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.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence