• Journal of Sensor Science and Technology
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• v.22 no.4
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• pp.297-301
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• 2013

We proposed and demonstrated a distributed fiber-optic overheating detection sensor using optical time domain refrectometry. With increased of temperature the optical fiber is bended by a bi-metal and it result in optical leaky loss of the fiber. The sensor structure is designed in such a way that the signal of overheating is happen when the temperature exceeding a threshold temperature and the optical fiber is protected from excess bending.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.243-247
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• 2014

In this paper, we design two types of the sensor module to measure the partial discharge causing failures and electric fires for power incoming/distributer. And evaluate the performance thereof. Analyze the correlation of the phase current and the partial discharge and overheating. Proposes to apply the stability index by utilizing the analyzed information and use in order to prevent the failure or electrical fire of the power incoming/distributer.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.7-15
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• 2022

Since the direct and indirect damage caused by the fire in the underground utility tunnel will cause great damage to society as a whole, it is necessary to make efforts to prevent and control it in advance. The most of the fires that occur in cables are caused by short circuits, earth leakage, ignition due to over-current, overheating of conductor connections, and ignition due to sparks caused by breakdown of insulators. In order to find the cause of fire at an early stage due to the characteristics of the underground utility tunnel and to prevent disasters and safety accidents, we are constantly managing it with a detection system using image analysis and making efforts. Among them, a case of developing a fire detection system using CCTV-based deep learning image analysis technology has been reported. However, CCTV needs to be supplemented because there are blind spots. Therefore, we would like to develop a high-performance acoustic-based deep learning model that can prevent fire by detecting the spark sound before spark occurs. In this study, we propose a method that can collect sound in underground utility tunnel environments using microphone sensor through development and experiment of prototype module. After arranging an acoustic sensor in the underground utility tunnel with a lot of condensation, it verifies whether data can be collected in real time without malfunction.