A Bounded Intensity Process Reliability Growth Model in a Bayes-Decision Framework
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Preeti Wanti Srivastava
and Nidhi Jain
Abstract
In this paper a Bounded Intensity Process (BIP) reliability growth model is used to analyze the failure data from repairable systems undergoing a Test-Find-Test growth program, in a Bayes-decision framework. Such an analysis is helpful in improving system reliability. Several identical copies of the equipment are put on test at each development stage. At the end of each stage, a decision between two alternative actions, viz., (a) to accept the current design of the system for mass production, or (b) to continue the development program, is made. The mean number of failures in a prefixed time interval is used to measure the system reliability at each testing stage, so that the decision process is based on the posterior distribution of this quantity and on specific loss functions that measure the economical consequences associated with each alternative action. A numerical example is used to illustrate the decision process.
© de Gruyter 2010
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- An Addendum to the Estimators of Murthy–Sarma and Anis–Pandey of the Mean of the Normal Distribution
- A Bounded Intensity Process Reliability Growth Model in a Bayes-Decision Framework
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Articles in the same Issue
- Editorial
- Optimal Replacement Policy Based on the Number of Down Times
- An L-Banded Approximation to the Inverse of Symmetric Toeplitz Matrices
- Selection of Mixed Sampling Plans for Second Quality Lots
- On New Perspectives for Statistical Computing in Business and Industry – A Solution with STATISTICA and R
- Reliability Analysis of k-out-of-n : G Repairable Shared Load Systems with Multiple Failures and Preventive Maintenance
- Monitoring the Parameters of the Market Model by Linear Profile Procedures
- An Addendum to the Estimators of Murthy–Sarma and Anis–Pandey of the Mean of the Normal Distribution
- A Bounded Intensity Process Reliability Growth Model in a Bayes-Decision Framework
- Optimal Structure in Heterogeneous Multi-state Series-parallel Reliability Systems