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http://dx.doi.org/10.5394/KINPR.2022.46.4.367

The Fault Diagnosis Model of Ship Fuel System Equipment Reflecting Time Dependency in Conv1D Algorithm Based on the Convolution Network  

Kim, Hyung-Jin (Graduate School of Inha University)
Kim, Kwang-Sik (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.4, 2022 , pp. 367-374 More about this Journal
Abstract
The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.
Keywords
CNN (Convolution Neural Network); Conv1D; time series; PHM((Prognostics and health management); fault diagnosis;
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Times Cited By KSCI : 1  (Citation Analysis)
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