• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
• /
• v.5B no.1
• /
• pp.78-83
• /
• 2005

Currently in Japan, the use of the small wind turbine is an upward trend. There are already many well established small wind turbine generators in use and their various failures have been reported. The most commonly sighted failure is blade damage. Thus the research purpose was set to develop a simple failure diagnostic system, where an Acoustic Emission (AE) signal was produced from the failure part of a blade which was measured by AE sensor. The failure diagnostic technique was thoroughly examined. Concurrently, the damage part of the blade was imitated, the AE signal was measured, and a FFT(Fast Fourier Transform) analysis was carried out, and was compared with the output characteristic. When one sheet of a blade was damaged 40mm or more, the level was computed at which failure could be diagnosed.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2003.07a
• /
• pp.96-99
• /
• 2003

In this paper, ageing diagnostics in the large capacity oil transformer are investigated. Following items are investigated for the ageing diagnostic in transformer oils (leakage current of sensor, power consumption and temperature of transformer oil). All temperature data are gathered from daily report in the substation. The power consumption of transformer are gathered output report of APIS(Airport Power Information System). Especially, data of sensor leakage current are accumulated from the online diagnostic system for transformer oil. The temperature of transformer oils major change factor was ambient temperature and capacity of power load. The leakage current are change by oil temperature. The leakage current ware not more than 2 [nA] in summer,

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.2
• /
• pp.536-564
• /
• 2019

Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.54 no.2
• /
• pp.94-100
• /
• 2005

Abnormal symptoms on operating conditions of power transformer are monitored by a preventive diagnostic system which prevents the sudden power failure in case of quick progress of abnormal situation. The preventive diagnostic system helps plan the proper maintenance method according to the transformer conditions via accumulated data. KEPCO has adopted the preventive diagnostic system at nine of 345kV substations since 1997. Application techniques of the diagnostic sensors were settled, but diagnostic algorithm and practical use of accumulated data are not yet established. To build up the diagnostic algorithm and effective use of the preventive diagnostic system, the reliability of the data which were accumulated in a server computer is very important. This paper describes the data analysis in the server in order to advance the reliability of the accumulated data of the preventive diagnostic system. The principles and data flows of the diagnostic sensors were analyzed, and the data discrepancy between sensors and server were calibrated.

• Nuclear Engineering and Technology
• /
• v.36 no.6
• /
• pp.540-548
• /
• 2004

Check valve failure is one of the worst problems in nuclear power plants. Recently, many researches have been based on new technology using accelerometers and ultrasonic and magnetic flux detection have been carried out. Here, we have suggested a method that uses acoustic emission sensors for detecting the failures of check valves through measuring and analyzing backward leakage flow, a system that works without disassembling the check valve. For validating the suggested acoustic emission sensor methodology, we designed a hydraulic test loop with a check valve. We have assumed in this study that check valve failure is caused by disk wear or by the insertion of a foreign object. In addition, we have developed diagnostic algorithms by using a neural network model to identify the type and size of the failure in the check valve. Our results show that the proposed diagnostic algorithm with acoustic emission sensors is a good solution for identifying check valve failure without necessitating any disassembly work.

• Nuclear Engineering and Technology
• /
• v.55 no.2
• /
• pp.493-505
• /
• 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.

• Design & Manufacturing
• /
• v.16 no.4
• /
• pp.52-59
• /
• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.1
• /
• pp.157-163
• /
• 2017

This system is a diagnosis system that checks whether it causes a partial discharge of a power cable or not. PD(Partial Discharge) is detected by FBDS(Frequency Band Detection Sensor). That is, it means a acoustic sensor capable of detecting each frequency band. The wave shape of PD sound is similar to noise and is systematically generated by partial discharge. Therefore, in this paper, we could discriminate between normal and abnormal case using relative level crossing rate(RLCR) and spectrogram of frequency energy rate.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.1
• /
• pp.29-37
• /
• 2016

Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG.

• Journal of the Korean Ceramic Society
• /
• v.42 no.1
• /
• pp.11-15
• /
• 2005

The configuration of the main components and the physical structure of the Taguchi sensor, the first ceramic gas sensor mass produced, has remained virtually unaltered since its appearance 40 years ago. This device owns an excellent combination of the quality factors but is non-selective. The research efforts carried out to enhance the selectivity in this resistive gas sensor are briefly reviewed. A novel design, Capillary-attached Gas Sensor (CGS), is introduced, which employs the same ceramic components used for the fabrication of a classical Taguchi sensor but in altered geometries. CGS presents remarkable advantages from the view point of selectivity over the original design. While the steady state response of a CGS has the same significance as that of the Taguchi sensor, its transient response presents valuable diagnostic information. Fabrication and test of a prototype CGS is reported.