• Journal of the Korean Society of Safety
• /
• v.19 no.1
• /
• pp.23-30
• /
• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• Proceedings of the KIEE Conference
• /
• 2003.11c
• /
• pp.645-648
• /
• 2003

Induction motors are a critical component of industrial processes. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyses the motor's supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2005.11a
• /
• pp.279-284
• /
• 2005

Knowledge based fault diagnosis has a limitation in determining the cause and scheme for the fault, because it detects faults from signal pattern only Therefore, model-based fault diagnosis is requested to determine the fault by analyzing output of the equipment from its dynamic model. This research shows a method how to devise the automaton of system as a model for normal and faulty condition through the reduction of handling data by quantization of vibration signals and the example which is concerning to the bearing of ATM. The developed model based fault diagnosis was applied to detect the faulty bearing of ATM, which results.

• Journal of Korea Multimedia Society
• /
• v.15 no.9
• /
• pp.1149-1155
• /
• 2012

In this paper, we propose cause diagnosis method of semiconductor defects from semiconductor industrial images. Our method constructs feature database (DB) of defect images. Then, defect and input images are subdivided by uniform block. And the block similarity is measured using histogram kai-square distance after color histogram calculation. Then, searched blocks in each image are merged into connected objects using clustering. Finally, the most similar defect image from feature DB is searched with the defect cause by measuring cluster similarity based on features of each cluster. Our method was validated by calculating the search accuracy of n output images having high similarity. With n = 1, 2, 3, the search accuracy was measured to be 100% regardless of defect categories. Our method could be used for the industrial applications.

• Nuclear Engineering and Technology
• /
• v.55 no.3
• /
• pp.814-826
• /
• 2023

Sensor faults in nuclear power plant instrumentation have the potential to spread negative effects from wrong signals that can cause an accident misdiagnosis by plant operators. To detect sensor faults and make accurate accident diagnoses, prior studies have developed a supervised learning-based sensor fault detection model and an accident diagnosis model with faulty sensor isolation. Even though the developed neural network models demonstrated satisfactory performance, their diagnosis performance should be reevaluated considering real-time connection. When operating in real-time, the diagnosis model is expected to indiscriminately accept fault data before receiving delayed fault information transferred from the previous fault detection model. The uncertainty of neural networks can also have a significant impact following the sensor fault features. In the present work, a pilot study was conducted to connect two models and observe actual outcomes from a real-time application with an integrated system. While the initial results showed an overall successful diagnosis, some issues were observed. To recover the diagnosis performance degradations, additive logics were applied to minimize the diagnosis failures that were not observed in the previous validations of the separate models. The results of a case study were then analyzed in terms of the real-time diagnosis outputs that plant operators would actually face in an emergency situation.

• Journal of Korean Neurosurgical Society
• /
• v.64 no.3
• /
• pp.460-468
• /
• 2021

Objective : Extremely low alanine transaminase (ALT) levels are associated with all-cause mortality in frail elderly individuals; the clinical significance of ALT as a reliable biomarker is now being considered. Predicting mortality with routine tests at the time of diagnosis is important for managing patients after intracranial hemorrhage. We aimed to investigate whether an extremely low ALT level is associated with mortality in the elderly after intracranial hemorrhage. Methods : A retrospective review was performed on 455 patients with intracranial hemorrhage admitted to a university-affiliated tertiary care hospital from February 2014 to May 2019. Multivariate Cox regression analysis was performed for all ages and for each age group to determine whether an extremely low ALT level is an independent predictor of mortality only in the elderly. Results : Overall, 294 patients were enrolled, and the mean age of the subjects was 59.1 years, with 99 (33.8%) aged ≥65 years. The variables associated with all-cause mortality in all subjects were age, C-reactive protein (CRP) levels, hemoglobin (Hb) levels (<11 g/dL), and initial Glasgow coma scale (GCS) scores. In young patients, CRP, low Hb levels, and initial GCS scores were significantly associated with all-cause mortality. However, in the elderly (≥65 years), the variables significantly associated with all-cause mortality were extremely low levels of ALT (<10 U/L) (adjusted hazard ratio, 3.313; 95% confidence interval, 1.232-8.909; p=0.018) and initial GCS scores. Conclusion : Extremely low ALT level (<10 U/L) at the time of diagnosis is a significant risk factor for all-cause mortality in the elderly after intracranial hemorrhage.

• Korean Journal of Oriental Medicine
• /
• v.1 no.1
• /
• pp.101-125
• /
• 1995

The terminology used for oriental medicine has not yet been standardized so far and this might cause the problems in developing theories and clinical research of oriental medicine. To establish scientific backgroupd of oriental medicine, it is required that all the terminology used for oriental medicine should be standardized and unified. For more efficient oriental medical practice, the standardization, unification of the terms and conditions used for diagnosis in oriental medicine should be achieved. The aim of this study are as follows; 1. To provide clear and logical systems for the diagnosis of symptoms and diseases. 2. To provide the theoritical clearmess of oriental medicine and to promote the public facilities for study. 3. To provide ways of idea exchange and understanding between oriental medicine and various biological sciences. 4. To provide practical basis for hospital administration for oriental medicine.

• Tuberculosis and Respiratory Diseases
• /
• v.76 no.5
• /
• pp.211-217
• /
• 2014

Malignant pleural effusions (MPEs) are the second leading cause of exudative pleural effusions after parapneumonic effusions. In the vast majority of cases, a MPE signifies incurable disease associated with high morbidity and mortality. Considerable advances have been made for the diagnosis of MPEs, through the development of improved methods in the specialized cytological and imaging studies. The cytological or histological confirmation of malignant cells is currently important in establishing a diagnosis. Furthermore, despite major advancements in cancer treatment for the past two decades, management of MPE remains palliative. This article presents a comprehensive review of the medical approaches for diagnosis and management of MPE.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2003.06a
• /
• pp.963-968
• /
• 2003

The continuous process systems usually consists of various components: driven rollers. idle rolls, load-cell and so on. Even a simple fault in a single component in the line may cause a catastrophic damage on the final products. Therefore it is absolutely necessary to diagnosis the components of the continuous systems. In this paper, an adaptive eccentricity compensation method is presented. And a new diagnosis method for transverse roll shape defects on rolling process is developed. The new method was induced from analyzing the rolling mechanism by using rolling force model, tension model, Hitchcock's equation, and measured delivery thickness of materials etc. Computer simulation results also show that the proposed diagnosis methods is very effective in the diagnosis of 3-D roll shape

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.10a
• /
• pp.442-445
• /
• 1997

A challenging activity in the manufacturing industry is to perform in real time the continuous monitoring of the process state, the situation assessment and identification of the problem on line and diagnosis of the cause and importance of the problem if he process does not work properly. This paper describes LAT(Ladder Analysis Tool) system for fault tree generation to improving the fault diagnosis of CNC machine tools. The system consists of 4 steps which can automatically ladder analysis from ladder diagram to two diagnosis function models. The two diagnostic models based on he ladder diagram is switching function model and step switching function model. This system tries to overcome diagnosis deficiencies present machine tool.