Journal
APPLIED SCIENCES-BASEL
Volume 8, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/app8071102
Keywords
continuous wavelet transform; convolutional neural network; bearings; remaining useful lifetime; prognostics and health management; health indicator
Categories
Funding
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [NRF-2016R1A2B4013678]
- BK21 Plus program (Big Data in Manufacturing and Logistics Systems, Korea University)
- Samsung Electronics Co., Ltd.
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In data-driven methods for prognostics, the remaining useful lifetime (RUL) is predicted based on the health indicator (HI). The HI detects the condition of equipment or components by monitoring sensor data such as vibration signals. To construct the HI, multiple features are extracted from signals using time domain, frequency domain, and time-frequency domain analyses, and which are then fused. However, the process of selecting and fusing features for the HI is very complex and labor-intensive. We propose a novel time-frequency image feature to construct HI and predict the RUL. To convert the one-dimensional vibration signals to a two-dimensional (2-D) image, the continuous wavelet transform (CWT) extracts the time-frequency image features, i.e., the wavelet power spectrum. Then, the obtained image features are fed into a 2-D convolutional neural network (CNN) to construct the HI. The estimated HI from the proposed model is used for the RUL prediction. The accuracy of the RUL prediction is improved by using the image features. The proposed method compresses the complex process including feature extraction, selection, and fusion into a single algorithm by adopting a deep learning approach. The proposed method is validated using a bearing dataset provided by PRONOSTIA. The results demonstrate that the proposed method is superior to related studies using the same dataset.
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