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이준현(Joon-Hyun Lee),김명준(Myeong-Joon Kim),김태훈(Tae-Hoon Kim),이진석,신제창(Che-Chang Shin),이량(Yang Li),조현직(Hyun-Jik Cho),강철구(Chul-Goo Kang) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11
Deep learning algorithms such as LSTM and CNN that can classify timeseries data can be applied to an air compressor to detect anomalies. In the encoder-decoder structure, the encoder compresses the original data and the decoder reconstructs the characteristic of the original data from the compressed data. In this paper, actual raw data from the sensors attached in a screw air compressor of a railway vehicle is preprocessed by using a moving window and normalization, and then LSTM encoder-decoder and CNN encoder-LSTM decoder logics are examined for detecting anomalies exising in the sensor data. The CNN encoder-LSTM decoder logic showed slightly better performance than the LSTM encoder-decoder logic. The validity of the logics is demonstrated by using Python codes.