Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 66, Issue 4, Pages 3208-3216Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2018.2844856
Keywords
Bearing; multiscale convolutional neural network (MSCNN); remaining useful life estimation; time frequency representation (TFR)
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Funding
- National Research Foundation
- Sembcorp Industries Ltd.
- National University of Singapore under the Sembcorp-NUS Corporate Laboratory [R-261-513-003-281]
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Bearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing safe operation of machinery and reducing maintenance loss. In this paper, we present a new deep feature learning method for RUL estimation approach through time frequency representation (TFR) and multiscale convolutional neural network (MSCNN). TFR can reveal nonstationary property of a bearing degradation signal effectively. After acquiring time-series degradation signals, we get TFRs, which contain plenty of useful information using wavelet transform. Owing to high dimensionality, the size of these TFRs is reduced by bilinear interpolation, which are further regarded as inputs for deep learning models. Here, we introduce an MSCNN model structure, which keeps the global and local information synchronously compared to a traditional convolutional neural network (CNN). The salient features, which contribute for RUL estimation, can be learned automatically by MSCNN. The effectiveness of the presented method is validated by the experiment data. Compared to traditional data-driven and different CNN-based feature extraction methods, the proposed method shows enhanced performance in the prediction accuracy.
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