disturbances. The non-linear simulation results are presented to show the effectiveness of the proposed controller and their ability to provide efficient damping of low frequency oscillations. It is also observed that the proposed SSSC-based controllers improve greatly the voltage profile of the system under severe disturbances. Further, the dynamic performances of both the PI and LL structured FACTS-controller are analyzed at different loading conditions and under various disturbance condition as well as under unbalanced fault conditions..
Abstract: This paper presents the impact study of apparent
reactance injected by series Flexible AC Transmission System
(FACTS) i.e. Thyristor Controlled Series Reactor (TCSR) on the
measured impedance of a 400 kV single electrical transmission line
in the presence of phase to earth fault with fault resistance. The study
deals with an electrical transmission line of Eastern Algerian
transmission networks at Group Sonelgaz (Algerian Company of
Electrical and Gas) compensated by TCSR connected at midpoint of
the line. This compensator used to inject active and reactive powers
is controlled by three TCSR-s. The simulations results investigate the
impacts of the TCSR on the parameters of short circuit calculation
and parameters of measured impedance by distance relay in the
presence of earth fault for three cases study.
Abstract: Recent years, adaptive pushover methods have been
developed for seismic analysis of structures. Herein, the accuracy of
the displacement-based adaptive pushover (DAP) method, which is
introduced by Antoniou and Pinho [2004], is evaluated for Irregular
buildings. The results are compared to the force-based procedure.
Both concrete and steel frame structures, asymmetric in plan and
elevation are analyzed and also torsional effects are taking into the
account. These analyses are performed using both near fault and far
fault records. In order to verify the results, the Incremental Dynamic
Analysis (IDA) is performed.
Abstract: In the semiconductor manufacturing process, large
amounts of data are collected from various sensors of multiple
facilities. The collected data from sensors have several different characteristics
due to variables such as types of products, former processes
and recipes. In general, Statistical Quality Control (SQC) methods
assume the normality of the data to detect out-of-control states of
processes. Although the collected data have different characteristics,
using the data as inputs of SQC will increase variations of data,
require wide control limits, and decrease performance to detect outof-
control. Therefore, it is necessary to separate similar data groups
from mixed data for more accurate process control. In the paper,
we propose a regression tree using split algorithm based on Pearson
distribution to handle non-normal distribution in parametric method.
The regression tree finds similar properties of data from different
variables. The experiments using real semiconductor manufacturing
process data show improved performance in fault detecting ability.
Abstract: An appropriate method for fault identification and classification on extra high voltage transmission line using discrete wavelet transform is proposed in this paper. The sharp variations of the generated short circuit transient signals which are recorded at the sending end of the transmission line are adopted to identify the fault. The threshold values involve fault classification and these are done on the basis of the multiresolution analysis. A comparative study of the performance is also presented for Discrete Fourier Transform (DFT) based Artificial Neural Network (ANN) and Discrete Wavelet Transform (DWT). The results prove that the proposed method is an effective and efficient one in obtaining the accurate result within short duration of time by using Daubechies 4 and 9. Simulation of the power system is done using MATLAB.
Abstract: This study adopted previous fault patterns, results of
detection analysis, historical records and data, and experts-
experiences to establish fuzzy principles and estimate the failure
probability index of components of a power transformer. Considering
that actual parameters and limiting conditions of parameters may
differ, this study used the standard data of IEC, IEEE, and CIGRE as
condition parameters. According to the characteristics of each
condition parameter, relative degradation was introduced to reflect the
degree of influence of the factors on the transformer condition. The
method of fuzzy mathematics was adopted to determine the
subordinate function of the transformer condition. The calculation
used the Matlab Fuzzy Tool Box to select the condition parameters of
coil winding, iron core, bushing, OLTC, insulating oil and other
auxiliary components and factors (e.g., load records, performance
history, and maintenance records) of the transformer to establish the
fuzzy principles. Examples were presented to support the rationality
and effectiveness of the evaluation method of power transformer
performance conditions, as based on fuzzy comprehensive evaluation.
Abstract: The effect of the rotational speed and axial torque on
the diagnostics of tapered rolling element bearing defects was
investigated. The accelerometer was mounted on the bearing housing
and connected to Sound and Vibration Analyzer (SVAN 958) and
was used to measure the accelerations from the bearing housing. The
data obtained from the bearing was processed to detect damage of the
bearing using statistical tools and the results were subsequently
analyzed to see if bearing damage had been captured. From this study
it can be seen that damage is more evident when the bearing is
loaded. Also, at the incipient stage of damage the crest factor and
kurtosis values are high but as time progresses the crest factors and
kurtosis values decrease whereas the peak and RMS values are low at
the incipient stage but increase with damage.
