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http://dx.doi.org/10.7837/kosomes.2021.27.1.127

Experimental Study on Application of an Anomaly Detection Algorithm in Electric Current Datasets Generated from Marine Air Compressor with Time-series Features  

Lee, Jung-Hyung (Division of Marine System Engineering, Mokpo Maritime University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.27, no.1, 2021 , pp. 127-134 More about this Journal
Abstract
In this study, an anomaly detection (AD) algorithm was implemented to detect the failure of a marine air compressor. A lab-scale experiment was designed to produce fault datasets (time-series electric current measurements) for 10 failure modes of the air compressor. The results demonstrated that the temporal pattern of the datasets showed periodicity with a different period, depending on the failure mode. An AD model with a convolutional autoencoder was developed and trained based on a normal operation dataset. The reconstruction error was used as the threshold for AD. The reconstruction error was noted to be dependent on the AD model and hyperparameter tuning. The AD model was applied to the synthetic dataset, which comprised both normal and abnormal conditions of the air compressor for validation. The AD model exhibited good detection performance on anomalies showing periodicity but poor performance on anomalies resulting from subtle load changes in the motor.
Keywords
Air compressor; Anomaly detection; Condition-based maintenance (CBM); Autoencoder; Machine learning;
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