The platform will undergo maintenance on Sep 14 at about 7:45 AM EST and will be unavailable for approximately 2 hours.
2016
DOI: 10.17531/ein.2016.1.13
|View full text |Cite
|
Sign up to set email alerts
|

Reliability analysis of the products subject to competing failure processes with unbalanced data

Abstract: Reliability analysis of the pRoducts subject to competing failuRe pRocesses with unbalanced data opaRta na nieZbilansowanych danych analiZa nieZawodnoŚci pRoduKtÓw podlegajĄcych pRocesom powstawania usZKodZeŃ KonKuRujĄcych LI J, ZHANG Y, WANG Z, FU H, XIAO F. Reliability analysis of the products subject to competing failure processes with unblanced data. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2016; 18 (1): 98-109, http://dx

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2016
2016
2023
2023

Publication Types

Select...
1
1

Relationship

1
1

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 23 publications
0
1
0
Order By: Relevance
“…Lee et al [6] reviewed the methodologies and applications of prognostics and health management design for rotary machinery systems. Li et al [7] analyzed products reliability using unbalanced data, in which unbalanced data means the number and time of measurements are not identical for degradation units. Roughly, the existing work can be divided into two classes, one class is the prediction models and methods are population-based, which means there are failure or suspension histories of same type components can be used.…”
Section: Introductionmentioning
confidence: 99%
“…Lee et al [6] reviewed the methodologies and applications of prognostics and health management design for rotary machinery systems. Li et al [7] analyzed products reliability using unbalanced data, in which unbalanced data means the number and time of measurements are not identical for degradation units. Roughly, the existing work can be divided into two classes, one class is the prediction models and methods are population-based, which means there are failure or suspension histories of same type components can be used.…”
Section: Introductionmentioning
confidence: 99%