• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.2
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• pp.210-217
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• 1990

For the failure detection of dynamic systems, processing the residuals from the observer of the estimator is the most general method. A failure detection method which use an adaptive predictor to separate the effect of sensor failure from the additive noise in the residuals of a Kalman filter that is employed as an estimator of a dynamic system is addressed here. In the method, the property of the residuals of an optimal Kalman estimator is exploited. The simulation results of this method shows that the proposed method is superior to the sequential probability ratio test for a small failure magnitude.

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.3
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• pp.223-236
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• 1999

The need to monitor and undertake remidial works on large structures has greatly increased in recent years due to the appearance of widespread faults in large structures such as bridges and buildings, etc, of 20 or more years of age. The health condition of structures must be monitored continuously to maintenance the structures. In order to do in-situ monitoring, the sensor is necessary to be embedded in the structures. Fiber optic sensors can be embedded in the structures to get the health information in the structures. The fiber sensor was constructed with $3{\times}3$ fiber couplers to sense the multi-point strains and failure instants. The 4 RC (reinforced concrete) beams were made to 2 of A type, 2 of B type beams. These beams were reinforced by the reinforcing bars, and were tested under the flexural loading. The behavior of the beams was simultaneously measured by the fiber optic sensors, electrical strain gages, and LVDT. The states of the beams were interpreted by these all signals. By these experiments, There were verified that the fiber optic sensors could measure the structural strains and failure instants of the RC beams, The fiber sensors were well operated until the failure of the beams. It was shown that the strains of the reinforcing steel bar can be used to monitor the health condition of the beams through the flexural test of RC beams. On the other words, the results were arrived that the two strains in the reinforcing bar measured at the same point can give the information of the structural health status. Also, the failure instants of beams were well detected from the fiber optic filtered signals.

• ETRI Journal
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• v.30 no.1
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• pp.13-20
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• 2008

We evaluate the sink capacity of wireless code division multiple access (CDMA) sensor networks with layered architecture. We introduce a model of interference at a sink considering two kinds of interference: multiple access interference (MAI) and node interference (NI). We also investigate the activity of sensor nodes around the sink in relation to gathering data under a layered architecture. Based on the interference model and the activity of sensor nodes around the sink, we derive the failure probability of the transmission from a source node located one hop away from the sink using Gaussian approximation. Under the requirement of 1% failure probability of transmission, we determine the sink capacity, which is defined as the maximum number of concurrent sensor nodes located one hop away from the sink. We demonstrate that as the node activity of the MAI decreases, the variation of the sink capacity due to the node activity of the NI becomes more significant. The analysis results are verified through computer simulations.

• Journal of Advanced Navigation Technology
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• v.7 no.2
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• pp.171-179
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• 2003

In this paper a neural network-based fault tolerant control system for aircraft sensor and actuator failures is considered. By exploiting flight dynamic relations a set of neural networks is constructed to detect sensor failure and give alternative signal for the faulty sensor. For actuator failures another set of neural networks is designed to perform fault detection, identification, and accomodation which returns the aircraft to a new stable trim. Integrated system is simulated to show the performance of the system with sensor and control surface failures.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.47-53
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• 2020

There is a need for a system that provides early warning of presence and type of failure of automobile wheel bearings through the application of predictive fault analysis technologies. In this paper, we presented a sensor module mounted on a wheel bearing and a diagnostic system that collects, stores and analyzes vehicle acceleration information and vibration information from the sensor module. The developed sensor module and predictive analysis system was tested and evaluated thorough excitation test equipment and real automotive vehicle to prove the effectiveness.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.6
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• pp.1059-1068
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• 2016

The current operation method for the AVC (Automatic Vehicle Classification) equipment does not generate vehicle speed, traffic volume and vehicle type information when part of the sensors has failed. Inefficiency of current methods would not use the collected data from the normal sensor. In this study was conducted research on the calculating method at the traffic volume and vehicle speed in the sensor failure AVC equipment. The failure situation of the sensor was classified into 4 types. Calculating the traffic volume and vehicle speed information for each type, and accuracy of these informations were analyzed. Analysis results, traffic volume was possible to calculate a highly accurate value (accuracy: 100%, 98%, 97%). In the case of speed, the accuracy of the calculated speed value reaches a level that can be accepted sufficiently (RMSE value is less than 16.8). So, using the methodology proposed in this study are expected to be able to increase the operational efficiency of the AVC equipment.

• Journal of the Korean Institute of Gas
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• v.14 no.4
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• pp.56-62
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• 2010

The purpose of this paper analyzes and investigates the failure case studies of electronic control sensors for a LP gas engine. The malfunction of crank angle sensor, which controls a fuel injection volume of LP gas, displays an irregular and non-uniform pulse wave form. The pulse form, which is related to the noise of the crank angle sensor, displays at the rectangular peak with a saw-toothed shape and is intermittently generated with a level of 2.46V noise signal. The malfunction of No. 1 TDC sensor in which is caused from the internal disorder affects to the reduction of engine power and engine stop suddenly. If the malfunction of oxygen sensor is occurred due to a wiring problem of a sensor connector, the LP gas vehicle may produce a shaking and disharmony of an engine because of no signal supply from the oxygen sensor. The air cleaner replica produces the clogging of continuous supply of fresh air. This may cause the retardation of vehicle acceleration and engine disharmony intermittently.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.84-94
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• 2018

In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies' attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

• Journal of the Korean Society for Railway
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• v.12 no.1
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• pp.104-109
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• 2009

In order to apply FBG(Fiber Bragg Grating) sensor as one of reliable sensors in the commercial railway structure, the reliability of FBG sensor in the mechanical strength viewpoint have to be confirmed and the maximum strain should surpass the fracture strain of the host structure to measure the measurands until the host structures fail. In this paper, several factors that influence the mechanical failure strength of fiber Bragg grating sensors were analyzed. A set-up for dynamic tensile testing of optical glass fibers with fiber Bragg gratings was made. To increase the FBG failure strength, techniques relying on the H2 loading treatment and stripping methods were established and testified as a result of the tensile strength test of optical fibers.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.5
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• pp.151-156
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• 2013

The experiment was done on cars travelling at the speeds of 20km/h, 60km/h and 100km/h using the performance testing mode for chassis dynamometer. In this experiment, the relativity between the secondary waveform coming from ignition coil and exhaust emissions were measured in case of cars with failures, in oxygen sensor, spark plugs. The following results obtained by analysis of the relativity between the secondary waveform and exhaust emissions. 1) When the oxygen sensor is failure, the average value of CO emission measured was 6.8 times higher than the standard CO emission value and the average value of HC emission measured was 2.3 times higher than the standard emission level. 2) When engine parts are in failure, more fuel enters the cylinder due to longer opening duration of injector, and it tended to make CO and HC emission values increase. 3) Combustion duration, the shape of flame propagation during spark line, and the size of the discharge-induced energy were the three main elements that directly cause variations in CO and HC emission values.