Abstract: Scheduling algorithms are used in operating systems
to optimize the usage of processors. One of the most efficient
algorithms for scheduling is Multi-Layer Feedback Queue (MLFQ)
algorithm which uses several queues with different quanta. The most
important weakness of this method is the inability to define the
optimized the number of the queues and quantum of each queue. This
weakness has been improved in IMLFQ scheduling algorithm.
Number of the queues and quantum of each queue affect the response
time directly. In this paper, we review the IMLFQ algorithm for
solving these problems and minimizing the response time. In this
algorithm Recurrent Neural Network has been utilized to find both
the number of queues and the optimized quantum of each queue.
Also in order to prevent any probable faults in processes' response
time computation, a new fault tolerant approach has been presented.
In this approach we use combinational software redundancy to
prevent the any probable faults. The experimental results show that
using the IMLFQ algorithm results in better response time in
comparison with other scheduling algorithms also by using fault
tolerant mechanism we improve IMLFQ performance.
Abstract: Collateralized Debt Obligations are not as widely used
nowadays as they were before 2007 Subprime crisis. Nonetheless
there remains an enthralling challenge to optimize cash flows
associated with synthetic CDOs. A Gaussian-based model is used
here in which default correlation and unconditional probabilities of
default are highlighted. Then numerous simulations are performed
based on this model for different scenarios in order to evaluate the
associated cash flows given a specific number of defaults at different
periods of time. Cash flows are not solely calculated on a single
bought or sold tranche but rather on a combination of bought and
sold tranches. With some assumptions, the simplex algorithm gives
a way to find the maximum cash flow according to correlation of
defaults and maturities. The used Gaussian model is not realistic in
crisis situations. Besides present system does not handle buying or
selling a portion of a tranche but only the whole tranche. However the
work provides the investor with relevant elements on how to know
what and when to buy and sell.
Abstract: Condition monitoring of electrical power equipment
has attracted considerable attention for many years. The aim of this
paper is to use Labview with Fuzzy Logic controller to build a
simulation system to diagnose transformer faults and monitor its
condition. The front panel of the system was designed using
LabVIEW to enable computer to act as customer-designed
instrument. The dissolved gas-in-oil analysis (DGA) method was
used as technique for oil type transformer diagnosis; meanwhile
terminal voltages and currents analysis method was used for dry type
transformer. Fuzzy Logic was used as expert system that assesses all
information keyed in at the front panel to diagnose and predict the
condition of the transformer. The outcome of the Fuzzy Logic
interpretation will be displayed at front panel of LabVIEW to show
the user the conditions of the transformer at any time.
Abstract: This paper presents a new method of fault detection and isolation (FDI) for polymer electrolyte membrane (PEM) fuel cell (FC) dynamic systems under an open-loop scheme. This method uses a radial basis function (RBF) neural network to perform fault identification, classification and isolation. The novelty is that the RBF model of independent mode is used to predict the future outputs of the FC stack. One actuator fault, one component fault and three sensor faults have been introduced to the PEMFC systems experience faults between -7% to +10% of fault size in real-time operation. To validate the results, a benchmark model developed by Michigan University is used in the simulation to investigate the effect of these five faults. The developed independent RBF model is tested on MATLAB R2009a/Simulink environment. The simulation results confirm the effectiveness of the proposed method for FDI under an open-loop condition. By using this method, the RBF networks able to detect and isolate all five faults accordingly and accurately.
Abstract: This paper presents Faults Forecasting System (FFS)
that utilizes statistical forecasting techniques in analyzing process
variables data in order to forecast faults occurrences. FFS is
proposing new idea in detecting faults. Current techniques used in
faults detection are based on analyzing the current status of the
system variables in order to check if the current status is fault or not.
FFS is using forecasting techniques to predict future timing for faults
before it happens. Proposed model is applying subset modeling
strategy and Bayesian approach in order to decrease dimensionality
of the process variables and improve faults forecasting accuracy. A
practical experiment, designed and implemented in Okayama
University, Japan, is implemented, and the comparison shows that
our proposed model is showing high forecasting accuracy and
BEFORE-TIME.
Abstract: A geothermal power plant multiple simulator for
operators training is presented. The simulator is designed to be
installed in a wireless local area network and has a capacity to train
one to six operators simultaneously, each one with an independent
simulation session. The sessions must be supervised only by one
instructor. The main parts of this multiple simulator are: instructor
and operator-s stations. On the instructor station, the instructor
controls the simulation sessions, establishes training exercises and
supervises each power plant operator in individual way. This station
is hosted in a Main Personal Computer (NS) and its main functions
are: to set initial conditions, snapshots, malfunctions or faults,
monitoring trends, and process and soft-panel diagrams. On the other
hand the operators carry out their actions over the power plant
simulated on the operator-s stations; each one is also hosted in a PC.
