A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings

Yudong Cao, Yifei Ding, Minping Jia and Rushuai Tian

Reliability Engineering and System Safety, 2021, vol. 215, issue C

Abstract: Remaining useful life (RUL) prediction has been a hotspot in the engineering field, which is useful to avoid unexpected breakdowns and reduce maintenance costs of the system. Due to the high nonlinearity and complexity of mechanical systems, traditional methods cannot meet the requirements of medium-term and long-term prediction tasks, and often ignore the influence of temporal information on prediction performance. To solve this problem, this paper proposes a new deep learning framework – Temporal convolutional network with residual self-attention mechanism (TCN-RSA), which can learn both time-frequency and temporal information of signals. First, we input the marginal spectrum of vibration signals to TCN. The causal dilated convolution structure in the TCN has the ability to capture long-term dependencies and extract high-level feature representations from the time-frequency domain at the same time. Then, a residual self-attention mechanism is introduced to obtain the feature contribution degree of different moments in the bearing degradation process. Finally, an end-to-end RUL prediction implementation can be established based on TCN-RSA network. The effectiveness of the proposed method is verified by IEEE PHM 2012 Data challenge datasets and XJTU-SY datasets respectively. The comparative study indicates that the proposed TCN-RSA framework outperforms the other state-of-the-art methods in RUL prediction and system prognosis with respect to better accuracy and computation efficiency.

Keywords: Remaining useful life prediction; Temporal convolutional; Self-attention mechanism; Rolling bearings (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (39)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832021003355
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021003355

DOI: 10.1016/j.ress.2021.107813

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().