• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.292-297
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• 2004

The conventional computerized numerical controller (CNC) of machine tools has been increasingly replaced by a PC-based open architecture CNC (OAC) which is independent of the CNC vendor. The OAC and machine tools with OAC led the convenient environment where it is possible to implement user-defined application programs efficiently within CNC. Tis paper proposes a method of operational fault cause diagnosis which is based on the status of programmable logic controller (PLC) in machine tools with OAC. The operational fault is defined as a disability state occurring during normal operation of machine tools. The faults are occupied by over 70% of all faults and are also unpredictable as most of them occur without any warning. Two diagnosis models, the switching function (SF) and the step switching function (SSF), are propose in order to diagnose the fault cause quickly and exactly. The cause of an occurring fault is logically diagnosed through a fault diagnosis system (FDS) using the diagnosis models. A suitable interface environment between CNC and develope application modules is constructed in order to implement the diagnostic functions in the CNC domain. The diagnosed results were displayed on a CNC monitor for machine operators and provided to a remote site through a web browser. The result of his research could be a model of the fault cause diagnosis and the remote monitoring for machine tools with OAC.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1138-1144
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• 2011

This article deals with the problem of fault detection of a spacecraft's actuators. The authors introduce an extended unknown input observer for nonlinear systems. This is an extended form of unknown input observers which are used for linear systems. Since faults are not available, those are considered as unknown inputs. Unknown input observers can estimate states without full information of inputs if some conditions are satisfied. The authors suggest a continuous-time extended UIO (eUIO) and prove the convergence of state estimation errors. Since the dynamic equation of a spacecraft is nonlinear, an extended UIO can be applied. Three eUIOs are designed to monitor three reaction wheels. The moving averages of each eUIO's residuals are selected for decision logic. The proposed method is verified by numerical simulations.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.6
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• pp.558-563
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• 2010

In this paper, the novel approach for the fault diagnosis of the bearing equipped with rotational mechanical facilities was studied. As research works, by applying the ball bearing used extensively in many industrial fields, experiments were conducted in order to propose the new prognostic method about the condition monitoring for the rotational bodies based on the condition analysis of infrared thermography. Also, by using the vibration spectrum analysis, the real time monitoring was performed. As results, it was confirmed that infrared thermography method could be adapted into monitor and diagnose the fault for bearing by evaluating quantitatively and qualitatively the temperature characteristics according to the condition of the ball bearing.

• Nuclear Engineering and Technology
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• v.36 no.3
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• pp.210-218
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• 2004

In the interest of nuclear power plant safety, a self-diagnostic monitoring system (SDMS) is needed to monitor defects in safety-related components. An air-operated valve (AOV) is one of the components to be monitored since the failure of its operation could potentially have catastrophic consequences. In this paper, a model of the AOV is developed with the parameters that affect the operational characteristics. The model is useful for both understanding the operation and correlating parameters and defects. Various defects are introduced in the experiments to construct a fault library, which will be used in a pattern recognition approach. Finally, the validity of the fault library is examined.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.355-357
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• 2017

With the development of automation technology and the increase of large-scale automation projects, sensors used for state monitor and parameter measurement have become more and more important. Once the sensor faults occur, which will lead to the degradation of automation system's performance, and even disastrous consequences. In this paper, sensor output value modeling is performed using Kung's Algorithm for direct fault diagnosis of sensor, and fault diagnosis method based on decision theory is presented.

• Journal of Biosystems Engineering
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• v.28 no.1
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• pp.53-58
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• 2003

