Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network-Based Remaining Useful Life Estimation of Bearings
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-09
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
In recent years, several time-frequency representation (TFR) and convolutional neural network- (CNN-) based approaches have been proposed to provide reliable remaining useful life (RUL) estimation for bearings.
However, existing methods cannot tackle the spatiotemporal continuity between adjacent TFRs since temporal proposals are considered individually and their temporal dependencies are neglected.
In allusion to this problem, a novel prognostic approach based on discrete cosine transformation (DCT) and temporal adjacent convolutional neural network (TACNN) is proposed.
Wavelet transform (WT) is applied to effectively map the raw signals to the time frequency domain.
Considering the high load and complexity of model computation, bilinear interpolation and DCT algorithm are introduced to convert TFRs into low-dimensional DCT spectrum coding matrix with strong sparsity.
Furthermore, the TACNN model is proposed which is capable of learning discriminative features for temporal adjacent DCT spectrum coding matrix.
Effectiveness of the proposed method is verified on the PRONOSTIA dataset, and experiment results show that the proposed model is able to realize automatic high-precision estimation of bearings RUL with high efficiency.
American Psychological Association (APA)
Pang, Yu& Jia, Limin& Liu, Zhan. 2020. Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network-Based Remaining Useful Life Estimation of Bearings. Shock and Vibration،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1210398
Modern Language Association (MLA)
Pang, Yu…[et al.]. Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network-Based Remaining Useful Life Estimation of Bearings. Shock and Vibration No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1210398
American Medical Association (AMA)
Pang, Yu& Jia, Limin& Liu, Zhan. Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network-Based Remaining Useful Life Estimation of Bearings. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1210398
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210398