• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.11
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• pp.1669-1674
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• 2017

In this study, We have discussed the development of a diagnostic device to detect and prevent electrical fire due to the arc caused by contact failure and partial disconnection at the connection part of the three-phase electric heater wiring used in the industrial field. The arc caused by contact failure and partial disconnection at the connection part of the electric heater shows a change in the current effective value. Therefore, it is possible to determine whether there exists a defect by analyzing the current unbalance factor and the number of current fluctuations with the diagnostic apparatus. The three-phase unbalanced heater is considered to be capable of determining defects through periodic measurement and trend analysis of the current unbalance factor. It is also expected that this device can be used not only for electric heaters but also for detection of defects in wiring and connections of electrical equipment having a characteristic of constant load current.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.324-328
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• 2007

Vibration monitoring of rolling element bearings is probably the most established diagnostic technique for rotating machinery. Acoustic Emission (AE) Analysis is an extremely powerful technology that can be used within a wide range of applications of non destructive testing. Therefor, this paper investigates the detection methods using AE for rolling element bearings about low-speed. Two transducers, the accelerometer and acoustic emission sensor, are used to acquire data and the results are compared for the capacity of early fault detection.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.3
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• pp.292-301
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• 2014

This paper describes development of distributed type independent electro-hydraulic ballast valve remote control system with diagnostic function based on fuzzy inference using redundant Modbus communication and ethernet Modbus TCP/IP. Diagnostic function estimate degradation of system components and diagnose system faults, which results in shortage of fault maintenance time and improvement of system safety. Slave devices which control each valve and master device which command, monitor and diagnose slave system are developed. Slave devices are connected to master device with redundant Modbus networks and master device is connected to ship's integrated control system with Modbus TCP/IP. Also this paper describes development of simulator to test and confirm whether developed system can be integrated with ship's integrated control and monitoring system.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.653-656
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• 2004

This paper outlines the three main fieldbus type standards: Foundation Fieldbus; WorldFIP; and the Profibus, each of which have great advantages over traditional instrumentation networking technology. The paper shows, using their specification, how they improve traditional control and data acquisition methods. By analysing the main robust, how the can be used to distribute data around the control system, provided increased diagnostic information, are easy to implement, fault confinement, and move simple control operations from the main controller to the local environment. This will help to segment industrial plants into zones which can control themselves. The main control will then be responsibility high-level control. and interzone communications.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.518-519
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• 2014

선형 유도 전동기 구동 인버터의 스위치 결함에 대한 신뢰성 향상을 위해, 실제 온라인 기반의 고장 및 고장점 검출에 대한 연구가 활발하다. 인버터에서 스위치 고장이 발생하면, 일반적으로 전압, 전류에 고조파가 발생되고 토크의 리플이 증가하게 된다. 또한, 인버터 스위치 고장의 경우 과전류가 발생하며 다른 전자 부품에 2차 피해를 일으킬 수 있다. 본 논문에서는 센서를 통해 얻은 전류의 정보를 이용하여 고장 및 고장 스위치를 검출하는 알고리즘을 제안한다. 한 개 또는 두 개의 스위치 고장을 4개의 faulty group으로 구별하여 고장 검출이 이루어지며 제안된 알고리즘을 입증하기 위해 Matlab-Simulink을 이용한 시뮬레이션 결과와 실험 결과가 제시된다.

• Aerospace Engineering and Technology
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• v.11 no.2
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• pp.182-194
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• 2012

To develop a liquid rocket engine with high reliability and safety under constraints of limited time and budget an optimal diagnosis system for the engine needs to be developed in parallel with the development of the engine. This paper is intended to set a development direction of the diagnosis system for the liquid rocket engine through the literature survey and addresses possible engine defects, characteristics of parameters for diagnosis and diagnostic methods including real-time diagnosis, post-test/post-flight diagnosis, fault detection method, parameter circuit method and test diagnosis. In addition tasks to be performed in the design and operation phases of the engine and foreign application case of engine diagnosis are presented.

• Journal of the Korea Safety Management & Science
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• v.22 no.1
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• pp.1-8
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• 2020

Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.

• Journal of Mechanical Science and Technology
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• v.18 no.9
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• pp.1537-1548
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• 2004

This paper presents a novel diagnostic technique for monitoring the system conditions and detecting failure modes and precursors based on wavelet-packet analysis of external noise/vibration measurements. The capability is based on extracting relevant features of noise/vibration data that best discriminate systems with different noise/vibration signatures by analyzing external measurements of noise/vibration in the time-frequency domain. By virtue of their localized nature both in time and frequency, the identified features help to reveal faults at the level of components in a mechanical system in addition to the existence of certain faults. A prima-facie case is made via application of the proposed approach to fault detection in scroll and rotary compressors, although the methods and algorithms are very general in nature. The proposed technique has successfully identified the existence of specific faults in the scroll and rotary compressors. In addition, its capability of tracking the severity of specific faults in the rotary compressors indicates that the technique has a potential to be used as a prognostic tool.

• Electrical & Electronic Materials
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• v.11 no.11
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• pp.1-8
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• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.