Building reliability growth model using sequential experiments and the Bayesian theorem for small datasets

Authors:
Der-Chiang Li;Fengming M. Chang;Kuei-Ching Chen
Affiliations:
Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan;Department of Information Management, National Taitung Junior College, Taitung, Taiwan;The United Microelectronics Corporation, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 3
Cited 1

Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation

Engineering Applications of Artificial Intelligence
Software maintenance project delays prediction using Bayesian Networks

Expert Systems with Applications: An International Journal
An exploratory project expert system for eliciting correlation coefficient and sequential updating of duration estimation

Expert Systems with Applications: An International Journal

A tree-based-trend-diffusion prediction procedure for small sample sets in the early stages of manufacturing systems

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Early in the process of developing a new product, on hand data is usually scarce and it is hard to assess product reliability. Many previous studies thus assume prior product failure distribution in the area of reliability estimation. However, when real data distribution is different from the assumed one, the reliability estimation accuracy is biased. This paper proposed a modeling method with four steps to build a reliability growth model for new products without assuming prior distribution. A sequential test procedure and the Bayesian theorem are mainly employed in the method presented. The results indicate that the proposed method can successfully build a reliability growth model for new products.