• Journal of the Korea Society of Computer and Information
• /
• v.17 no.6
• /
• pp.101-109
• /
• 2012

To develop high quality software, quality control plan is required about fault correction that is latent within software. We should describe fault correction profile properly for this. The tank and pipe model performs complex processes to calculate fault that is remove and escapes. Also, we have to know in which phase the faults were inserted, removed and escaped and know the fault detection rate at any phases. To simplify such complex process, this paper presented model to fault filtering concept. Presented model has advantage that can describe fault more shortly because need not to consider whether was involved in fault that escaped fault is inserted at any step at free step. Also, presented effort estimating model that do fetters in function of fault removal quality and productivity measure and is required in fault detection.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.36 no.1
• /
• pp.39-55
• /
• 2011

Process monitoring has been emphasized for the monitoring of complex system such as chemical processing industries to achieve the efficiency enhancement, quality management, safety improvement. Recently, ICA (Independent Component Analysis) based MSPC (Multivariate Statistical Process Control) was widely used in process monitoring approaches. Moreover, DICA (Dynamic ICA) has been introduced to consider the system dynamics. However, the existing approaches show the limitation that their performances are strongly dependent on the statistical distributions of control variables. To improve the limitation, we propose a novel approach for process monitoring by integrating DICA and LOF (Local Outlier Factor). In this paper, we aim to improve the fault detection rate with the proposed method. LOF detects local outliers by using density of surrounding space so that its performance is regardless of data distribution. Therefore, the proposed method not only can consider the system dynamics but can also assure robust performance regardless of the statistical distributions of control variables. Comparison experiments were conducted on the widely used benchmark dataset, Tennessee Eastman process (TE process), and showed the improved performance than existing approaches.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.02a
• /
• pp.499-499
• /
• 2012

This study was evaluated on etcher of capacitive coupled plasmas with OES (Optical Emission Spectroscopy) and impedance by VI probe that are widely used for process control and monitoring at semiconductor industry. The experiment was operated at conventional Ar and C4F8 plasma with variable change such as pressure and addition of gas (Atmospheric Leak: N2 and O2), RF, pressure, that are highly possible to impact wafer yield during wafer process, in order to observe OES and VI Probe signals. The sensitivity change on OES and Impedance by Vi probe was analyzed by statistical method to determine healthy of process. The main goal of this study is to understand unwanted tool performance to eventually improve productive capability. It is important for process engineers to actively adjust tool parameter before any serious problem occurs.

• Journal of the Korean Institute of Gas
• /
• v.7 no.3 s.20
• /
• pp.24-31
• /
• 2003

To operate process plant safely and economically, process monitoring is very important. There are a great number of data acquired through distributed control system and process information system. Fault monitoring is the task with difficulties owing to not only the huge amount of data, but also nonlinearity of chemical processes. In this research, the program, REFA, based on PCA and functional link associative neural network has developed. REFA has better learning capabilities, generalization abilities, and shorter learning time than existing neural network programs. In this work its usefulness has proven by application to Tennessee Eastman process.

• Journal of the Korean Institute of Gas
• /
• v.9 no.3 s.28
• /
• pp.38-43
• /
• 2005

There is monitored amount operation variables and controlled by operating conditions and loads at many facilities using gas also chemical plants. The process fault which can be indicated by operators, is occurred when the abnormal state was accumulated continuously owing to physical failure, external disturbance or human error. This is studied a Early Warning System which is to estimate process status by real-time monitoring operation variables and to early warning before it will be occurred process fault.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.5
• /
• pp.175-181
• /
• 2002

In this paper, fault tolerant digital governor is designed to realize ceaseless controlling and to improve the reliability of control system. Designed digital governor huts duplex I/O module and triplex CPU module and also 2 out of 3 voting algorithm and self diagnostic ability. The Processor module of the system(SIDG-3000) is developed based on 32 Bit industrial microprocessor, which guaranteed high quality of the module and SRAM for data also SRAM for command are separated. The process module also includes inter process communication function and power back up function (SRAM for back-up). System reliability is estimated by using the model of Markov process. It is shown that the reliability of triplex system in mission time can be dramatically improved compared with a single control system Designed digital governor system is applied after modelling of the steam turbine generator system of Buk-Cheju Thermal Power Plant. Simulation is carried out to prove the effectiveness of the designed digital governor system

• Journal of Information Processing Systems
• /
• v.6 no.4
• /
• pp.537-552
• /
• 2010

Software Debugging is the most time consuming and costly process in the software development process. Many techniques have been proposed to isolate different faults in a program thereby creating separate sets of failing program statements. Debugging in parallel is a technique which proposes distribution of a single faulty program segment into many fault focused program slices to be debugged simultaneously by multiple debuggers. In this paper we propose a new technique called Faulty Slice Distribution (FSD) to make parallel debugging more efficient by measuring the time and labor associated with a slice. Using this measure we then distribute these faulty slices evenly among debuggers. For this we propose an algorithm that estimates an optimized group of faulty slices using as a parameter the priority assigned to each slice as computed by value of their complexity. This helps in the efficient merging of two or more slices for distribution among debuggers so that debugging can be performed in parallel. To validate the effectiveness of this proposed technique we explain the process using example.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.9
• /
• pp.1583-1589
• /
• 2016

This paper proposes an improved spread spectrum time domain reflectometry (ISSTDR) using time-frequency correlation and reference signal elimination method in order to have more accurate fault determination and location detection than conventional (SSTDR) despite increased signal attenuation due to the long distance to cable fault location. The proposed method has a two-step process: the first step is to detect a peak location of the reference signal using time-frequency correlation analysis, and the second step is to detect a peak location of the correlation coefficient of the reflected signal by removing the reference signal. The proposed method was validated through comparison with existing SSTDR methods in open-and short-circuit fault detection experiments of low voltage power cables. The experimental results showed that the proposed method can detect correlation coefficients at fault locations accurately despite reflected signal attenuation so that cable faults can be detected more accurately and clearly in comparison to existing methods.

• Journal of Computing Science and Engineering
• /
• v.9 no.3
• /
• pp.142-154
• /
• 2015

Fault localization techniques help locate faults in source codes by exploiting collected test information and have shown promising results. To precisely locate faults, the techniques require a large number of test cases that sufficiently exercise the executable statements together with the label information of each test case as a failure or a success. However, during the process of software development, developers may not have high-coverage test cases to effectively locate faults. With the test case generation techniques, a large number of test cases without expected outputs can be automatically generated. Whereas the execution results for generated test cases need to be inspected by developers, which brings much manual effort and potentially hampers fault-localization effectiveness. To address this problem, this paper presents a method to select a few test cases from a number of test cases without expected outputs for result inspection, and in the meantime selected test cases can still support effective fault localization. The experimental results show that our approach can significantly reduce the number of test cases that need to be inspected by developers and the effectiveness of fault localization techniques is close to that of whole test cases.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.5
• /
• pp.520-525
• /
• 1997

This paper presents a fault detection and diagnosis methodology based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. In the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model is used to generate those candidates. The weight is determined from dynamic simulation. Using WSM, the methodology can generate the cause candidates and rank them according to the probability. Second, the fault propagation trends identified from the partial or complete sequence of measurements are compared with the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies, and the results showed satisfactory diagnostic resolution.