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http://dx.doi.org/10.7837/kosomes.2021.27.7.1088

Vibration Data Denoising and Performance Comparison Using Denoising Auto Encoder Method  

Jang, Jun-gyo (Dept. of Ocean System Engineering, Gyeongsang Nat'l Univ.)
Noh, Chun-myoung (Dept. of Ocean System Engineering, Gyeongsang Nat'l Univ.)
Kim, Sung-soo (Adia Lab inc.)
Lee, Soon-sup (Dept. of Naval Architecture and Ocean Engineering, Gyeongsang Nat'l Univ.)
Lee, Jae-chul (Dept. of Naval Architecture and Ocean Engineering, Gyeongsang Nat'l Univ.)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.27, no.7, 2021 , pp. 1088-1097 More about this Journal
Abstract
Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.
Keywords
Denoising Auto Encoder; Prognostics Health Management (PHM); Wavelet Transform; One-Class Support Vector Machine; Noise Reduction;
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Times Cited By KSCI : 1  (Citation Analysis)
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