Abstract
In this paper, we consider a Bayesian dynamic forecasting model that utilizes both the engineering knowledge about the product reliability and attributes (success or failure) data gathered from the inspection of the early stage of development or storage. We assume that a prior distribution of reliability follows a beta distribution. The expected reliability is represented as a cumulative logistic function of the length of time that a new product has been under development or a finished item has been stockpiled in storage. As periodic testing produces attribute data, a prior distribution is updated. The expected reliability and forecasting errors are obtained from a posterior distribution that reflects the uncertainty involved in forecasting. The proposed method is applied to predict the expected reliability decay of a gyroscope in a missile stockpile.
| Original language | English |
|---|---|
| Pages (from-to) | 741-744 |
| Number of pages | 4 |
| Journal | Computers and Industrial Engineering |
| Volume | 33 |
| Issue number | 3-4 |
| Publication status | Published - 1997 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Engineering(all)
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Bayesian dynamic forecasting for attribute reliability. / Sohn, So Young.
In: Computers and Industrial Engineering, Vol. 33, No. 3-4, 01.12.1997, p. 741-744.Research output: Contribution to journal › Article
TY - JOUR
T1 - Bayesian dynamic forecasting for attribute reliability
AU - Sohn, So Young
PY - 1997/12/1
Y1 - 1997/12/1
N2 - In this paper, we consider a Bayesian dynamic forecasting model that utilizes both the engineering knowledge about the product reliability and attributes (success or failure) data gathered from the inspection of the early stage of development or storage. We assume that a prior distribution of reliability follows a beta distribution. The expected reliability is represented as a cumulative logistic function of the length of time that a new product has been under development or a finished item has been stockpiled in storage. As periodic testing produces attribute data, a prior distribution is updated. The expected reliability and forecasting errors are obtained from a posterior distribution that reflects the uncertainty involved in forecasting. The proposed method is applied to predict the expected reliability decay of a gyroscope in a missile stockpile.
AB - In this paper, we consider a Bayesian dynamic forecasting model that utilizes both the engineering knowledge about the product reliability and attributes (success or failure) data gathered from the inspection of the early stage of development or storage. We assume that a prior distribution of reliability follows a beta distribution. The expected reliability is represented as a cumulative logistic function of the length of time that a new product has been under development or a finished item has been stockpiled in storage. As periodic testing produces attribute data, a prior distribution is updated. The expected reliability and forecasting errors are obtained from a posterior distribution that reflects the uncertainty involved in forecasting. The proposed method is applied to predict the expected reliability decay of a gyroscope in a missile stockpile.
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UR - http://www.scopus.com/inward/citedby.url?scp=0031386401&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0031386401
VL - 33
SP - 741
EP - 744
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
SN - 0360-8352
IS - 3-4
ER -