The main software of instructor and operator-s stations are executed
on the same NS and displayed in PCs through graphical Interactive
Process Diagrams (IDP). The geothermal multiple simulator has been
installed in the Geothermal Simulation Training Center (GSTC) of
the Comisi├│n Federal de Electricidad, (Federal Commission of
Electricity, CFE), Mexico, and is being utilized as a part of the
training courses for geothermal power plant operators.
Abstract: Transmission and distribution lines are vital links between the generating unit and consumers. They are exposed to atmosphere, hence chances of occurrence of fault in transmission line is very high which has to be immediately taken care of in order to minimize damage caused by it. In this paper Discrete wavelet transform of voltage signals at the two ends of transmission lines have been analyzed. The transient energy of the detail information of level five is calculated for different fault conditions. It is observed that the variation of transient energy of healthy and faulted line can give important information which can be very useful in classifying and locating the fault.
Abstract: In this paper we present high performance
dynamically allocated multi-queue (DAMQ) buffer schemes for fault
tolerance systems on chip applications that require an interconnection
network. Two virtual channels shared the same buffer space. Fault
tolerant mechanisms for interconnection networks are becoming a
critical design issue for large massively parallel computers. It is also
important to high performance SoCs as the system complexity keeps
increasing rapidly. On the message switching layer, we make
improvement to boost system performance when there are faults
involved in the components communication. The proposed scheme is
when a node or a physical channel is deemed as faulty, the previous
hop node will terminate the buffer occupancy of messages destined
to the failed link. The buffer usage decisions are made at switching
layer without interactions with higher abstract layer, thus buffer
space will be released to messages destined to other healthy nodes
quickly. Therefore, the buffer space will be efficiently used in case
fault occurs at some nodes.
Abstract: A new observer based fault detection and diagnosis
scheme for predicting induction motors- faults is proposed in this
paper. Prediction of incipient faults, using different variants of
Kalman filter and their relative performance are evaluated. Only soft
faults are considered for this work. The data generation, filter
convergence issues, hypothesis testing and residue estimates are
addressed. Simulink model is used for data generation and various
types of faults are considered. A comparative assessment of the
estimates of different observers associated with these faults is
included.
Abstract: Rapid progress in process automation and tightening
quality standards result in a growing demand being placed on fault
detection and diagnostics methods to provide both speed and
reliability of motor quality testing. Doubly fed induction generators
are used mainly for wind energy conversion in MW power plants.
This paper presents a detection of an inter turn stator and an open
phase faults, in a doubly fed induction machine whose stator and
rotor are supplied by two pulse width modulation (PWM) inverters.
The method used in this article to detect these faults, is based on
Park-s Vector Approach, using a neural network.
Abstract: In the normal operation conditions of a pico satellite,
conventional Unscented Kalman Filter (UKF) gives sufficiently good
estimation results. However, if the measurements are not reliable
because of any kind of malfunction in the estimation system, UKF
gives inaccurate results and diverges by time. This study, introduces
Robust Unscented Kalman Filter (RUKF) algorithms with the filter
gain correction for the case of measurement malfunctions. By the use
of defined variables named as measurement noise scale factor, the
faulty measurements are taken into the consideration with a small
weight and the estimations are corrected without affecting the
characteristic of the accurate ones. Two different RUKF algorithms,
one with single scale factor and one with multiple scale factors, are
proposed and applied for the attitude estimation process of a pico
satellite. The results of these algorithms are compared for different
types of measurement faults in different estimation scenarios and
recommendations about their applications are given.
Abstract: The new idea of analyze of power system failure with
use of artificial neural network is proposed. An analysis of the
possibility of simulating phenomena accompanying system faults and
restitution is described. It was indicated that the universal model for
the simulation of phenomena in whole analyzed range does not exist.
The main classic method of search of optimal structure and
parameter identification are described shortly. The example with
results of calculation is shown.
Abstract: In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for training and testing of the proposed approach. The performance of the models is recorded in terms of Accuracy, MAE and RMSE values. The performance of the proposed approach is better in case of Joined Model. As evidenced from the results obtained it can be concluded that Clustering and fuzzy logic together provide a simple yet powerful means to model the earlier detection of faults in the function oriented software systems.
