• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.37-44
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• 2014

This paper proposes a new diagnosis algorithm to detect broken rotor bars (BRBs) faults in induction motors. The proposed algorithm is composed of a frequency signal dimension order (FSDO) estimator and a fault decision module. The FSDO estimator finds a number of fault-related frequencies in the stator current signature. In the fault decision module, the fault diagnostic index from the FSDO estimator is used depending on the load conditions of the induction motors. Experimental results obtained in a 75 kW three-phase squirrel-cage induction motor show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to a zoom multiple signal classification (ZMUSIC) and a zoom estimation of signal parameters via rotational invariance techniques (ZESPRIT).

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Nuclear Engineering and Technology
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• 제55권3호
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• pp.827-838
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• 2023

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and non-stationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.

• 반도체디스플레이기술학회지
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• 제18권4호
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• pp.87-91
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• 2019

The diagnosis of the failure for the existing electrical facilities was based on regular preventive maintenance, but this preventive maintenance was limited in preventing a lot of cost loss and sudden system failure. To overcome these shortcomings, fault prediction and diagnostic techniques are critical to increasing system reliability by monitoring electrical installations in real time and detecting abnormal conditions in the facility early. As the performance and quality deterioration problem occurs frequently due to the increase in the number of users of the motor pump, the purpose is to build an intelligent control system that can control the motor pump to maximize the performance and to improve the quality and reliability. To this end, a vibration sensor, temperature sensor, pressure sensor, and low water level sensor are used to detect vibrations, temperatures, pressures, and low water levels that can occur in the motor pump, and to build a system that can identify and diagnose information to users in real time.

• 문화기술의 융합
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• 제10권4호
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• pp.663-668
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• 2024

본 논문에서는 웨이퍼 이송 로봇의 고장 진단에 시간 영역에서의 통계적 분석 방법을 적용하고, 진동 및 토크 신호의 중요 특성을 파악하는 방법을 제안한다. 이를 기반으로 데이터의 차원을 축소하기 위해 주성분 분석을 사용하고, 유클리드 거리와 Hotelling의 T-제곱 통계량을 활용하여 고장 진단 알고리즘을 개발했다. 이 알고리즘은 관측된 데이터에 대해 고장 상태를 분류하는 결정 경계를 형성한다. 속도 파라미터를 고려한 데이터 분류는 진단 정확도를 향상시킴을 확인했다. 이러한 접근 방식은 고장 진단의 정확성과 효율성을 개선하는 데 기여한다.

• 조명전기설비학회논문지
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• 제27권3호
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• pp.65-78
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• 2013

To analyze influences under open fault conditions in switching devices, an integrated simulation and condition monitoring scheme for a permanent magnet synchronous generator (PMSG) based variable speed grid-connected wind turbine system are presented. Among various faults in power electronics components, the open fault in switching devices may arise when the switches are destructed by an accidental over current, or a fuse for short protection is blown out. Under such a faulty condition, the grid-side inverter as well as the generator-side converter does not operate normally, producing an increase of current harmonics, and a reduction in output and efficiency. As an effective way for a condition monitoring of generation system by online basis without requiring any diagnostic apparatus, the estimation schemes for generated voltage, flux linkage, and stator resistance are proposed and the validity of the proposed scheme is proved through comparative simulations.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.292-297
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• 2004

The conventional computerized numerical controller (CNC) of machine tools has been increasingly replaced by a PC-based open architecture CNC (OAC) which is independent of the CNC vendor. The OAC and machine tools with OAC led the convenient environment where it is possible to implement user-defined application programs efficiently within CNC. Tis paper proposes a method of operational fault cause diagnosis which is based on the status of programmable logic controller (PLC) in machine tools with OAC. The operational fault is defined as a disability state occurring during normal operation of machine tools. The faults are occupied by over 70% of all faults and are also unpredictable as most of them occur without any warning. Two diagnosis models, the switching function (SF) and the step switching function (SSF), are propose in order to diagnose the fault cause quickly and exactly. The cause of an occurring fault is logically diagnosed through a fault diagnosis system (FDS) using the diagnosis models. A suitable interface environment between CNC and develope application modules is constructed in order to implement the diagnostic functions in the CNC domain. The diagnosed results were displayed on a CNC monitor for machine operators and provided to a remote site through a web browser. The result of his research could be a model of the fault cause diagnosis and the remote monitoring for machine tools with OAC.

• 한국산업융합학회 논문집
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• 제23권5호
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• pp.825-833
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• 2020

Currently, the K-Water uses various valves that can be remotely controlled for optimal water management. Valve system fault can be classified into rotor defects, stator defects, bearing defects, and gear defects of induction motors. If the valve cannot be operated due to a gear fault, the water management operation can be greatly affected. For effective water management, there is an urgent need for preemptive repairs to determine whether gear is damaged through failure prediction diagnosis.. Recently, deep learning algorithms are being applied for valve failure diagnosis. However, the method currently applied has a disadvantage of attaching a vibration sensor to the valve. In this paper, propose a new algorithm to determine whether a fault exists using a convolutional neural network (CNN) based on the voltage and current information of the valve without additional sensor mounting. In particular, a normalized Lisasjous diagram was used to maximize the fault classification performance in the CNN-based diagnostic system.

• Journal of Electrical Engineering and Technology
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• 제2권4호
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• pp.461-467
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• 2007

The paper describes a way to diagnose machine condition based on the expert system. In this paper, an expert system-decision tree is built and experimented to diagnose and to detect machine defects. The main objective of this study is to provide a simple way to monitor machine status by synthesizing the knowledge and experiences on the diagnostic case histories of the rotating machinery. A traditional decision tree has been constructed using vibration-based inputs. Some case studies are provided to illustrate the application and advantages of the decision tree system for machine fault diagnosis.

• 대한기계학회논문집B
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• 제20권6호
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• pp.2057-2066
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• 1996

^In this paper, diagnostic technique for detecting the engine faults, especially misfire, are introduced and compared with each other under the same conditions. With all of them the instantaneous angular velocitys, measured at the flywheel, were analyzed. The techniques include the frequency analysis, auto-correlation function, velocity index, acceleration index, maximum acceleration index, and integrated torque index. Since the main driving components for the angular velocity fluctuation are both the pressure and the inertia torque, the component of the inertia torque in it must be excluded to extract the information of the combustion from the angular velocity. To do this, it is required to consider only the first half of the combustion period in the angular velocity fluctuations, which has never been proposed in the existing methods. On the basis of this fact, the results show that the most effective diagnostic technique is maximum acceleration index.