• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.9
• /
• pp.28-34
• /
• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.3
• /
• pp.365-373
• /
• 2018

In this paper, inter-turn fault diagnosis of the interior permanent magnet synchronous motor (IPMSM) is performed in offline state by linking the finite element analysis (FEA) tool and control simulation tool. In order to diagnose the inter-turn fault, it is important to select the current value to determine the fault. First, the basic principles for inter-turn fault diagnosis of IPMSM are explained and co-analysis model for fault diagnosis is constructed. Further, in order to select the appropriate high frequency voltage, the change of the current value to be judged as failure was analyzed at various voltage and frequency conditions, and the change of the current value according to the number of the failed windings was analyzed. Finally, the current value to be judged as failure is selected.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.66 no.4
• /
• pp.194-199
• /
• 2017

Power transformers are an important factor for power transmission and cause fatal losses if faults occur. Various diagnostic methods have been applied to predict the failure and to identify the cause of the failure. Typical diagnostic methods include the IEC diagnostic method, the Duval diagnostic method, the Rogers diagnostic method, and the Doernenburg diagnostic method using the ratio of the main gas. However, each diagnostic method has a disadvantage in that it can't diagnose the state of the power transformer unless the gas ratio is within the defined range. In order to solve these problems, we propose a diagnosis method using ELM based intelligent algorithm and fuzzy membership function. The final diagnosis is performed by multiplying the result of diagnosis in the four diagnostic methods (IEC, Duval, Rogers, and Doernenburg) by the fuzzy membership values. To show its effectiveness, the proposed fault diagnostic system has been intensively tested with the dissolved gases acquired from various power transformers.

• Journal of Korea Multimedia Society
• /
• v.25 no.11
• /
• pp.1547-1556
• /
• 2022

As mechanical facilities are interacting with each other, the failure of some equipment can affect the entire system, so it is necessary to quickly detect and diagnose the abnormality of mechanical equipment. This study proposes a deep learning model that can effectively diagnose abnormalities in rotating machinery and equipment. CNN is widely used for feature extraction and LSTMs are known to be effective in learning sequential information. In LSTM, the number of parameters and learning time increase as the length of input data increases. In this study, we propose a method of segmenting an input segment signal into shorter-length sub-segment signals, sequentially inputting them to CNN through a time-distributed method for extracting features, and inputting them into LSTM. A failure diagnosis test was performed using the vibration data collected from the motor for ventilation equipment installed at the urban railway station. The experiment showed an accuracy of 99.784% in fault diagnosis. It shows that the proposed method is effective in the fault diagnosis of rotating machinery and equipment.

• 연구논문집
• /
• s.34
• /
• pp.47-58
• /
• 2004

Power plants rely on air operated valves for the proper operation of many plant system. Many significant problems arise in vital systems of power plants due to air operated valve failures. This paper deals with the diagnosis technique and data acquisition method of an AOV actuator peformance. We constructed AOV diagnosis system and performed some tests to find out whether an AOV actuator was properly designed.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.1
• /
• pp.53-62
• /
• 2023

Due to the recent increase in new and renewable energy, gas turbine generators start and stop every day to supply high-quality power, and accordingly, the life span of high-temperature parts is shortened and the failure of combustion chamber temperature sensors increases. Therefore, in this study, we proposed a fuzzy logic-based failure diagnosis algorithm that can accurately diagnose and systematically detect the failure of the sensor when the dual temperature sensor used for gas turbine control fails, and to confirm the usefulness of the proposed algorithm We tried to confirm the usefulness of the proposed algorithm by performing various simulations under the matlab/simulink environment.

