In:
Measurement Science and Technology, IOP Publishing, Vol. 34, No. 7 ( 2023-07-01), p. 075019-
Abstract:
The success of rotating machines’ data-driven remaining useful life (RUL) prognosis approaches depends heavily on the abundance of entire life cycle data. However, it is not easy to obtain sufficient run-to-failure data in industrial practice. Data generation technology is a promising solution for enriching data but fails to address the intrinsic complexity of nonlinear stage degradation and the time correlation of long-term data. This research proposes an RUL prognosis approach improved by the degradation trend feature generation variational autoencoder. First, this study develops a framework combining degradation trend generation features to resolve the issue of capturing the elements of time distribution for run-to-failure data. Second, a generation variational autoencoder network with a tendency block is proposed to create high-quality time series data correlation features. Third, original and created degradation trend features are subjected to deep adaptive fusion and health indicator extraction. A bi-directional long short-term memory network is employed to predict the degradation trend and obtain the RUL prognosis. Finally, the proposed approach’s feasibility is confirmed by cross-validation experiments on a bearing dataset, which reduces the prediction error by 22.309%.
Type of Medium:
Online Resource
ISSN:
0957-0233
,
1361-6501
DOI:
10.1088/1361-6501/accbde
Language:
Unknown
Publisher:
IOP Publishing
Publication Date:
2023
detail.hit.zdb_id:
1362523-8
detail.hit.zdb_id:
1011901-2
SSG:
11
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