• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.68.4-68
• /
• 2002

$\textbullet$ This report presents an implementation a set of reusable software components which use of fault-tolerance embedded controller for railway signalling systems. These components can be used in real-time applications without application reprogramming. $\textbullet$ This library runs under VxWorks operating system and is oriented on real-time embedded systems. The library includes fault detection, fault containment, checkpointing and recovery components. $\textbullet$ The library enables to support high-speed response to fault occurrence in application software. Garbage collector together with VxWorks Watchdog provides both dead tasks detection and useless resources removing to avoid an overflow. Control flow...

• Nuclear Engineering and Technology
• /
• v.36 no.3
• /
• pp.210-218
• /
• 2004

In the interest of nuclear power plant safety, a self-diagnostic monitoring system (SDMS) is needed to monitor defects in safety-related components. An air-operated valve (AOV) is one of the components to be monitored since the failure of its operation could potentially have catastrophic consequences. In this paper, a model of the AOV is developed with the parameters that affect the operational characteristics. The model is useful for both understanding the operation and correlating parameters and defects. Various defects are introduced in the experiments to construct a fault library, which will be used in a pattern recognition approach. Finally, the validity of the fault library is examined.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.8
• /
• pp.2497-2504
• /
• 2000

This paper is aimed at defining items of fault, and then constructing rules of fault diagnosis and recovery using INBANCA technology for the purpose of managing the weh server. The fault items of web server consist of the process fault, server overload, network interface fault, configuration and performance fault. Based on these items, the actual fault management is carried out fault referencing. In order to reference the fault, we have formulated the system-level fault diagnosis production rule and the service-level fault diagnosis rule, conjunction with translating management knowledge into active network. Also, adaptive recovery mechanism of web server is applied to defining recovery rule and constructing case library for case-based web server fault recovery. Finally, through the experiment, fault environment and applicability of each proposed production rule and recovering scheme are presented to verify justification of proposed diagnosis rules and recovery mechanism for fault management. An intelligent case-based fault management scheme proposed in this paper can minimize an effort of web master to remove fault incurred web administration and operation.

• Journal of Internet Computing and Services
• /
• v.9 no.5
• /
• pp.185-193
• /
• 2008

An MTBF(mean time between failures) of large scale parallel systems is known to be only an order of several hours, and large computations sometimes result in a waste of huge amount of CPU time, However. the MPI(Message Passing Interface), a de facto standard for message passing parallel programming, suggests no possibility to handle such a problem. In this paper, we propose an application-level fault tolerant computation system, purely on the basis of the current MPI standard without using any non-standard fault tolerant MPI library, that can be used for general scientific synchronous parallel computation.

• The KIPS Transactions:PartA
• /
• v.12A no.5 s.95
• /
• pp.421-426
• /
• 2005

In a large scale parallel computation, some processor or communication link failure results in a waste of huge amount of CPU hours. However, MPI in its current specification gives the user no possibility to handle such a problem. In this paper, we propose an application-level fault tolerant linear system solver by using an MPMD-type asynchronous iteration, purely on the basis of the MPI standard without using any non-standard fault-tolerant MPI library.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.40 no.1
• /
• pp.30-38
• /
• 2003

UDP library of Verilog HDL has been used for simulation of digital systems. But it takes a lot of time and efforts to generate a gate-level library equivalent to the UDP library manually due to the characteristic of UDP that does not support synthesis. It is indispensable to generate equivalent gate-level model in testing the digital systems because fault coverage can be reduced without the equivalent gate-level models. So, it is needed to automate the process of generating the equivalent gate-level models. An algorithm to solve this problem has been proposed, but it is unnecessarily complex and time-consuming. This paper suggests a new improved algorithm to implement the conversion to gate-level models, which exploits the characteristic of UDP Experimental results are demonstrated to show the effectiveness of the new algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.6
• /
• pp.1065-1070
• /
• 2009

This paper presents the new technique of modeling using Dynamic Link Library(DLL) in ElectroMagnetic Transients Program - Restructured Version(EMTP-RV) in which we have simplified the procedures of OverCurrent Relay(OCR) modeling. The DLL function is designed to allow EMTP-RV users to develop advanced program model modules and interface them directly and intimately with the EMTP-RV engine. The modeled OCR is verified by simulating the various fault cases in the distribution system. Also, the performance for the modeling of OCR using DLL is compared with that of the method using the control components of EMTP-RV and using EMTP/MODELS. The results show the validity of modeled OCR and the effectiveness of the method using DLL function.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.3
• /
• pp.654-662
• /
• 2016

As the number of gates and basic events in fault trees increases, it becomes difficult to calculate the exact probability of the top event. In order to overcome this difficulty the BDD methodology can be used to calculate the exact top event probability for small and medium size fault trees in short time. Fault trees are converted to BDD by using CUDD library functions and a failure path search algorithm is proposed to calculate the exact top event probability. The backward search algorithm is more efficient than the forward one in finding failure paths and in the calculation of the top event probability. This backward search algorithm can reduce searching time in the identification of disjoint failure paths from BDD and can be considered as an effective tool to find the cut sets and the minimal cut sets for the given fault trees.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2008.03a
• /
• pp.187-194
• /
• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer's performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.02a
• /
• pp.239-240
• /
• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.