• Journal of Korean Tunnelling and Underground Space Association
• /
• v.25 no.6
• /
• pp.509-523
• /
• 2023

The high-level nuclear waste (HLW) repository is exposed to complex environmental conditions consisting of high temperature, high humidity, and radiation, resulting in structural deterioration. Therefore, structural health monitoring is essential, and piezo sensors are used to detect cracks and estimate strength. However, since the monitoring sensors installed in the disposal tunnel and disposal container cannot be replaced or removed, the quantitative life of the monitoring sensor and its suitability must be assessed. In this study, the life of a piezo sensor for monitoring was assessed using an accelerated life test (ALT). The failure mode and mechanism of the piezo sensor under high temperature conditions were determined, and temperature stress's influence on the piezo sensor's life was analyzed. ALT was conducted on temperature stress and the relationship between temperature stress and piezo sensor life was suggested. The life of the piezo sensor was assessed using the Weibull probability distribution and the Arrhenius acceleration model. The suggested relationship can be used in multiple stress ALT designs for more precise life assessment.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.59 no.5
• /
• pp.101-108
• /
• 2017

Recently, the occurrence of landslides has been increasing over the years due to the extreme weather event. Developments of landslides monitoring technology that reduce damage caused by landslide are urgently needed. Therefore, in this study, a strain ratio sensor was developed to predict the ground behavior during the slope failure, and the change in surface ground displacement was observed as slope failed on the field model experiment. As a result, in the slope failure, the ground displacement process increases the risk of collapse as the inverse displacement approaches zero. It is closely related to the prediction of precursor. In all cases, increase in displacement and reverse speed of inverse displacement with time was observed during the slope failure, and it is very important event for monitoring collapse phenomenon of risky slopes. In the future, it can be used as disaster prevention technology to contribute in reduction of landslide damage and activation of measurement industry.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.18 no.9
• /
• pp.52-57
• /
• 2019

The key technology of the fourth industrial revolution is artificial intelligence and machine learning. In this study, FMEA was performed on fuel pumps used as key items in most systems to identify major failure components, and artificial neural networks were built using big data. The main failure mode of the fuel pump identified by the test was coil damage due to overheating. Based on the artificial neural network built, machine learning was conducted to predict the failure and the mean error rate was 4.9% when the number of hidden nodes in the artificial neural network was three and the temperature increased to $140^{\circ}C$ rapidly.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.9
• /
• pp.28-34
• /
• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

• Journal of Sensor Science and Technology
• /
• v.19 no.2
• /
• pp.124-129
• /
• 2010

For the industrial printing applications, the stability of the piezo-driven inkjet printhead is a major requirement. In this paper, we focused on the failure modes of the inkjet printhead and realized a method to detect and repair them at high speed. The printhead monitoring is performed by detecting the residual vibration of the actuating plate using the self- sensing capability of the piezoelectric material. To measure the channel acoustics and to identify the malfunctioning nozzle, we devised the bridge sensing circuitry and failure detection algorithm. The residual vibration signals can be affected by the boundary conditions of the channel acoustics, so it is possible to identify the failure causes by analyzing the monitoring signals. Therefore it is also possible to apply a proper restoring process to the defective printhead. The experimental results show that this method is effective in improving the reliability of the industrial printing.

• 한국신재생에너지학회:학술대회논문집
• /
• 2008.10a
• /
• pp.343-346
• /
• 2008

To improve the reliability and extend the life for a wind-turbine gearbox, the gearbox needs to be monitored and analysed exactly. This study was conducted to analyze and detect the gearbox conditions when lubricating oil contaminated by wear particles was used. Characteristics of the gearbox failure by wear particles were monitored simultaneously by the on-line measurement sensor of vibration, oil condition and temperature. For the detail vibration analyses, frequency analysis(FFT) was performed. The results of the study were summarized as follows: Vibrational signal was found sensitive to abnormal changes of the gearbox conditions when lubricant was contaminated by wear particles. Also, using frequency analysis for the harmonics of gear mesh frequency(GMF), it is found that the failure of gearbox was caused by the damages of meshing gears. However, temperature and oil condition measuring signals were found not so effective to detect any gearbox failure by oil contamination.

• Proceedings of the SAREK Conference
• /
• 2008.06a
• /
• pp.36-41
• /
• 2008

Fault detection and diagnosis(FDD) system is beneficial in equipment management by providing the operator with tools which can help find out a failure of the system. An experimental study has been performed on fault detection and diagnosis method for a water chiller. Bayes classifier, which is one of classical pattern classifiers, is adopted in deciding whether fault occurred or not. FDD algorithm can detect refrigerant leak failure, when 20% amount of charged refrigerant for normal operation leaks from the water chiller. The refrigerant leak failure caused COP reduction by 6.7% compared with normal operation performance. When two kinds of faults, such as a decrease in the mass flow rate of cooling water and temperature sensor fault of cooling water inlet, are detected, COP is a little decreased by these faults.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.11
• /
• pp.218-226
• /
• 1998

In this study a neural network based expert system designed to diagnose operating status of a rotating spindle system supported by ball bearings was introduced. In order to facilitate practical failure situations, five exemplary abnormal status was fabricated. Out of several possible data source locations, seven most effective spots were chosen and proven to be the most successful in predicting single and multiple abnormalities. Increased signal strength was measured around where abnormality was embedded. Signal mea-surement locations producing high prediction rate were also classified. Even though multiple abnormalities were hard to be decoupled into their individual causes, proposed diagnostic system was somewhat effective in predicting such cases under certain combination of sensor locations. Among several abnormal operating conditions, highest prediction rate can be expected when signal is spoiled by the failure or damage in outer race. Proposed diagnostic system was again proven to be the most effective system in analyzing and ranking the importance of data sources.

• Proceedings of the KSR Conference
• /
• 2008.11b
• /
• pp.789-795
• /
• 2008

Slope stability is affected by various factors. For safety management of slopes, monitoring systems have been widely constructed along railway lines. The representative data from the systems are variations of ground profile such like ground water level and pore water pressure etc. and direct displacement measured by ground clinometer and tension wire sensor. Slopes are mainly effected by rainfall and rainfall causes the decrease of factor of safety(FOS). Because FOS varies linearly by the variation of ground water level and pore pressure, it has a weak point that could not define the time and proper warning sign to secure the safety of the train. In this study, alternative of FOS such as reliability index and probability of failure is applied to slope stability analysis introducing the reliability concept. FOS, reliability index, probability of failure and velocity of probability of failure of the slopes by variation of ground water level are investigated for setting up the specification of safety management of slopes. By executing case study of a slope(ILLO-IMSUNGLI), it is showed to be applied to specification of safety management.

• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 2004.11a
• /
• pp.320-320
• /
• 2004