Abstract: The numerous qualities of squirrel cage induction
machines enhance their use in industry. However, various faults can
occur, such as stator short-circuits and rotor failures.
In this paper, we use a technique based on the spectral analysis of
stator current in order to detect the fault in the machine: broken rotor
bars. Thus, the number effect of the breaks has been highlighted. The
effect is highlighted by considering the machine controlled by the
Direct Torque Control (DTC). The key to fault detection is the
development of a simplified dynamic model of a squirrel cage
induction motor taking account the broken bars fault and the stator
current spectrum analysis (FFT).
Abstract: In the project FleGSens, a wireless sensor network
(WSN) for the surveillance of critical areas and properties is currently developed which incorporates mechanisms to ensure information
security. The intended prototype consists of 200 sensor nodes for
monitoring a 500m long land strip. The system is focused on ensuring
integrity and authenticity of generated alarms and availability in the
presence of an attacker who may even compromise a limited number
of sensor nodes. In this paper, two of the main protocols developed
in the project are presented, a tracking protocol to provide secure
detection of trespasses within the monitored area and a protocol for secure detection of node failures. Simulation results of networks
containing 200 and 2000 nodes as well as the results of the first prototype comprising a network of 16 nodes are presented. The focus of the simulations and prototype are functional testing of the protocols
and particularly demonstrating the impact and cost of several attacks.
Abstract: Non-Destructive evaluation of in-service power
transformer condition is necessary for avoiding catastrophic failures.
Dissolved Gas Analysis (DGA) is one of the important methods.
Traditional, statistical and intelligent DGA approaches have been
adopted for accurate classification of incipient fault sources.
Unfortunately, there are not often enough faulty patterns required for
sufficient training of intelligent systems. By bootstrapping the
shortcoming is expected to be alleviated and algorithms with better
classification success rates to be obtained. In this paper the
performance of an artificial neural network, K-Nearest Neighbour
and support vector machine methods using bootstrapped data are
detailed and shown that while the success rate of the ANN algorithms
improves remarkably, the outcome of the others do not benefit so
much from the provided enlarged data space. For assessment, two
databases are employed: IEC TC10 and a dataset collected from
reported data in papers. High average test success rate well exhibits
the remarkable outcome.
Abstract: This paper aims to provide a conceptual framework to examine competitive disadvantage of banks that suffer from poor performance. Banks generate revenues mainly from the interest rate spread on taking deposits and making loans while collecting fees in the process. To maximize firm value, banks seek loan growth and expense control while managing risk associated with loans with respect to non-performing borrowers or narrowing interest spread between assets and liabilities. Competitive disadvantage refers to the failure to access imitable resources and to build managing capabilities to gain sustainable return given appropriate risk management. This paper proposes a four-quadrant framework of organizational typology is subsequently proposed to examine the features of competitive disadvantage in the banking sector. A resource configuration model, which is extracted from CAMEL indicators to examine the underlying features of bank failures.
Abstract: To estimate the risks of dam failure phenomenon, it is necessary to understand this phenomenon and the involved governing factors. Overtopping and piping are the two main reasons of earthdam failures. In the piping context, the piping is determined as a phenomenon which is occurred between two phases, the water liquid and the solid soil. In this investigation, the onset of piping and its development, as well as the movement of water in soil, are numerically approached. In this regard, a one-dimensional numerical model based on the mass-conserving finite-volume method is developed and applied in order to simulate the piping phenomenon in a continuous circular tunnel of given initial length and radius, located between upstream and downstream. The simulation result includes the time-variations of radius along the tunnel until the radius value reaches its critical and the piping phenomenon converts to overtopping.
Abstract: Detection of incipient abnormal events is important to
improve safety and reliability of machine operations and reduce losses
caused by failures. Improper set-ups or aligning of parts often leads to
severe problems in many machines. The construction of prediction
models for predicting faulty conditions is quite essential in making
decisions on when to perform machine maintenance. This paper
presents a multivariate calibration monitoring approach based on the
statistical analysis of machine measurement data. The calibration
model is used to predict two faulty conditions from historical reference
data. This approach utilizes genetic algorithms (GA) based variable
selection, and we evaluate the predictive performance of several
prediction methods using real data. The results shows that the
calibration model based on supervised probabilistic principal
component analysis (SPPCA) yielded best performance in this work.
By adopting a proper variable selection scheme in calibration models,
the prediction performance can be improved by excluding
non-informative variables from their model building steps.
Abstract: In this paper parametric analytical studies have been carried out to examine the intrinsic flow physics pertaining to the liftoff time of solid propellant rockets. Idealized inert simulators of solid rockets are selected for numerical studies to examining the preignition chamber dynamics. Detailed diagnostic investigations have been carried out using an unsteady two-dimensional k-omega turbulence model. We conjectured from the numerical results that the altered variations of the igniter jet impingement angle, turbulence level, time and location of the first ignition, flame spread characteristics, the overall chamber dynamics including the boundary layer growth history are having bearing on the time for nozzle flow chocking for establishing the required thrust for the rocket liftoff. We concluded that the altered flow choking time of strap-on motors with the pre-determined identical ignition time at the lift off phase will lead to the malfunctioning of the rocket. We also concluded that, in the light of the space debris, an error in predicting the liftoff time can lead to an unfavorable launch window amounts the satellite injection errors and/or the mission failures.
Abstract: Nowadays, hard disk is one of the most popular storage components. In hard disk industry, the hard disk drive must pass various complex processes and tested systems. In each step, there are some failures. To reduce waste from these failures, we must find the root cause of those failures. Conventionall data analysis method is not effective enough to analyze the large capacity of data. In this paper, we proposed the Hough method for straight line detection that helps to detect straight line defect patterns that occurs in hard disk drive. The proposed method will help to increase more speed and accuracy in failure analysis.
Abstract: Car failure detection is a complicated process and
requires high level of expertise. Any attempt of developing an expert
system dealing with car failure detection has to overcome various
difficulties. This paper describes a proposed knowledge-based
system for car failure detection. The paper explains the need for an
expert system and the some issues on developing knowledge-based
systems, the car failure detection process and the difficulties involved
in developing the system. The system structure and its components
and their functions are described. The system has about 150 rules for
different types of failures and causes. It can detect over 100 types of
failures. The system has been tested and gave promising results.