Abstract: Business process modeling has become an accepted
means for designing and describing business operations. Thereby,
consistency of business process models, i.e., the absence of modeling
faults, is of upmost importance to organizations. This paper presents
a concept and subsequent implementation for detecting faults in
business process models and for computing a measure of their
consistency. It incorporates not only syntactic consistency but also
semantic consistency, i.e., consistency regarding the meaning of
model elements from a business perspective.
Abstract: Economically transformers constitute one of the largest investments in a Power system. For this reason, transformer condition assessment and management is a high priority task. If a transformer fails, it would have a significant negative impact on revenue and service reliability. Monitoring the state of health of power transformers has traditionally been carried out using laboratory Dissolved Gas Analysis (DGA) tests performed at periodic intervals on the oil sample, collected from the transformers. DGA of transformer oil is the single best indicator of a transformer-s overall condition and is a universal practice today, which started somewhere in the 1960s. Failure can occur in a transformer due to different reasons. Some failures can be limited or prevented by maintenance. Oil filtration is one of the methods to remove the dissolve gases and prevent the deterioration of the oil. In this paper we analysis the DGA data by regression method and predict the gas concentration in the oil in the future. We bring about a comparative study of different traditional methods of regression and the errors generated out of their predictions. With the help of these data we can deduce the health of the transformer by finding the type of fault if it has occurred or will occur in future. Additional in this paper effect of filtration on the transformer health is highlight by calculating the probability of failure of a transformer with and without oil filtrating.
Abstract: Appropriate description of business processes through
standard notations has become one of the most important assets for
organizations. Organizations must therefore deal with quality faults
in business process models such as the lack of understandability and
modifiability. These quality faults may be exacerbated if business
process models are mined by reverse engineering, e.g., from existing
information systems that support those business processes. Hence,
business process refactoring is often used, which change the internal
structure of business processes whilst its external behavior is
preserved. This paper aims to choose the most appropriate set of
refactoring operators through the quality assessment concerning
understandability and modifiability. These quality features are
assessed through well-proven measures proposed in the literature.
Additionally, a set of measure thresholds are heuristically established
for applying the most promising refactoring operators, i.e., those that
achieve the highest quality improvement according to the selected
measures in each case.
Abstract: Short Message Service (SMS) has grown in
popularity over the years and it has become a common way of
communication, it is a service provided through General System
for Mobile Communications (GSM) that allows users to send text
messages to others.
SMS is usually used to transport unclassified information, but
with the rise of mobile commerce it has become a popular tool for
transmitting sensitive information between the business and its
clients. By default SMS does not guarantee confidentiality and
integrity to the message content.
In the mobile communication systems, security (encryption)
offered by the network operator only applies on the wireless link.
Data delivered through the mobile core network may not be
protected. Existing end-to-end security mechanisms are provided
at application level and typically based on public key
cryptosystem.
The main concern in a public-key setting is the authenticity of
the public key; this issue can be resolved by identity-based (IDbased)
cryptography where the public key of a user can be derived
from public information that uniquely identifies the user.
This paper presents an encryption mechanism based on the IDbased
scheme using Elliptic curves to provide end-to-end security
for SMS. This mechanism has been implemented over the standard
SMS network architecture and the encryption overhead has been
estimated and compared with RSA scheme. This study indicates
that the ID-based mechanism has advantages over the RSA
mechanism in key distribution and scalability of increasing
security level for mobile service.
Abstract: In this paper, we generalize several techniques in
developing Fault Tolerant Software. We introduce property
“Correctness" in evaluating N-version Systems and compare it to
some commonly used properties such as reliability or availability.
We also find out the relation between this property and the number of
versions of system. Our experiments to verify the correctness and the
applicability of the relation are also presented.
Abstract: This conference paper discusses a risk allocation problem for subprime investing banks involving investment in subprime structured mortgage products (SMPs) and Treasuries. In order to solve this problem, we develop a L'evy process-based model of jump diffusion-type for investment choice in subprime SMPs and Treasuries. This model incorporates subprime SMP losses for which credit default insurance in the form of credit default swaps (CDSs) can be purchased. In essence, we solve a mean swap-at-risk (SaR) optimization problem for investment which determines optimal allocation between SMPs and Treasuries subject to credit risk protection via CDSs. In this regard, SaR is indicative of how much protection investors must purchase from swap protection sellers in order to cover possible losses from SMP default. Here, SaR is defined in terms of value-at-risk (VaR). Finally, we provide an analysis of the aforementioned optimization problem and its connections with the subprime mortgage crisis (SMC).
Abstract: The paper presents a compressor anti-surge control
system, that results in maximizing compressor throughput with
pressure standard deviation reduction, increased safety margin
between design point and surge limit line and avoiding possible
machine surge. Alternative control strategies are presented.
Abstract: The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.