• Journal of Applied Reliability
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• v.16 no.3
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• pp.180-191
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• 2016

Purpose: The purpose of this study was to develop the accelerated life test method for Constant Electrical Potential Electrolysis gas sensor (CEPE gas sensor). Methods: The parts and modules of CEPE gas sensor were analyzed by using Reliability Block Diagram (RBD). Failure Mode and Effect Analysis (FMEA) and Quality Function Deployment (QFD) methods were performed for each part to determine the most affecting stress factor in its life cycle. The long term testing was conducted at three different dry heat levels and the acceleration factor was developed by using Arrhenius relationship. Conclusion: The acceleration factor for CEPE gas sensor was developed by using FMEA, QFD, and statistical analysis for its failure data. Also qualification tests were designed to meet the target life.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.530-531
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• 2021

In this paper, I proposed a technique for identifying discrepancies between data input in the IoT sensor environment. The proposed technique can manage numerically input sensor data so that it can be applied to actual field problems. The proposed technique can detect when contradictory data is input from two or more sensors in an actual IoT sensor environment, and through this, it can be developed into a method that can identify and resolve sensor failure or intentional data disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.437-440
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• 1989

In the paper a failure detection and diagnosis method of a stochastic system is proposed. It is based on the comparison of the moving averages generated from outputs of the real plant and a modeled normal plant. The proposed method allows us to locate the failed sensor and can be efficiently used for the failure detection and diagnosis of a plant with many sensors.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.357-360
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• 2007

An asset tracking using wireless sensor network is concerned with geographical locations of sensor nodes. The limited size of sensor nodes makes them attractable for tracking service, at the same time their size causes power restrictions, limited computation power, and storage restrictions. Due to such constrained capabilities, the wireless sensor network basically assumes the failure of sensor nodes. This causes a set of concerns in designing asset tracking system on wireless sensor network and one of the most critical factors is location uncertainty of sensor nodes. In this paper, we classify the location uncertainty problem in asset tracking system into following cases. First, sensor node isn't read at all because of sensor node failure, leading to misunderstanding that asset is not present. Second, incorrect location is read due to interference of RSSI, providing unreliable location of asset. We implemented and installed our asset tracking system in a real environment and continuously monitored the status of asset and measured error rate of location of sensor nodes. We present experimental results that demonstrate the location uncertainty problem in asset tracking system using wireless sensor network.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.360-374
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• 2016

Since WSNs (Wireless Sensor Networks) applied to their application areas such as smart home, smart factory, environment monitoring, etc., depend on sensor data, the sensor data is the most important among WSN components. The resources of each node consisting of WSN are extremely limited in energy, hardware and so on. Due to these limitation, communication failure probabilities become much higher and the communication failure causes data loss to occur. For this reason, this paper proposes 2MC (Maximum/Minimum Compression) that is a method to compress sensor data by selecting circular queue-based maximum/minimum sensor data values. Our proposed method reduces sensor data losses and value errors when they are recovered. Experimental results of 2MC method show the maximum/minimum 35% reduction efficiency in average sensor data accumulation error rate after the 3 times compression, comparing with CQP (Circular Queue Compression based on Period) after the compressed data recovering.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.9
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• pp.1297-1305
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• 2004

Reliability of MEMS devices is receiving more attention as they are heading towards commercial production. In particular are the reliability and long-term stability of wafer level vacuum packaged MEMS gyroscope sensors subjected to cyclic mechanical stresses at high frequencies. In this study, we carried out several reliability tests such as environmental storage, fatigue, shock, and vibration, and we investigated the failure mechanisms of the anodically bonded vacuum gyroscope sensors. It was found that successful vacuum packaging could be achieved through reducing outgassing inside the cavity by deposition of titanium as well as by pre-taking process. The current gyroscope structure is found to be safe from fatigue failure for 1000 hours of operation test. The gyroscope sensor survives the drop and vibration tests without any damage, indicating robustness of the sensor. The reliability test results presented in this study demonstrate that MEMS gyroscope sensor is very close to commercialization.

• Journal of Information Processing Systems
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• v.6 no.3
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• pp.413-434
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• 2010

Wireless sensor networks are usually characterized by dense deployment of energy constrained nodes. Due to the usage of a large number of sensor nodes in uncontrolled hostile or harsh environments, node failure is a common event in these systems. Another common reason for node failure is the exhaustion of their energy resources and node inactivation. Such failures can have adverse effects on the quality of the real-time services in Wireless Sensor Networks (WSNs). To avoid such degradations, it is necessary that the failures be recovered in a proper manner to sustain network operation. In this paper we present a dynamic Energy efficient Real-Time Job Allocation (ERTJA) algorithm for handling node failures in a cluster of sensor nodes with the consideration of communication energy and time overheads besides the nodes' characteristics. ERTJA relies on the computation power of cluster members for handling a node failure. It also tries to minimize the energy consumption of the cluster by minimum activation of the sleeping nodes. The resulting system can then guarantee the Quality of Service (QoS) of the cluster application. Further, when the number of sleeping nodes is limited, the proposed algorithm uses the idle times of the active nodes to engage a graceful QoS degradation in the cluster. Simulation results show significant performance improvements of ERTJA in terms of the energy conservation and the probability of meeting deadlines compared with the other studied algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.8
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• pp.721-724
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• 2016

Because of the inherent uncertainties caused by the manufacturing process variations, future loading conditions, and incomplete damage models, the lifetimes of mechanical structures under field conditions are significantly different from the results obtained in the laboratories. In this study, a dual sensor was developed to prognosticate the fatigue failure of structures under these uncertain conditions, and its effectiveness was demonstrated on a rectangular columnar structure under repeated uni-axial loading. The dual sensor is a slightly weaker structure embedded in the target structure, so that failure occurs in the sensor earlier than in the target structure. From the signal differences in the strain gauges in the embedded dual sensor, it is possible to differentiate between the normal status and warning status, even under variable loads.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.244-250
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• 1997

The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent Property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability's or components are combined, which results in the dynamic MTTF or system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.05a
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• pp.31-34
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• 2002

A study on the method that can measure the internal strain of composite materials is performed to monitor the health status of composite structures. A fiber optic sensor was constructed using the total reflected extrinsic Fabry-Perot interferometer(TR-EFPI) probe with a broadband light source. Result obtained from electrical strain gage adhered on the aluminum beam specimen was compared with that from the fiber optic TR-EFPI sensor and showed a good agreement. It was found that fiber optic TR-EFPI sensor system was adequate for monitoring the strain and thus failure processes in the interior of composite materials.