EconPapers    
Economics at your fingertips  
 

Two improvements of similarity-based residual life prediction methods

Mengyao Gu () and Youling Chen ()
Additional contact information
Mengyao Gu: Chongqing University
Youling Chen: Chongqing University

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 1, No 23, 303-315

Abstract: Abstract The similarity-based residual life prediction (SbRLP) approach is an emerging technique and occupies a significant place in remaining useful life (RUL) prediction. Researches on (a) considering different operating conditions; and (b) considering maintenance are rare. But aforesaid factors have great influence on effective utilization of the SbRLP method. In this article, improvements are implemented from two above perspectives and thus a novel weight function and a fresh similarity measurement are advanced. Afterwards, a case study of the gyroscope’ RUL estimation demonstrates the reasonability and effectiveness of the proposed weight function and similarity measurement through comparisons with the classical SbRLP method. Meanwhile, the investigation results reveal that the performance of the SbRLP method with the recommended weight function improves fast with the increment of available reference systems, which have different operating conditions with the operating systems. And with the increase of maintenance frequency, the difference between the local performance of the SbRLP method with the introduced similarity measurement and that of the classical SbRLP method decreases gradually, which is just the opposite of the difference between their overall performances.

Keywords: SbRLP; RUL; Similarity measure; Weight function; Gyroscope; Performance (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-016-1249-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1249-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-016-1249-3

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1249-3