• 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.

• 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.

• Tribology and Lubricants
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• v.14 no.3
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• pp.74-80
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• 1998

The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2198-2202
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• 2005

In this paper we have presented a condition monitoring method of check valve using neural network. The acoustic emission sensor was used to acquire the condition signals of check valve in direct vessel injection (DVI) test loop. The acquired sensor signal pass through a signal conditioning which are consisted of steps; rejection of background noise, amplification, analogue to digital conversion, extract of feature points. The extracted feature points which represent the condition of check valve was utilized input values of fault diagnosis algorithms using pre-learned neural network. The fault diagnosis algorithm proceeds fault detection, fault isolation and fault identification within limited ranges. The developed algorithm enables timely diagnosis of failure of check valve’s degradation and service aging so that maintenance and replacement could be preformed prior to loss of the safety function. The overall process has been experimented and the results are given to show its effectiveness.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.6
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• pp.847-859
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• 2011

This paper presents an alarm system for the integrated monitoring and control station of waterworks in Daegu City. An alarm system informs the operator or other responsible individuals about the abnormality in the process so that an appropriate action can be taken. In practice, operators receive far more false and nuisance alarms than valid and useful alarms. Too many false and nuisance alarms can distract the operator from operating the plant, and thus critical alarms may be ignored. This problem can lead to the point that the operator no longer trusts the alarms or even shuts down the whole monitoring system. This paper proposes an efficient method to reduce false and nuisance alarms by prioritizing every fault using the Fault Tree Analysis (FTA) technique. The effectiveness of the proposed method is evaluated with a set of computer simulation under various faulty conditions.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.797-806
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• 2012

Fault detection and isolation (FDI) algorithms provide fault monitoring methods in GPS measurement to isolate abnormal signals from the GPS satellites or the acquired signal in receiver. In order to monitor the occurred faults, FDI generates test statistics and decides the case that is beyond a designed threshold as a fault. For such problem of fault detection and isolation, this paper presents and evaluates position domain integrity monitoring methods by formulating various pseudorange prediction methods and investigating the resulting test statistics. In particular, precise measurements like carrier phase and Doppler rate are employed under the assumption of fault free carrier signal. The presented position domain algorithm contains the following process; first a common pseudorange prediction formula is defined with the proposed variations in pseudorange differential update. Next, a threshold computation is proposed with the test statistics distribution considering the elevation angle. Then, by examining the test statistics, fault detection and isolation is done for each satellite channel. To verify the performance, simulations using the presented fault detection methods are done for an ideal and real fault case, respectively.

• Smart Structures and Systems
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• v.30 no.5
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• pp.433-446
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• 2022

Despite efforts to reduce unexpected accidents at confined construction sites, choking accidents continue to occur. Because of the poorly ventilated atmosphere, particularly in long, confined underground spaces, workers are subject to dangerous working conditions despite the use of artificial ventilation. Moreover, the traditional monitoring methods of using portable gas detectors place safety inspectors in direct contact with hazardous conditions. In this study, a long-range (LoRa)-based wireless safety monitoring system that features the network organization, fault-tolerant, power management, and a graphical user interface (GUI) was developed for underground construction sites. The LoRa wireless data communication system was adopted to detect hazardous gases and oxygen deficiency within a confined underground space with adjustable communication range and low power consumption. Fault tolerance based on the mapping information of the entire wireless sensor network was particularly implemented to ensure the reliable operation of the monitoring system. Moreover, a sleep mode was implemented for the efficient power management. The GUI was also developed to control the entire safety-monitoring system and to manage the measured data. The developed safety-monitoring system was validated in an indoor testing and at two full-scale water pipeline construction sites.

• Journal of Welding and Joining
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• v.28 no.4
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• pp.81-86
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• 2010

In resistance spot welding, regardless of the optimal condition, bad weld quality was still produced due to complicated manufacturing processes such as electrode wear, misalignment between the electrode and workpiece, poor part fit-up, and etc.. Therefore, the goal of this study was to measure the process signal which contains weld quality information, and to develop the process fault monitoring system. Welding force signal obtained through variety experimental conditions was analyzed and divided into three categories: good, shunt, and poor fit-up group. And then a monitoring algorithm made up of an artificial neural network that could estimate the process fault of each different category based on pattern was developed.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3206-3208
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• 1999

This paper describes the remote fault monitoring system for tower parking facilities. This system consists of central station, remote monitoring equipments and communication equipments. The central station is developed under client-server architecture which composed a DB server, a fault detection client, a status collection client and a A/S client. And the remote monitoring systems are connected to central station by LAN using RAS(Remote Access Service) which is constructed PSTN(Public Switched Telephone Network). This system offers real-time fault detection and status data acquisition of tower parking system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.15-19
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• 1994

The application of neural network for fault dection of cutting force in turning was introduced. This monitoring system consist of a RBF predicton model and a ART-1 pattern classifier. RBF prediction model predict a cutting force signal. Prediction error of predictor is used for a input vector of ART-1 pattern classifier. Prediction error could be successfully performed to fault signal monitoring of ART-1 pattern classifier.