• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.382.1-382
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• 2002

This paper presents a new approach integrated Case-Based Reasoning with Self- Organization Map(SOM) in diagnosis systems. The causes of faults are obtained by case-base trained from SOM. When the vibration problem of rotating machinery occurs, this provides an exact diagnosis method that shows the fault cause of vibration problem. In order to verify the performance of algorithm, we applied it to diagnose the fault cause of the electric motor.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.57-64
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• 2024

In the industrial field, robots are used to increase productivity by replacing labors with dangerous, difficult, and hard tasks. However, failures of individual industrial robots in the entire production process may cause product defects or malfunctions, and may cause dangerous disasters in the case of manufacturing parts used in automobiles and aircrafts. Although requirements for early diagnosis of industrial robot failures are steadily increasing, there are many limitations in early detection. This paper introduces methods for diagnosing robot failures using sound-based data and deep learning. This paper also analyzes, compares, and evaluates the performance of failure diagnosis using various deep learning technologies. Furthermore, in order to improve the performance of the fault diagnosis system using deep learning technology, we propose a method to increase the accuracy of fault diagnosis based on an inference window. When adopting the inference window of deep learning, the accuracy of the failure diagnosis was increased up to 94%.

• The Journal of Korean Orthopaedic Ultrasound Society
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• v.2 no.1
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• pp.27-36
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• 2009

Musculoskeletal sonography is rapidly developing due to the merits such as relatively low cost and possibility of dynamic study. Sonography can be helpful and easily introduced for the diagnosis of the foot and ankle disease. This review tried to clarify the cause and sonographic diagnosis of common foot and ankle diseases.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.749-757
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• 2007

Recently, with increasing interested in improvement of operational reliability and the systematic maintenance activities, the RCM analysis has been applied and tried to lots of applicable industries. This study covers applying the probability of failure cause to FMEA, and proposes an analytical method for this. Also, the measures of quantitative classification for the result of failure cause probability are addressed. Based on the field data, this thesis presents an identification for causes and characteristics of failure, and reviews them periodically from the above methodologies. As using FMEA applied the probability of failure cause, we in the future can look forward to improvement of efficiency for failure diagnosis & inspection, and reliability.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.2
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• pp.252-261
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• 2014

A Bayesian network (BN) based fault diagnosis framework for semiconductor etching equipment is presented. Suggested framework contains data preprocessing, data synchronization, time series modeling, and BN inference, and the established BNs show the cause and effect relationship in the equipment module level. Statistically significant state variable identification (SVID) data of etch equipment are preselected using principal component analysis (PCA) and derivative dynamic time warping (DDTW) is employed for data synchronization. Elman's recurrent neural networks (ERNNs) for individual SVID parameters are constructed, and the predicted errors of ERNNs are then used for assigning prior conditional probability in BN inference of the fault diagnosis. For the demonstration of the proposed methodology, 300 mm etch equipment model is reconstructed in subsystem levels, and several fault diagnosis scenarios are considered. BNs for the equipment fault diagnosis consists of three layers of nodes, such as root cause (RC), module (M), and data parameter (DP), and the constructed BN illustrates how the observed fault is related with possible root causes. Four out of five different types of fault scenarios are successfully diagnosed with the proposed inference methodology.

• Journal of Physiology & Pathology in Korean Medicine
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• v.28 no.6
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• pp.662-667
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• 2014

The purpose of this clinical study is to develop structured clinical trial protocol and guideline for improvement of safety, useful and effective of pulse diagnosis devices. As a first step, papers on pulse diagnosis and pulse diagnosis devices from 2001 and 2013 were systematically reviewed. In the next step, we have collected the opinions from the specialists, companies, and statistician in pulse diagnosis to evaluate the current condition, the state and problem of domestic clinical trial cases of pulse diagnosis device. And we have to created protocol and case report form (CRF) in regards to site condition and characteristics of pulse diagnosis devices, and showed the guideline of eligibility criteria, operation process, investigation items, evaluation items and so on. This clinical protocol will become a basic information for a researcher in designing or performing a clinical study of pulse diagnosis devices, and be used as a useful material during acquisition of good clinical data. Furthermore, we hope to enhance the invigoration of pulse diagnosis clinical trials and the performance improvement of pulse diagnosis devices.

• Clinical and Experimental Pediatrics
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• v.58 no.1
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• pp.28-32
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• 2015

Purpose: Diencephalic syndrome is an uncommon cause of failure to thrive in early childhood that is associated with central nervous system neoplasms in the hypothalamic-optic chiasmatic region. It is characterized by complex signs and symptoms related to hypothalamic dysfunction; such nonspecific clinical features may delay diagnosis of the brain tumor. In this study, we analyzed a series of cases in order to define characteristic features of diencephalic syndrome. Methods: We performed a retrospective study of 8 patients with diencephalic syndrome (age, 5-38 months). All cases had presented to Seoul National University Children's Hospital between 1995 and 2013, with the chief complaint of poor weight gain. Results: Diencephalic syndrome with central nervous system (CNS) neoplasm was identified in 8 patients. The mean age at which symptoms were noted was $18{\pm}10.5$ months, and diagnosis after symptom onset was made at the mean age of $11{\pm}9.7$ months. The mean z score was $-3.15{\pm}1.14$ for weight, $-0.12{\pm}1.05$ for height, $1.01{\pm}1.58$ for head circumference, and $-1.76{\pm}1.97$ for weight-for-height. Clinical features included failure to thrive (n=8), hydrocephalus (n=5), recurrent vomiting (n=5), strabismus (n=2), developmental delay (n=2), hyperactivity (n=1), nystagmus (n=1), and diarrhea (n=1). On follow-up evaluation, 3 patients showed improvement and remained in stable remission, 2 patients were still receiving chemotherapy, and 3 patients were discharged for palliative care. Conclusion: Diencephalic syndrome is a rare cause of failure to thrive, and diagnosis is frequently delayed. Thus, it is important to consider the possibility of a CNS neoplasm as a cause of failure to thrive and to ensure early diagnosis.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.959-964
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• 1993

We developed fault diagnosis fuzzy expert system for ACS(Advanced Control System). ACS is a DCS(Distributed Control System) with advanced control algorithm fault tolerance capabilities, fault diagnosis functions, and so on. Fuzzy expert system developed for an ACS in this paper gives an operator alarm signal depending on the state of process value and manipulated value, and the cause of alarm in real time. Simple experiment result with several rules for the-fault-diagnosis of drum level loop in Seoul-Power-Plant.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.643-646
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• 2000

As many industrial systems become more complex, it becomes extremely difficult to diagnose the cause of failures. This paper presents a failure diagnosis approach based on discrete event system theory. In particular, the approach is a hybrid of event-based and state-based ones leading to a simpler failure diagnoser with supervisory control capability. The design procedure is presented along with a pump-valve system as an example.

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