• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.1-12
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• 2017

Preventive maintenance is required for best performance of facilities in large scale industry. Ultimately, the efficiency of production is maximized by preventing the failure of facilities in advance. Typically, regular maintenance is conducted manually; however, it is hard to prevent repeated failures. Also, since measures to prevent failure depend on proactive problem-solving by the facility expert, they have limitations when the expert is absent or diagnosis error is made by an unskilled expert. Alarm system is used to aid manual facility diagnosis and early detection. However, it is not efficient in practice, since it is designed to simply collect information and is activated even with small problems. In this study, we designed and developed an automated preventive maintenance system based on expert's experience in detecting failure, determining the cause, and predicting future system failure. We also discussed the system structure designed to reuse the expert's knowledge and its applications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.737-746
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• 2019

Most landslides in the country are shallow failures triggered by intense rainfall. Many researchers have revealed the possibility of predicting shallow failure through the volumetric water content (VWC). This study examined how to determine shallow failure using the gradient characteristics of the volumetric water content. For this, flume experiments were conducted using weathered granite soil. To confirm the saturation state of the surface layer under a rainfall intensity of 30 and 50mm/hr, VWC sensors were installed at depths of 10 and 20 cm on the upper, middle and lower slope. The test results showed that a shallow failure determination using VWC could be applied limitedly according to the slope degree. In addition, the effective cumulative rainfall due to the rainfall infiltration velocity is considered the main factor for the failure time. The failure prediction using the gradient of the VWC depends on the installation location and depth of the sensor. According to the experimental data, the measured value at 20 cm below the slope was most effective. Therefore, an analysis method of VWC and the method of selecting the installation location confirmed through this study can provide important data for presenting the measurement criteria using VWC in the future.