This study was carried out to design, construct, and test a greenhouse monitoring system fur the environment and status of control devices in a greenhouse from a remote site using internet. The measuring items selected out of many environmental factors were temperature, humidity, solar radiation, CO$_2$, SOx, NOx concentration, EC, pH of nutrient solution, the state of control devices, and the image of greenhouse. The developed greenhouse monitoring system was composed of the network system and the measuring module. The network system consists of the three kinds of monitors named the Croup Monitor. the Client Monitor and the Server Monitor. The results of the study are summarized as follows. 1. The measuring module named the House Monitor. which is used to watch the state of the control device and the environment of the greenhouse, was developed to a embedded monitoring module using one chip microprocessor 2. For all measuring items. the House Monitor showed a satisfactory accuracy within the range of ${\pm}$0.3%FS. The House Monitors were connected to the Croup Monitor by communication method of RS-485 type and could operate under power and communication fault condition within 10 hours. The Croup Monitor was developed to receive and display measurement data received from the House Monitors and to control the greenhouse environmental devices. 3. The images of the plants inside greenhouse were captured by PC camera and sent to the Group Monitor. The greenhouse manager was able to monitor the growth state of plants inside greenhouse without visiting individual greenhouses. 4. Remote monitoring the greenhouse environment and status of control devices was implemented in a client/server environment. The client monitor of the greenhouse manager at a remote site or other greenhouse manager was able to monitor the greenhouse environment and the state of control devices from the Server Monitor using internet.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.10
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• pp.516-523
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• 2000

In this paper, intelligent methods using digital protective relay in power supervisory control system is developed in order to protect power systems by means of timely fault detection and diagnosis during operation for induction motor which has various load environments and capacities in power systems. The spectrum pattern of input currents was used to monitor to state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrums pattern caused by faults were detected. For diagnosis of the fault detected, the fuzzy fault tree was derived, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, was solved. The solution of the fuzzy relation equation shows the possibility of each fault's occurring. The results obtained are summarized as follows: 1) The test result on the basis of KEMC1120 and IEC60255, show that the operation time error of the digital motor protective relay is improved within ${\pm}5%$. 2) Using clustering algorithm by unsupervisory learning, an on-line fault detection method, not affected by the characteristics of loads and rates, was implemented, and the degree of dependency by experts during fault detection was reduced. 3) With the fuzzy fault tree, fault diagnosis process became systematic and expandable to the whole system, and the diagnosis for sub-systems can be made as an object-oriented module.

• Journal of the Korea Society of Computer and Information
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• v.17 no.9
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• pp.127-138
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• 2012

In this paper, we present a tool (named VMBootFailMonitor) to detect and analyze a failure of a VM boot creation caused by faults on virtual disks of a Xen-based VM. Also, we presents an architecture and detail analysis process of the virtual disk faults in our tool. Especially, VMBootFailMonitor provides a causual analysis result for a case of VM creation failure based on three modules which performs virtual disk analysis, virtualized system analysis and system log analysis. We also support a comparison result between boot times of normal VMs and fault detection times of VM creation based on abnormal virtual disks. At result, our tool detects VM boot failures (3~6 seconds) within normal VM boot times (8~16 seconds).

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.10
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• pp.983-990
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• 2008

This paper presents the fault detection and diagnosis(FDD) algorithm to enhance reliability of a longitudinal controller for an autonomous All-Terrain Vehicle(ATV). The FDD is designed to monitor and identify faults which may occur in distributed hardware used for longitudinal control, e.g., DSPs, CAN, sensors, and actuators. The proposed FDD is an integrated approach of decentralized and centralized FDD. While the former is processed in a DSP and suitable to detect faults in a single hardware, it is sensitive to noise and disturbance. On the other hand, the latter is performed via communication and it detects and diagnoses faults through analyzing concurrent performances of multiple hardware modules, but it is limited to isolate faults specifically in terms of components in the single hardware. To compensate for disadvantages of each FDD approach, two layered structure including both decentralized and centralized FDD is proposed and it allows us to make more robust fault detection and more specific fault isolation. The effectiveness of the proposed method will be validated experimentally.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.706-712
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• 2009

In this paper, a fault diagnosis method is proposed to detect and classifies faults that occur in press process line. An oil level automatic monitoring method is also presented to detect oil level. The FFT(fast fourier transform) frequency analysis and ART2 NN(adaptive resonance theory 2 neural network) with uneven vigilance parameters are used to achieve fault diagnosis in proposing method, and GUI(graphical user interface) program for fault diagnosis and oil level automatic monitoring using LabVIEW is produced and fault diagnosis was done. The experiment results demonstrate the effectiveness of the proposed fault diagnosis method of induction motors and oil level automatic monitor system.