• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.6
• /
• pp.503-510
• /
• 2002

This paper presents a fault diagnosis method using neural network-based multi-fault models and statistical method to detect and isolate faults in nonlinear systems. In the proposed method, faults are detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.11
• /
• pp.2708-2716
• /
• 2013

This paper presents implementation of embedded remote monitoring diagnosis system which has effective wireless channel structure and communication protocols with user-friendly UI for intelligent digital vessel. Developed system contains integrated vessel monitoring system, server, exclusive mobile terminal and smart phone. We designed an effective dual structure communication channel and simple but effective communication protocol on the monitoring system. Failures of the wireless communication are minimized and the wrong wireless communication channel is immediately replaced. In addition, we developed an effective embedded Linux UI for LCD. The implemented wireless monitoring system was tested and verified on digital vessel.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.4
• /
• pp.468-475
• /
• 2012

Nurses have performed their nursing practice according to the standard guidelines such as NANDA, NIC, and NOC, and recorded the information on nursing process into EMR system. In particular, NANDA, nursing diagnosis taxonomy, has difficulty expressing nursing diagnosis in detail because it represents abstract concepts of nursing diagnosis. So, the hospitals in KOREA have developed and used the list of nursing diagnosis on their own without referring the international standard terminologies, and it caused the delay of computerization of nursing records. Therefore, we proposed a ontology development methodology on nursing diagnosis based on NANDA and SNOMED-CT. The developed ontology, systematically developed with the frequently used nursing diagnosis terminologies in each hospital, based on the proposed methodology enables knowledge expansion and interoperable exchange of nursing records between EMR systems. We developed an ontology using the 112 nursing diagnosis terms defined by extracting and refining information on nursing diagnosis recorded in Kyungpook National University Hospital. We also confirmed the content validity and the usefulness of the developed ontology through expert assessment and experiment.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.4
• /
• pp.168-178
• /
• 2002

The CNC machine toots has two kinds of fault. One is the fault due to degraded parts and the other is the fault due to operation disability. The phenomena of degradation is predictable but the operational fault is unpredictable because it occurred without any warning. The major faults of CNC machine tool are operational faults which are charged over 70%. This paper describes the model of remote service and the intelligent fault diagnosis system to diagnosis operational faults of CNC machine tools. To generalize fault diagnosis, two diagnosis models such as SF(Switching Function) and SSF(Step Switching Function) are proposed. The SF is static model and SSF is dynamic model for expression of fault. The SF and SSF model can be generated using SFG(Switching Function Generator) which is developed in this research. The three major operational faults such as emergency stop error, cycle start disability and machine ready disability are applied to experiment of fault modeling. To remote service of faults fur CNC machine tool, the web server and client system based internet are proposed as the suitable environment. The developed two technologies are implemented with the internal function of open architecture controller. The implemental results for two technologies are presented to validate the proposed scheme.

• Journal of IKEEE
• /
• v.27 no.4
• /
• pp.601-609
• /
• 2023

In this paper, we propose an artificial intelligence-based high-speed fault diagnosis method using an FPGA in the event of a ground fault in a DC system. When applying artificial intelligence algorithms to fault diagnosis, a substantial amount of computation and real-time data processing are required. By employing an FPGA with AI-based high-speed fault diagnosis, the DC breaker can operate more rapidly, thereby reducing the breaking capacity of the DC breaker. therefore, in this paper, an intelligent high-speed diagnosis algorithm was implemented by collecting fault data through fault simulation of a DC system using Matlab/Simulink. Subsequently, the proposed intelligent high-speed fault diagnosis algorithm was applied to the FPGA, and performance verification was conducted.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.37-41
• /
• 2002

This study has been focused on the development of an open architecture CNC system and integration of core application technology for machine tool such as a remote monitoring/diagnosis system, NURBS interpolation, and cutting process simulation. To do this, we have developed a comprehensive CNC software including the basic HMI, screen editor, ASF, and visual builder. As a control hardware system for machine tool, the universal I/O module and CNC main unit have been developed. Then the remote monitoring/diagnosis system and NURBS interpolation have been implemented in the CNC software. The cutting simulation software will be used for enhancing the productivity of machine tools. Through a simulator and test bed, the whole technology has been verified.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.61 no.4
• /
• pp.228-233
• /
• 2012

In power system, substation facilities have become too complex and larger according to an extended power system. Also, customers require the high quality of electrical power system. However, some facilities become old and often break down unexpectedly. The unexpected failure may cause a break in power system and loss of profits. Therefore it is important to prevent abrupt faults by monitoring the condition of power systems. Among the various power facilities, power transformers play an important role in the transmission and distribution systems. In this research, we develop intelligent diagnosis technique for monitoring of power transformer by FCM(Fuzzy c-means) and ELM(Extreme Learning Machine). The proposed technique make it possible to diagnosis the faults occurred in transformer. To demonstrate the validity of proposed method, various experiments are performed and their results are presented.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.7
• /
• pp.830-837
• /
• 2004

Neural networks-based fault diagnosis algorithm to detect and isolate faults in the nonlinear systems is proposed. In the proposed method, the fault is detected when the errors between the system output and the multilayer neural network-based nominal model output cross a Predetermined threshold. Once a fault in the system is detected, the system outputs are transferred to the fault classifier by nultilayer/ART2 NN (adaptive resonance theory 2 neural network) for fault isolation. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.6
• /
• pp.673-681
• /
• 2005

As it is the most important to make sure security and reliability for nuclear Power Plant, it's considered the most crucial issues to develop a fault detective and diagnostic system in spite of multiple hardware redundancy in itself. To develop an algorithm for a fault diagnosis in the nuclear PWR steam generator, this paper proposes a method based on ART2(adaptive resonance theory 2) neural network that senses and classifies troubles occurred in the system. The fault diagnosis system consists of fault detective part to sense occurred troubles, parameter estimation part to identify changed system parameters and fault classification part to understand types of troubles occurred. The fault classification part Is composed of a fault classifier that uses ART2 neural network. The Performance of the proposed fault diagnosis a18orithm was corroborated by applying in the steam generator.

• Proceedings of the KSME Conference
• /
• 2005.11a
• /
• pp.205.2-205.2
• /
• 2005