Automotive reliability inference based on past data and technical knowledge

The constantly increasing 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 are 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.