• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.233-247
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• 2008

Process monitoring technology is able to detect the faults and the process changes which occur in a process unpredictably, which makes it possible to find the reasons of the faults and get rid of them, resulting in a stable process operation, high-quality product. Statistical process monitoring method based on data set has a main merit to be a tool which can easily supervise a process with the statistics and can be used in the analysis of process data if a high quality of data is given. Because a real process has the inherent characteristics of nonlinearity, non-Gaussianity, multiple operation modes, sensor faults and process changes, however, the conventional multivariate statistical process monitoring method results in inefficient results, the degradation of the supervision performances, or often unreliable monitoring results. Because the conventional methods are not easy to properly supervise the process due to their disadvantages, several advanced monitoring methods are developed recently. This review introduces the theories and application results of several remarkable monitoring methods, which are a nonlinear monitoring with kernel principle component analysis (KPCA), an adaptive model for process change, a mixture model for multiple operation modes and a sensor fault detection and reconstruction, in order to tackle the weak points of the conventional methods.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.224-229
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• 2020

We propose a machine learning-based method for proactively detecting faults in LDPE processes and predicting equipment lifespan. It is important to detect and prevent unexpected faults in chemical processes in order to maximize safety and productivity. Since LDPE process is a high-pressure process up to 3,000 kg/㎠g or more, once ESD occurs, it can result in productivity loss due to increased maintenance periods. By collecting key variables operation data of the process and using unsupervised machine leaning methods, we developed a fault detection model which detected 4 ESDs 2.4 days prior to the occurrence. In addition, it was confirmed that the life expectancy of a hyper compressor can be predicted by using the physically significant key variables.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.10A
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• pp.1113-1122
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• 2004

This paper is basically to achieve the high-availability and high-reliability of the control system from the implementation of the fault-tolerant system using the hot-standby sparing technique. To meet the objective, we design and implement a board with fault tolerance I/O bus to detect the fault. Warm-standby sparing technique is the fault tolerance technique usually used for switching control system in present. This technique can be easily implemented, but can not detect the fault quickly and can malfunction because of the hardware fault. The hot-standby sparing fault tolerant technique implemented in this paper is consists of dual processor modules and a I/O processor using fault tolerant I/O bus. The proposed method can find the faults as soon as possible, so it can prevent from wrong operation. Also it is possible to normal re-service due to the short recovering time. To implement the fault-tolerant dual system with fault detection be, two daughter, called FTMA and FTIA, boards designed and implemented are applied to the system. And we also simulated the proposed method to verify the high-availability and high-reliability of the control system using Markov process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3458-3481
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• 2021

Existing methods for blind identification of linear block codes without a candidate set are mainly built on the Gauss elimination process. However, the fault tolerance will fall short when the intercepted bit error rate (BER) is too high. To address this issue, we apply the reverse algebra approach and propose a novel "two-step-screening" algorithm by solving the linear error equations on the binary Galois field, or GF(2). In the first step, a recursive matrix partition is implemented to solve the system linear error equations where the coefficient matrix is constructed by the full codewords which come from the intercepted noisy bitstream. This process is repeated to derive all those possible parity-checks. In the second step, a check matrix constructed by the intercepted codewords is applied to find the correct parity-checks out of all possible parity-checks solutions. This novel "two-step-screening" algorithm can be used in different codes like Hamming codes, BCH codes, LDPC codes, and quasi-cyclic LDPC codes. The simulation results have shown that it can highly improve the fault tolerance ability compared to the existing Gauss elimination process-based algorithms.

• Journal of Sensor Science and Technology
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• v.16 no.1
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• pp.52-61
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• 2007

Networked embedded systems using the smart device and fieldbus technologies are now found in many industrial fields including process automation and automobiles. However the discrepancy between a node's view of current time and the rest of the system can cause many difficulties in the design and implementation of a networked system. To provide a networked system with a global reference time, the problem of clock synchronization has been intensively studied over the decades. However, many of the existing solutions, which are mainly developed for large scale distributed computer systems, cannot be directly applied to embedded systems. This paper presents a fault-tolerant clock synchronization technique that can be used for a low-cost embedded system using a CAN bus. The effectiveness of the proposed method is demonstrated with a set of microcontrollers and DC motor-based actuators.

• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.17-26
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• 1993

Process fault diagnosis is a complicated matter because quality control problems can result from a variety of causes. These causes include problems with electrical components, mechanical components, human errors, job justification errors, and air conditioning influences. In order to make the system run smoothly with minimum delay, it is necessary to suggest heuristic remedies for the detected faults. Hence, this paper describes a heuristic methodology of fault diagnosis that is performed using statistical patterns generated by quality characteristics The proposed methodology is described briefly as follows: If a sample pattern generated by random variables is similar to the number of prototype patterns, the sample pattern may be matched by any prototype pattern among them to be resembled. This concept is based on the similarity between a sample pattern and the matched prototype pattern. The similarity is calculated as the weighted average of squared deviation, which is expressed as the difference between the relative values of standard normal distribution to be transformed by the observed values of quality characteristics in a sample pattern and the critical values of the corresponding ones in a matched prototype pattern.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.3
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• pp.199-205
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• 2011

This paper proposes a framework to predict the mechanical fault of hot rolling mill system (HRMS). The optimum process of HRMS is usually identified by the rotating velocity of working roll. Therefore, observing the velocity of working roll is relevant to early know the HRMS condition. In this paper, we propose the framework which consists of two methods namely spectrum matrix which related to case-based fast Fourier transform(FFT) analysis, and three dimensional condition monitoring based on novel visualization. Validation of the proposed method has been conducted using vibration data acquired from HRMS by accelerometer sensors. The acquired data was also tested by developed software referred as hot rolling mill facility analysis module. The result is plausible and promising, and the developed software will be enhanced to be capable in prediction of remaining useful life of HRMS.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.122-124
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• 2000

As the complexity of power systems increases, especially in the case of multiple faults or incorrect operation of protective devices, fault diagnosis requires new and systematic methods to the reasoning process, which improves both its accuracy and its efficiency. Therefore this paper proposes a method of the modeling of protection systems and fault diagnosis in power systems using Fuzzy Petri Nets (FPN). The proposed method can reduce processing time and increase accuracy when compared with the traditional methods. And also this method can cover online processing of real-time data from SCADA (Supervisory Control and Data Acquisition).

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.21-23
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• 2006

As a general rule for evaluating dependability of a system, reliability is commonly considered which barely rays attention to the system behavior, however the estimation is based on the assumption of a fault-frost system, which may be impracticable and inaccurate especially for complicated system. This paper introduces a security and dependability integrated approach to analyze the availability of a fault-active system both from dependability and security points of view. Two fault modes involved are discussed about the impairment to the system reliance. The approach can be well applied to estimate and quantify the attribute of system robustness with the help of Markov chain process, which is good at solving status related problem. The comparison result between dual system and IEC61850-based almighty backup system is shown to sup-port the suggested approach.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2230-2232
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• 2004

A residual generation scheme that can be employed in the process fault detection and isolation systems for a class of nonlinear (control) systems is suggested. Although the scheme is a kind of observer scheme, the design of the observers employed for residual generation is very simple and the order of the observer is very low. In spite of the simplicity, the residual generation scheme provides the same information for the detection and isolation of the anticipated faults as the conventional multiple observer based schemes. The residuals may be structured so that fault isolation can be performed by pre-selected logic. An FDIS using the residual generation scheme is constructed and evaluated for a nonlinear DC motor system.