• Journal of Electrical Engineering and Technology
• /
• v.13 no.4
• /
• pp.1614-1622
• /
• 2018

The research related to fault diagnosis in permanent magnet synchronous motors (PMSMs) has attracted considerable attention in recent years because various faults such as permanent magnet demagnetization and short-circuited turns can occur and result in unexpected failure of motor related system. Several conventional current and back electromotive force (BEMF) analysis techniques were proposed to detect certain faults in PMSMs; however, they generally deal with a single fault only. On the contrary, cases of multiple faults are common in PMSMs. We propose a fault diagnosis method for PMSMs with single and multiple combined faults. Our method uses three phase BEMF voltages based on the fast Fourier transform (FFT), support vector machine(SVM), and visualization tools for identifying fault types and severities in PMSMs. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are used to visualize the high-dimensional data into two-dimensional space. Experimental results show good visualization performance and high classification accuracy to identify fault types and severities for single and multiple faults in PMSMs.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.4
• /
• pp.315-320
• /
• 2015

This paper describes the implemenation of localization and visualization scheme to find out an ultrasonic source caused by defects of a power distribution line equipment. To increase the fault detection performance, $2{\times}4$ sensor array is configured with MEMS ultrasonic sensors, and from the sensor signals aquired, the azimuth and elevation angles of the ultrasonic source is estimated based on the delay-sum beam forming method. Also, to visualize the estimated location, it is marked on the background image. Experimental results show applicability of the present technique.

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

• 한국가스학회:학술대회논문집
• /
• 2006.11a
• /
• pp.235-240
• /
• 2006

• Structural Engineering and Mechanics
• /
• v.87 no.5
• /
• pp.431-450
• /
• 2023

In this study, it is intended to perform nonlinear time-history analyses of nuclear power plant structures (NPP) under near-fault earthquakes showing directivity pulse and fling-step characteristics. Simulation procedures based on cycloidal pulse and far-fault ground motions are also used to simulate near-fault motions showing forward-directivity and fling-step characteristics and the structural responses are compared with those of the recorded near-fault ground motions. Because it is aimed to determine specifically the pulse type characteristics of near-fault ground motions on NPPs, all the ground motions are normalized to have a PGA of 0.3 g. Depending on the obtained results it can be underlined that although near-fault ground motion has the potential to cause damage mostly on structural systems having larger periods, it may also have noticeable effects on the responses of rigid structures, like NPP containment buildings. On the other hand, simulated near-fault motions can help us to get an insight into the near-fault mechanism as well as an approximate visualization of the structural responses under near-fault earthquakes.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.55 no.7
• /
• pp.297-305
• /
• 2006

This paper summarizes a development of visualization software for protective engineering in low-voltage power systems. The study is concentrated on the following aspects. First, a software engineering method is applied for designing the object-oriented program. The design and implementation of a Graphic User Interface(GUI) and its integration to a power system framework are developed using object-oriented programming(OOP) in Visual C++. Second, we develop the short circuit analysis module that oriented a low-voltage power system. It is possible to calculate a peak, symmetrical RMS, DC component and asymmetrical fault currents for each time. And it is the first software that can calculate the fault current for single branch of three-phase system. The calculation accuracy is compared with commercial software, and the libraries of low-voltage components are served for convenience use. Third, protective engineering functions are equipped. It is possible to automatically select the circuit breaker which based on the user input characteristics and the fault current calculation and examine the protective coordination. Through the case study, we verified that the developed software can be effectively used to examine the protective engineering in low-voltage power systems.

• Nuclear Engineering and Technology
• /
• v.55 no.6
• /
• pp.2096-2106
• /
• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.146-152
• /
• 2022

Information collected from temperature, humidity, inclination, and pressure sensors using Raspberry Pi or Arduino is used in automatic constant temperature and constant humidity systems. In addition, by using it in the agricultural and livestock industry to remotely control the system with only a smartphone, workers in the agricultural and livestock industry can use it conveniently. In general, temperature and humidity are expressed in a line graph, etc., and the change is monitored in real time. The technology to visually express the temperature has recently been used intuitively by using an infrared device to test the fever of Corona 19. In this paper, the information collected from the Raspberry Pi and the DHT11 sensor is used to predict the temperature change in space through intuitive visualization and to make a immediate response. To this end, an algorithm was created to effectively visualize temperature and humidity, and data representation is possible even if some sensors are defective.

• Journal of the Korean Society of Safety
• /
• v.15 no.2
• /
• pp.36-45
• /
• 2000

Automated production system is composed of many complicated techniques and it become a very difficult task to control, monitor and diagnose this compound system. Moreover, it is required to develop an effective diagnosing technique and reduce the diagnosing time while operating the system in parallel under many faults occurring concurrently. This study develops a Modular Artificial Neural Network(MANN) which can perform a diagnosing function of multiple faults with the following steps: 1) Modularizing a complicated system into subsystems. 2) Formulating a hierarchical structure by dividing the subsystem into many detailed elements. 3) Planting an artificial neural network into hierarchical module. The system developed is implemented on workstation platform with $X-Windows^{(r)}$ which provides multi-process, multi-tasking and IPC facilities for visualization of transaction, by applying the software written in $ANSI-C^{(r)}$ together with $MOTIF^{(r)}$ on the fault diagnosis of PI feedback controller reactor. It can be used as a simple stepping stone towards a perfect multiple diagnosing system covering with various industrial applications, and further provides an economical approach to prevent a disastrous failure of huge complicated systems.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.518-520
• /
• 2004

In these days, there is a demand to develop fault current limiters(FCLs) to reduce excessive fault current and protect electrical equipments which are installed in the transmission and distribution power systems. We considered the resistive superconducting FCLs among the various kinds of FCLs. In this study, in order to develop the resistive superconducting FCL of 6.6kV 200A $3\phi$, we designed the new mask pattern for etching YBCO films by means of numerical analysis method, current limiting experiments and visualization of bubbles in films and investigated dielectric performance of the designed mask by using elecrtostatic numerical analysis method and breakdown experiments. We etched YBCO films by using the newly designed mask, connected the etched films in series and in parallel, and designed the 6.6kV resistive SFCL and then we observed the current limiting characteristics of the SFCL.