Developing ladder network for intelligent evaluation system: Case of remaining useful life prediction for centrifugal pumps

Rui He, Yiyang Dai, Jiachen Lu and Chuanlin Mou

Reliability Engineering and System Safety, 2018, vol. 180, issue C, 385-393

Abstract: Intelligent evaluation system has been widely used in industries to estimate essential indexes which are unable to be measured directly through physical devices. Due to the complexity of labeling samples, common data-driven techniques such as supervised learning are developed on a small number of labeled data, while a large amount of unlabeled data is discarded. The amount of labeled information greatly limits the improvement of prediction accuracies. Furthermore, conventional evaluation approaches have only static structures, which makes the dynamic characteristics of parameters difficult to be presented. This paper proposes a ladder network (LN) based semi-supervised learning model to evaluate parameter dynamics, and a case of remaining useful life (RUL) prediction for centrifugal pumps is illustrated. LN datasets comprise a small part of labeled data and a large amount of unlabeled data. We exploited fluid-structure interaction (FSI) numerical simulation to replace actual monitoring, as well as built a RUL prediction model to annotate useful life for offline datasets. After that, the RUL was performed in the online stage by substituting real-time monitored variables into the network. The case study indicates that the LN-based intelligent evaluation system identifies the real-time RUL profile and achieves better predictive outcomes than supervised learning approaches.

Keywords: Ladder network; Deep learning; Intelligent system; RUL prediction; Fluid-structure interaction; Centrifugal pump (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832018304976
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:180:y:2018:i:c:p:385-393

DOI: 10.1016/j.ress.2018.08.010

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 ().