• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.276-280
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• 2016

Due to the increasing interest in safety and consistent product quality over a past few decades, demand for effective quality monitoring and safe operation in the modern industry has propelled research into statistical based fault detection and diagnosis methods. This paper describes the application of Hotelling $T^2$ index based Principal Component Analysis (PCA) method for fault detection and diagnosis in industrial processes. Multivariate statistical process control techniques are now widely used for performance monitoring and fault detection. Conventional methods such as PCA are suitable only for steady state processes. These conventional projection methods causes false alarms or missing data for the systems with transient values of processes. These issues significantly compromise the reliability of the monitoring systems. In this paper, a reliable method is used to overcome false alarms occur due to varying process conditions and missing data problems in transient states. This monitoring method is implemented and validated experimentally along with matlab. Experimental results proved the credibility of this fault detection method for both the steady state and transient operations.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.924-931
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• 1999

Most conventional protection relays are based on processing information in the spectrum that is close to or at power frequency. It is, however, widely known that faults on transmission lines produce frequency components of a wide range. High frequency signals caused by sudden changes in system voltage that occurs in the immediate post-fault period are generally outside the bandwidth of receptibility of most protection scheme. In this respect, a specially designed stack tuner is connected to the coupling capacitor of CVT, in order to capture the high frequency signals. Digital signal processing is then applied to the captured information to determine whether the fault is inside or outside the protected zone, and to discriminate the fault type. In this paper, modal transform is not applied to fault generated signals, because signals which are converted by modal transform are not have an information of each phase any longer. Instead, using peak voltage value of data windows is able to discriminate fault type. The paper concludes by presenting fault detection and discrimination of various faults on transmission line which are based on extensive simulation studies carried out on a typical 154kV Korean transmission line, using the EMTP software.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.202-207
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• 2016

Checkpoint placement is an effective fault tolerance technique against transient faults in which the task is re-executed from the latest checkpoint when a fault is detected. In this paper, we propose a new checkpoint placement strategy separating data saving and fault detection processes that are performed together in conventional checkpoints. Several fault detection processes are performed in one checkpoint interval in order to decrease the latency between the occurrence and detection of faults. We address the placement method of fault detection processes to maximize the probability of successful execution of a task within the given deadline. We develop the Markov chain model for a real-time task having the proposed checkpoints, and derive the optimal fault detection and checkpoint interval.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.408-415
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• 2020

In this study, the AE-SOM method, which combines auto-encoder and self-organizing map, is used to detect and diagnose faults in EVA production process. Then, the fault propagation pathways are identified using Granger causality test. One year and seven months of operation data were obtained to detect faults of the process, and the process variables of the autoclave reactor are mainly analyzed. In the data pretreatment process, the data are standardized and 200 samples of each grade are randomly chosen to obtain a fault detection model. After that, the best matching unit (BMU) of each grade is confirmed by applying AE-SOM. The faults are determined based on each BMU. When a fault is found, the most causative variable of the fault is identified by using a contribution plot, and the fault propagation pathway is identified by Granger causality test. The prognostic of the two shutdowns is detected, and the fault propagation pathway caused by the faulty variable was analyzed.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.176-180
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• 1992

At the KACC'91 conference, we proposed a method of automatic detection of shape of the faulty holes of a shadow mask which is used in a cathode-ray tube of a color television. In this method, the image data are taken from two areas of the mask with CCD camera. Comparing the shape of holes in these two areas by use of a signal processing technique, we can find any fault in the shape of holes. This paper describes the effect of smoothing filters of effectively finding the faulty holes from the difference image data. A computer simulation and actual experiment with a shadow mask have shown that this method of fault detection is very effective for practical use.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.410-418
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• 1999

This paper describes the use of residual and parameter identification methods for fault detection in an air handling unit. Faults can be detected by comparing expected condition with the measured faulty data using residuals. Faults can also be detected by examining unmeasurable parameter changes in a model of a controlled system using a system identification technique. In this study, AutoRegressive Moving Average with seXtrnal input(ARMAX) and AutoRegressive with eXternal input(ARX) models with both single-input/single-input and multi-input/single-input structures are examined. Model parameters are determined using the Kalman filter recursive identification method. Regression equations are calculated from normal experimental data and are used to compute expected operating variables. These approaches are tested using experimental data from a laboratory's variable-air-volume air-handling-unit.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• IE interfaces
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• v.22 no.2
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• pp.126-134
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• 2009

As product quality and yield are essential factors in semiconductor manufacturing, monitoring the main manufacturing steps is a critical task. For the purpose, FDC(Fault detection and classification) is used for diagnosing fault states in the processes by monitoring data stream collected by equipment sensors. This paper proposes an FDC model based on decision tree which provides if-then classification rules for causal analysis of the processing results. Unlike previous decision tree approaches, we reflect the structural aspect of the data stream to FDC. For this, we segment the data stream into multiple subregions, define structural features for each subregion, and select the features which have high relevance to results of the process and low redundancy to other features. As the result, we can construct simple, but highly accurate FDC model. Experiments using the data stream collected from etching process show that the proposed method is able to classify normal/abnormal states with high accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.836-842
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• 2016

When an accident occurs in the nuclear power plant, the faulted information might mislead to the high possibility of aggravating the accident. At the Fukushima accident, the operators misunderstood that there was no core exposure despite in the processing of core damage, because the instrument information of the reactor water level was provided to the operators optimistically other than the actual situation. Thus, this misunderstanding actually caused to much confusions on the rapid countermeasure on the accident, and then resulted in multiplying the accident propagation. It is necessary to be equipped with the function that informs operators the status of instrument integrity in real time. If plant operators verify that the instruments are working properly during accident conditions, they are able to make a decision more safely. In this study, we have performed various tests for the fault detection sensitivity of an data-driven empirical model to review the usability of the model in the accident conditions. The test was performed by using simulation data from the compact nuclear simulator that is numerically simulated to PWR type nuclear power plant. As a result of the test, the proposed model has shown good performance for detecting the specified instrument faults during normal plant conditions. Although the instrument fault detection sensitivity during plant accident conditions is lower than that during normal condition, the data-drive empirical model can be detected an instrument fault during early stage of plant accidents.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1205-1208
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• 2005

By adding user interface to the usual router, an improved functional router is implemented in this paper. Due to the massive amount of spatial data processing, spatial information processing area has been rapidly grown up in recent years based on powerful computer hardware and software development. Spatial index structures are the core engine of geographic information system(GIS). Analyzing and processing of spatial information using GIS has a lot of applications and the number application will be increased in the future. However, study on the under ground is in its infancy due to invisible characteristic of this information. This paper proposes the sub-surface fault detection system using the sub-surface layer information gathered from elastic wave. Detection of sub-surface fault provides very important information to the safety of above and sub-surface man made structures. Development of sub-surface fault detection system will serve as a pre-processing system assisting the interpretation of the geologist.