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윈도우 특성 이미지 기반 합성곱 신경망을 활용한 보일러 상태 진단
이현찬(Hyeonchan Lee),김형민(Hyeongmin Kim),나규민(Kyumin Na),윤병동(Byeng D. Youn) 대한기계학회 2020 대한기계학회 춘추학술대회 Vol.2020 No.12
Thermal power plant boiler is one of the main facility that produce steam. Long and thin tubes are inside boiler to generate steam efficiently. Because boiler operate under harsh condition, boiler tube is prone to leakage causing unexpected shutdown of the power plant. To detect leakage, various kind of signals are collected from boiler tube and acoustic signals are the most sensitive signal to tube leakage. However, some event unrelated to leakage cause increase in acoustic signal trend, making it harder to determine the status of boiler. Soot Blowing, cleaning procedure of soot deposited on the internal furnace tubes, is a representative event. In this paper, we propose a novel leakage detection method using Sliding Window Correlation(SWC) Matrix and Sliding Window Energy(SWE) Matrix. Two feature images are trained with Convolutional Neural Network(CNN). Test result from domestic power plant data shows that the proposed method can successfully classify normal, soot blowing and leakage.
음향신호 에너지 감소 메커니즘 기반 확률적 보일러 튜브 누설 위치 추정
나규민(Kyumin Na),김형민(Hyeongmin Kim),이현찬(Hyeonchan Lee),윤병동(Byeng D. Youn) 대한기계학회 2020 대한기계학회 춘추학술대회 Vol.2020 No.12
Estimation of leak location is important considering the labor cost of maintenance procedure and downtime cost in thermal power plant. The well-known approach, TDOA(Time difference of arrival) has lots of problem on practical situation such as limitation of leak signal extraction and numerically not solving issue. To solve these kinds of difficulties, we use probabilistic approach considering energy decaying effect of sound such as attenuation and geometric spreading. In addition, we use bayesian updating method to prevent bias error caused by unpredictable outsource energy variation such as soot-blowing. Finally, We validate our method with simulation data and acoustic emission sensor data of real power plant from installed BTLD(Boiler tube leak detection) system.