A prognostic assessment method for power electronics modules

This paper describes a prognostic method which combines the physics of failure models with probability reasoning algorithm. The measured real time data (temperature vs. time) was used as the loading profile for the PoF simulations. The response surface equation of the accumulated plastic strain in the solder interconnect in terms of two variables (average temperature, and temperature amplitude) was constructed. This response surface equation was incorporated into the lifetime model of solder interconnect, and therefore the remaining life time of the solder component under current loading condition was predicted. The predictions from PoF models were also used to calculate the conditional probability table for a Bayesian Network, which was used to take into account of the impacts of the health observations of each product in lifetime prediction. The prognostic prediction in the end was expressed as the probability for the product to survive the expected future usage. As a demonstration, this method was applied to an IGBT power module used for aircraft applications.

Exploring the conditional performance of UK unit trusts

We evaluate the conditional performance of U.K. equity unit trusts using the approach of Lynch and Wachter (2007, 2008) relative to three conditional linear factor models. We find significant time variation in the conditional performance of some trust portfolios and individual trusts using the lag term spread as the information variable. The conditional performance of the trusts is countercyclical and larger trusts have more countercyclical performance than smaller trusts within certain investment sectors. These patterns in conditional trust performance cannot be fully explained by the underlying securities that the trusts hold.