Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76260
Title: Inference on an adaptive accelerated life test with application to smart-grid data-acquisition-devices
Authors: Shen, LJ
Sun, DY 
Ye, ZS
Zhao, XQ 
Keywords: Adaptive Test
Log-Location-Scale Distribution
Order Statistics
Step-Stress Test
Type-I Censoring
Issue Date: 2017
Publisher: Amer Soc Quality Control (ASQC)
Source: Journal of quality technology, 2017, v. 49, no. 3, p. 191-212 How to cite?
Journal: Journal of quality technology 
Abstract: An accelerated life test (ALT) is often well planned to yield the most statistical information given limited test resources. Nevertheless, ALT planning requires rough estimates of the model parameters as an input, called planning values. The discrepancy between the planning values and the true values may result in insufficient or even no failures at the low-stress level, making the subsequent data analysis difficult. Motivated by the need in the ALTs of data acquisition devices used in smart grids, an adaptive ALT scheme is proposed. The key idea is based on the observation that, when the product reliability is underestimated during the ALT design phase, it is unlikely to observe failures at the early stage of the test. Therefore, the low-stress level should be elevated to protect against insufficient failures. Under this adaptive ALT framework, order statistics techniques are used to derive the likelihood function by assuming a general log location-scale distribution for the product lifetime. Confidence intervals for the parameters are constructed based on the large-sample approximation as well as the accelerated bootstrap method. A simulation study is conducted to demonstrate the advantages of the adaptive ALT compared with the simple constant-stress ALT. Its application is illustrated using the motivating example from smart grids.
URI: http://hdl.handle.net/10397/76260
ISSN: 0022-4065
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

3
Citations as of May 21, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.