Data Regeneration Based on Multiple Degradation Processes for Remaining Useful Life Estimation

Ningning Yang, Zhijian Wang, Wenan Cai and Yanfeng Li

Reliability Engineering and System Safety, 2023, vol. 229, issue C

Abstract: Remaining useful life prediction based on deep learning for critical components demands sufficient and varied degradation samples. However, the field acquisition or laboratory preparation is generally cumbersome or the samples obtained are stereotyped. The paper proposes a data regeneration method based on multiple degradation processes to deal with the dilemma, which consists of three parts: state identification, regeneration rules from run to failure and state databases. In the first part, a global gain index and a local gain index are proposed to identify the different states of components. In the second part, an identical transformation method, a probability distribution of degradation states and data regeneration criteria are proposed to serve regeneration process of samples from run to failure. In the third part, an augmentation framework based on conditional generative adversarial networks is proposed to enrich the samples of the state database, which makes state samples more diverse. The practicability of regenerated samples obtained by the proposed method was verified by two experiments. In each experiment, initial samples, regenerated samples and hybrid samples were established respectively. Experiments with different training samples based on the same network were carried out to verify the effectiveness of the regenerated samples.

Keywords: Data regeneration; Deep learning; Remaining useful life; Regeneration rules; State identification (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832022004847
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:229:y:2023:i:c:s0951832022004847

DOI: 10.1016/j.ress.2022.108867

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 ().