Abstract: Wavelet transform has been extensively used in
machine fault diagnosis and prognosis owing to its strength to deal
with non-stationary signals. The existing Wavelet transform based
schemes for fault diagnosis employ wavelet decomposition of the
entire vibration frequency which not only involve huge
computational overhead in extracting the features but also increases
the dimensionality of the feature vector. This increase in the
dimensionality has the tendency to 'over-fit' the training data and
could mislead the fault diagnostic model. In this paper a novel
technique, envelope wavelet packet transform (EWPT) is proposed in
which features are extracted based on wavelet packet transform of the
filtered envelope signal rather than the overall vibration signal. It not
only reduces the computational overhead in terms of reduced number
of wavelet decomposition levels and features but also improves the
fault detection accuracy. Analytical expressions are provided for the
optimal frequency resolution and decomposition level selection in
EWPT. Experimental results with both actual and simulated machine
fault data demonstrate significant gain in fault detection ability by
EWPT at reduced complexity compared to existing techniques.
Abstract: This paper presents a computational methodology
based on matrix operations for a computer based solution to the
problem of performance analysis of software reliability models
(SRMs). A set of seven comparison criteria have been formulated to
rank various non-homogenous Poisson process software reliability
models proposed during the past 30 years to estimate software
reliability measures such as the number of remaining faults, software
failure rate, and software reliability. Selection of optimal SRM for
use in a particular case has been an area of interest for researchers in
the field of software reliability. Tools and techniques for software
reliability model selection found in the literature cannot be used with
high level of confidence as they use a limited number of model
selection criteria. A real data set of middle size software project from
published papers has been used for demonstration of matrix method.
The result of this study will be a ranking of SRMs based on the
Permanent value of the criteria matrix formed for each model based
on the comparison criteria. The software reliability model with
highest value of the Permanent is ranked at number – 1 and so on.
Abstract: Directional over current relays (DOCR) are commonly used in power system protection as a primary protection in distribution and sub-transmission electrical systems and as a secondary protection in transmission systems. Coordination of protective relays is necessary to obtain selective tripping. In this paper, an approach for efficiency reduction of DOCRs nonlinear optimum coordination (OC) is proposed. This was achieved by modifying the objective function and relaxing several constraints depending on the four constraints classification, non-valid, redundant, pre-obtained and valid constraints. According to this classification, the far end fault effect on the objective function and constraints, and in consequently on relay operating time, was studied. The study was carried out, firstly by taking into account the near-end and far-end faults in DOCRs coordination problem formulation; and then faults very close to the primary relays (nearend faults). The optimal coordination (OC) was achieved by simultaneously optimizing all variables (TDS and Ip) in nonlinear environment by using of Genetic algorithm nonlinear programming techniques. The results application of the above two approaches on 6-bus and 26-bus system verify that the far-end faults consideration on OC problem formulation don-t lose the optimality.
Abstract: In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the
kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental treestructure
algorithm. Then, by using this method, we obtained 3
distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes
prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented.
At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study.
Abstract: As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done on time especially for the critical applications. In this paper, we have explored the different predictor models to NASA-s public domain defect dataset coded in Perl programming language. Different machine learning algorithms belonging to the different learner categories of the WEKA project including Mamdani Based Fuzzy Inference System and Neuro-fuzzy based system have been evaluated for the modeling of maintenance severity or impact of fault severity. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provides relatively better prediction accuracy as compared to other models and hence, can be used for the maintenance severity prediction of the software.
Abstract: It is estimated that the total cost of abnormal
conditions to US process industries is around $20 billion dollars in
annual losses. The hydrotreatment (HDT) of diesel fuel in petroleum
refineries is a conversion process that leads to high profitable
economical returns. However, this is a difficult process to control
because it is operated continuously, with high hydrogen pressures
and it is also subject to disturbances in feed properties and catalyst
performance. So, the automatic detection of fault and diagnosis plays
an important role in this context. In this work, a hybrid approach
based on neural networks together with a pos-processing
classification algorithm is used to detect faults in a simulated HDT
unit. Nine classes (8 faults and the normal operation) were correctly
classified using the proposed approach in a maximum time of 5
minutes, based on on-line data process measurements.
Abstract: This paper addresses the problems encountered by conventional distance relays when protecting double-circuit transmission lines. The problems arise principally as a result of the mutual coupling between the two circuits under different fault conditions; this mutual coupling is highly nonlinear in nature. An adaptive protection scheme is proposed for such lines based on application of artificial neural network (ANN). ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. One of the key points of the present work is that only current signals measured at local end have been used to detect and classify the faults in the double circuit transmission line with double end infeed. The adaptive protection scheme is tested under a specific fault type, but varying fault location, fault resistance, fault inception angle and with remote end infeed. An improved performance is experienced once the neural network is trained adequately, which performs precisely when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within a quarter cycle; thus the proposed adaptive protection technique is well suited for double circuit transmission line fault detection & classification. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms.