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http://dx.doi.org/10.21289/KSIC.2022.25.2.169

Classification of Operating State of Screw Decanter using Video-Based Optical Flow and LSTM Classifier  

Lee, Sang-Hyeop (Dept. of Electronic Eng., Kyungsung University)
Wesonga, Sheilla (Dept. of Electronic Eng., Kyungsung University)
Park, Jang-Sik (Dept. of Electronic Eng., Kyungsung University)
Publication Information
Journal of the Korean Society of Industry Convergence / v.25, no.2_1, 2022 , pp. 169-176 More about this Journal
Abstract
Prognostics and health management (PHM) is recently converging throughout the industry, one of the trending issue is to detect abnormal conditions at decanter centrifuge during water treatment facilities. Wastewater treatment operation produces corrosive gas which results failures on attached sensors. This scenario causes frequent sensor replacement and requires highly qualified manager's visual inspection while replacing important parts such as bearings and screws. In this paper, we propose anomaly detection by measuring the vibration of the decanter centrifuge based on the video camera images. Measuring the vibration of the screw decanter by applying the optical flow technique, the amount of movement change of the corresponding pixel is measured and fed into the LST M model. As a result, it is possible to detect the normal/warning/dangerous state based on LSTM classification. In the future work, we aim to gather more abnormal data in order to increase the further accuracy so that it can be utilized in the field of industry.
Keywords
Screw Decanter; Predictive Maintenance; Vibration Analysis; Long short term memory (LSTM);
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Times Cited By KSCI : 2  (Citation Analysis)
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