• Journal of IKEEE
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• v.24 no.1
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• pp.132-139
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• 2020

The ionizer module used in this air cleaner supplies high voltages of 3.5KV / -4KV to the discharge electrode HV+ / HV- using a winding transformer to generate positive and negative ions by electric field radiation of carbon fiber brush. The ionizer module circuit using the existing MCU has the disadvantage of large PCB size and expensive price, and the gate driver chip using the existing ring oscillator has oscillation period sensitive to PVT (Process-Voltage-Temperature) fluctuation and there is risk of fire or electric shock because there is no fault detection function by short circuit of HV+ and GND as well as HV- and GND. Therefore, in this paper, even though PVT fluctuates, by using 7-bit binary up counter, HV+ voltage reaches the target voltage by adjusting oscillation period. And an HV+ short fault detection circuit for detecting a short circuit between HV+ and GND, an HV- short fault detection circuit for detecting a short circuit between HV- and GND, and an OVP (Over-Voltage Protection) for detecting that HV+ rises above an overvoltage are newly proposed.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.719-722
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• 1998

Fault simulation for large and complex sequential circuits is highly cpu-intensive task in the intergrated circuit design process. In this paper, we propose CM-SIM, a concurrent fault simulator which employs an optimal memory management strategy and simple list operations. CM-SIM removes inefficiencies and uses new dynamic memory management strategies, using contiguous array memory. Consequently, we got improved performance and reduced memory usage in concurrent fault simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.109-114
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• 1993

It is well known that neural networks can be used to diagnose multiple faults to some limited extent. In this work we present a Multiple Fault Diagnosis Method (MFDM) via neural network which can effectively diagnose multiple faults. To diagnose multiple fault, the proposed method finds the maximum value in the output nodes of the neural network and decreases the node value by changing the hidden node values. This method can find the other faults by computing again with the changed hidden node values. The effectiveness of this method is explored through a neural-network-based fault diagnosis case study of a fluidized catalytic cracking unit (FCCU).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1276-1280
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• 2009

This paper presents a fault diagnosis expert system for 765kV substation. The proposed system includes the topology processor and intelligent alarm processing subsystems. This expert system estimates the fault section through the inference process using heuristic knowledge and the output of topology processor and intelligent alarm processing system. The rule-base of this expert system is composed of basic rules suggested by Korea Electric Power Corporation and heuristic rules. This expert system is developed using PROLOG language. Also, user friendly Graphic User Interface is developed using visual basic programming in the windows XP environment. The proposed expert system showed a promising performance through the several case studies.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1136-1138
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• 1999

This paper describes a new method of the modeling of protection system and fault diagnosis in power systems using Petri nets. The Petri net models of protection system are compose of the operating process of protective devices and the fault diagnosis process. Fault diagnosis model which makes use of the nature of Petri net is developed to overcome the drawbacks of methods that depend on operator knowledge. The proposed method can reduce processing time and increase accuracy when compared with the traditional methods. And also this method covers online processing of real-time data from SCADA.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.2
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• pp.287-291
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• 2013

Fault Diagnosis is a process of detecting and isolating faults in a system. On demanding for safety and high reliability systems make it important for some reasons such as economical and environmental incentives. Especially embedded technology and IT technology combined with precise sensing techniques has been doing well developed and applied to fault diagnosis and prognosis in industrial systems like as automotive, ship, heavy industry and aerospace as well. This paper, as an empirical application of diesel engine, presents a method how to get raw data from physical systems, what to consider for successful implementation and which theoretic mathematical models should be applied. In a sense of system level Adaptive Filtering (we call Modified Kalman Filter) and a unit of part level Hidden Markov Process was developed and applied.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.47-54
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• 2009

Spin coaters are the essential instruments in micro-fabrication processes, which apply uniform thin films to flat substrates. In this research, a spin coater diagnosis system is developed to detect the abnormal operation of TFT-LCD process in real time. To facilitate the real-time data acquisition and analysis, the circular-buffered continuous data transfer and the short-time Fourier transform are applied to the fault diagnosis system. To determine whether the system condition is normal or not, a steady-state detection algorithm and a frequency spectrum comparison algorithm using confidence interval are newly devised. Since abnormal condition of a spin coater is rarely encountered, algorithm is tested on a CD-ROM drive and the developed program is verified by a function generator. Actual threshold values for the fault detection are tuned in a spin coater in process.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.53-66
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• 1994

In this paper, a fuzzy expert system is developed for fault diagnoisis of a drum-type boiler system in fossil power plants. The develped fuzzy espert system is composed of knowledge base, fuzzification module, knowledge base process module, knowledge base management module, inference module, and linguistic approximation module. The main objective of the fuzzy expert system is to check the states of the system including the drum level and detect faults such as the feedwater flow sensor fault, feedwater flow control valve fault, and water wall bube rupture. The fuzzy expert system diagnoses faults using process values, manipulated values, and knowledge base which is built via interviews and questionaries with the experts on the plant operations. Finally, the validity of the developed fuzzy expert system is shown via experiments using the digital simulator for boiler system is Seoul Power Plant Unit 4.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2272-2275
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• 2002

PCA(Principle Component Analysis) has emerged as a useful tool for process monitoring and fault diagnosis. The general approach requires the user to identify the root cause by interpreting the residual or principle components. This could be tedious and often impossible for a large process. In this paper, PCA scheme is combined with the FCM-based fault diagnostic algorithm to enhance the diagnosistic results. The implementation of the PCA-FCM based fault diagnostic system is done and its application is illustrated on the two-tank system.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.