• Health Policy and Management
• /
• v.32 no.3
• /
• pp.323-329
• /
• 2022

Background: This study aimed to identify the characteristics of the referral and return of patients to clinics in the endocrinology and cardiology departments at the National Health Insurance Service Ilsan Hospital to evaluate the "referral and return of patients to clinics" program and reduce the rate of returning patients. Methods: From May 2018 to December 2020, we identified the number of visits to referral hospitals and hospital usage status at Ilsan Hospital after returning to clinics. We also identified the patients who returned to Ilsan Hospital within 6 months, defined as "failure to transport," among those recommended to be transported to clinics of the Medical Cooperation Center. Additionally, we evaluated the characteristics of the "failure to transport" patients. Results: Among the returning patients, the rate of visiting Ilsan Hospital within 6 months was higher in cardiology than in endocrinology (25.1% vs. 16.7%). Older age, more severe disease, and more number of visits to the department were associated with a high rate of failure to transport. The rate of failure to return was low in cases diagnosed with hyperlipidemia/lipoprotein metabolism disorder. With respect to diabetes, the rate of failure to transport differed according to each type of diagnosis of diabetes. Conclusion: The success rate of the "referral and return of patient to clinics" program differed based on each patient's characteristics, department of visit, and diagnosis. Individualizing according to the visit department and diagnosis is required to ensure successful transfers, and infrastructure expansion and institutional arrangements must be facilitated.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.1
• /
• pp.127-138
• /
• 1994

The application of fault tree technique to the analysis of compressor failure is considered. The techniques involve the decomposition of the system into a form of fault tree where certain basic events lead to a specified top event which signifies the total failure of the system. In this paper, fault trees are made by using fault train of screw type air compressor failure. The fault trees are used to obtain minimal cut sets from the modes of system failure and, hence the system failure rate for the top event can be calculated. The method of constructing fault trees and the subsequent estimation of reliability of the system is illustrated through compressor failure. It is proved that FTA is efficient to investigate the compressor failure modes and diagnose system.

• Proceedings of the KIEE Conference
• /
• 2005.07a
• /
• pp.277-279
• /
• 2005

Recently, due to the increase of power conversion devices and nonlinear loads with the development of information, communication and control technologies, the instantaneous minute interruption factors such as voltage & current harmonics, surge occurring frequency, instantaneous voltage variation, voltage unbalance, flicker etc. have greatly threatened the power quality, and the deterioration of electric power facilities and the functional error of controllers are increasing. As such an instantaneous minute interruption appears to be small and local, accurate evaluation with measurement is difficult and total analysis system is required through a wide range of power quality effect analysis such as the simultaneous measurement on various power supply phenomena and the analysis on the interrelation with system loads. Most of conventional power quality diagnosis equipments have beer developed and applied, which were able to measure the stability rate of frequency, the stability rate of voltage, the electricity-failure duration etc, However, they were insufficient to analyze the system present situation, understand the cause of the failure occurred by the problem of power quality and analyze out the phenomena. Accordingly, this study will address the development of the system for a wide range of power quality diagnosis over the present level, the system for supporting the determination such as the analysis on risk factors, failure mode and impact, the system for harmonic evaluation based on international standards(IEC 61000 Series) and the total power quality diagnosis network & system with the extension and openness as a local and national-scale broadband power quality diagnosis system.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.23 no.6
• /
• pp.501-510
• /
• 2020

Purpose: To develop a probability-based differential diagnosis for pediatric acute liver failure (PALF) based on age and socioeconomic status of the country of origin. Methods: Comprehensive literature search using PubMed, EMBASE, and SCOPUS databases was performed. Children 0-22 years of age who met PALF registry criteria were included. Articles included >10 children, and could not be a case report, review article, or editorial. No language filter was utilized, but an English abstract was required. Etiology of PALF, age of child, and country of origin was extracted from included articles. Results: 32 full text articles were reviewed in detail; 2,982 children were included. The top diagnosis of PALF in developed countries was acetaminophen toxicity (9.24%; 95% CredI 7.99-10.6), whereas in developing countries it was Hepatitis A (28.9%; 95% CredI 26.3-31.7). In developed countries, the leading diagnosis of PALF in children aged <1 year was metabolic disorder (17.2%; 95% CredI 10.3-25.5), whereas in developing countries it was unspecified infection (39.3%; CredI 27.6-51.8). In developed countries, the leading diagnosis in children aged >1 year was Non-A-B-C Hepatitis (8.18%; CredI 5.28-11.7), whereas in developing countries it was Hepatitis A (32.4%; CredI 28.6-36.3). Conclusion: The leading causes of PALF in children aged 0-22 years differ depending on the age and developmental status of their country of origin, suggesting that these factors must be considered in the evaluation of children with PALF.