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http://dx.doi.org/10.5394/KINPR.2022.46.3.280

Comparison of Prediction Accuracy Between Classification and Convolution Algorithm in Fault Diagnosis of Rotatory Machines at Varying Speed  

Moon, Ki-Yeong (Graduate School of Inha University)
Kim, Hyung-Jin (Graduate School of Inha University)
Hwang, Se-Yun (Inha University)
Lee, Jang Hyun (Department of Naval Architecture and Ocean Engineering, Inha University)
Publication Information
Journal of Navigation and Port Research / v.46, no.3, 2022 , pp. 280-288 More about this Journal
Abstract
This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.
Keywords
Variable rotation speed; Rotatory machine; Anomaly detection; Fault diagnosis; Machine learning; Convolutional neural network;
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Times Cited By KSCI : 2  (Citation Analysis)
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