Automotive reliability inference based on past data and technical knowledge

The constantly increasingly market requirements of high quality vehicles ask for the automotive manufacturers to carry out – before starting mass production – reliability demonstration tests on new products. However, due to cost and time limitation, a small number of copies of the new product is available for testing, so that, when the classical approach is used, a very low level of confidence in reliability estimation results in. In this paper, a Bayes procedure is proposed for making inference on the reliability of a new upgraded version of a mechanical component, by using both failure data relative to a previous version of the component and prior information on the effectiveness of design modifications introduced in the new version. The proposed procedure is then applied to a case study and its feasibility in supporting reliability estimation is illustrated.