Degradation modeling and monitoring of truncated degradation signals

Rensheng Zhou, Nagi Gebraeel and Nicoleta Serban

IISE Transactions, 2012, vol. 44, issue 9, 793-803

Abstract: Advancements in condition monitoring techniques have facilitated the utilization of sensor technology for predicting failures of engineering systems. Within this context, failure is defined as the point where a sensor-based degradation signal reaches a pre-specified failure threshold. Parametric degradation models rely on complete signals to estimate the parametric functional form and do not perform well with sparse historical data. On the other hand, non-parametric models that address the challenges of data sparsity usually assume that signal observations can be made beyond the failure threshold. Unfortunately, in most applications, degradation signals can only be observed up to the failure threshold resulting in what this article refers to as truncated degradation signals. This article combines a non-parametric degradation modeling framework with a signal transformation procedure, allowing different types of truncated degradation signals to be characterized. This article considers (i) complete signals that result from constant monitoring of a system up to its failure; (ii) sparse signals resulting from sparse observations; and (iii) fragmented signals that result from dense observations over disjoint time intervals. The goal is to estimate and update the residual life distributions of partially degraded systems using in situ signal observations. It is showed that the proposed model outperforms existing models for all three signal types.

Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1080/0740817X.2011.618175 (text/html)
Access to full text is restricted to subscribers.

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:taf:uiiexx:v:44:y:2012:i:9:p:793-803

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/0740817X.2011.618175

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().