Reliability prediction for complex medical systems

In innovative fast product development processes is it necessary to determine as fast as possible whether the product reliability is at the right level. This paper describes analyses of field data for several apparently similar systems during the warranty period. The paper considers the adequacy of the widely used non-homogeneous Poisson process with different intensity functions. The non-parametric Nelson-Aalen model is used to allow the data to "speak". The analyses are of field data from medical imaging systems. While the systems are apparently similar, the data show large differences in performances. The overall aim of this research is to develop methods and techniques based on the existing field data to predict the occurrence of product failures in the development process and early in the field introduction. The company where this case study is performed wants to be able to monitor, control and predict the product reliability in an earlier stage. In this way the feedback loop can be shortened, which leads to faster problem recognition