Abstract
Reliability testing is an indispensable tool for evaluating the lifetime of a product. However, for a highly reliable product, it is quite common that a large proportion of test units will be censored in a regular life test or even in accelerated life testing (ALT) when the total testing time is too short. As an alternative, accelerated degradation testing (ADT) can be conducted to collect degradation data of a highly reliable product under accelerated conditions. For a reliability practitioner, it will be very valuable to use both ALT and ADT data for reliability estimation. In practice, degradation data are often contaminated by measurement error, which may affect the accuracy of reliability estimation. Therefore, a statistical procedure is needed when using both ALT data and ADT data with measurement error for evaluating the reliability of a highly reliable product. In this paper, an Inverse Gaussian (IG) process is used to model the degradation process of a product considering measurement error. To incorporate the two types of accelerated testing data, a new expectation-maximization (EM) algorithm is developed to estimate the model parameters by taking advantage of the parameter structure. A simulation study and a case study on a hydraulic piston pump are presented to illustrate the practical value of the proposed method in improving the accuracy of reliability estimation for a highly reliable product.
| Original language | English (US) |
|---|---|
| Article number | 106610 |
| Journal | Reliability Engineering and System Safety |
| Volume | 193 |
| DOIs | |
| State | Published - Jan 1 2020 |
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Keywords
- Accelerated degradation testing (ADT)
- Accelerated life testing (ALT)
- Expectation-maximization (EM)
- Inverse Gaussian (IG) process
- Measurement error
- Reliability estimation
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering
Cite this
Reliability estimation from two types of accelerated testing data considering measurement error. / Ma, Zhonghai; Wang, Shaoping; Ruiz, Cesar; Zhang, Chao; Liao, Haitao; Pohl, Edward.
In: Reliability Engineering and System Safety, Vol. 193, 106610, 01.01.2020.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Reliability estimation from two types of accelerated testing data considering measurement error
AU - Ma, Zhonghai
AU - Wang, Shaoping
AU - Ruiz, Cesar
AU - Zhang, Chao
AU - Liao, Haitao
AU - Pohl, Edward
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Reliability testing is an indispensable tool for evaluating the lifetime of a product. However, for a highly reliable product, it is quite common that a large proportion of test units will be censored in a regular life test or even in accelerated life testing (ALT) when the total testing time is too short. As an alternative, accelerated degradation testing (ADT) can be conducted to collect degradation data of a highly reliable product under accelerated conditions. For a reliability practitioner, it will be very valuable to use both ALT and ADT data for reliability estimation. In practice, degradation data are often contaminated by measurement error, which may affect the accuracy of reliability estimation. Therefore, a statistical procedure is needed when using both ALT data and ADT data with measurement error for evaluating the reliability of a highly reliable product. In this paper, an Inverse Gaussian (IG) process is used to model the degradation process of a product considering measurement error. To incorporate the two types of accelerated testing data, a new expectation-maximization (EM) algorithm is developed to estimate the model parameters by taking advantage of the parameter structure. A simulation study and a case study on a hydraulic piston pump are presented to illustrate the practical value of the proposed method in improving the accuracy of reliability estimation for a highly reliable product.
AB - Reliability testing is an indispensable tool for evaluating the lifetime of a product. However, for a highly reliable product, it is quite common that a large proportion of test units will be censored in a regular life test or even in accelerated life testing (ALT) when the total testing time is too short. As an alternative, accelerated degradation testing (ADT) can be conducted to collect degradation data of a highly reliable product under accelerated conditions. For a reliability practitioner, it will be very valuable to use both ALT and ADT data for reliability estimation. In practice, degradation data are often contaminated by measurement error, which may affect the accuracy of reliability estimation. Therefore, a statistical procedure is needed when using both ALT data and ADT data with measurement error for evaluating the reliability of a highly reliable product. In this paper, an Inverse Gaussian (IG) process is used to model the degradation process of a product considering measurement error. To incorporate the two types of accelerated testing data, a new expectation-maximization (EM) algorithm is developed to estimate the model parameters by taking advantage of the parameter structure. A simulation study and a case study on a hydraulic piston pump are presented to illustrate the practical value of the proposed method in improving the accuracy of reliability estimation for a highly reliable product.
KW - Accelerated degradation testing (ADT)
KW - Accelerated life testing (ALT)
KW - Expectation-maximization (EM)
KW - Inverse Gaussian (IG) process
KW - Measurement error
KW - Reliability estimation
UR - http://www.scopus.com/inward/record.url?scp=85070919207&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070919207&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2019.106610
DO - 10.1016/j.ress.2019.106610
M3 - Article
AN - SCOPUS:85070919207
VL - 193
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
M1 - 106610
ER -