Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions

Ahmed Ragab (), Soumaya Yacout (), Mohamed-Salah Ouali () and Hany Osman ()
Additional contact information
Ahmed Ragab: École Polytechnique de Montréal
Soumaya Yacout: École Polytechnique de Montréal
Mohamed-Salah Ouali: École Polytechnique de Montréal
Hany Osman: École Polytechnique de Montréal

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 1, No 20, 255-274

Abstract: Abstract This paper presents a novel methodology for multiple failure modes prognostics in rotating machinery. The methodology merges a machine learning and pattern recognition approach, called logical analysis of data (LAD), with non-parametric cumulative incidence functions (CIFs). It considers the condition monitoring data collected from a system that experiences several competing failure modes over its life span. LAD is used as a non-statistical classification technique to detect the actual state of the system, based on the condition monitoring data. The CIF provides an estimate for the marginal probability of each failure mode in the presence of the other competing failure modes. Accordingly, the assumption of independence between the failure modes, which is essential in many prognostic methods, is irrelevant in this paper. The proposed methodology is validated using vibration data collected from bearing test rigs. The obtained results are compared to those of two common machine learning prediction techniques: the artificial neural network and support vector regression. The comparison shows that the proposed methodology has a stable performance and can predict the remaining useful life of an individual system accurately, in the presence of multiple failure modes.

Keywords: Failure prognostics; Multiple failure modes; Logical analysis of data; Machine learning; CBM; Survival analysis; Rotating machinery (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-016-1244-8 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_s10845-016-1244-8

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

DOI: 10.1007/s10845-016-1244-8

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 ().