A DFMEA-based Reliability Prediction Approach in Early Product Design

Abstract

In early product development stages, reliability growth testing (RGT) has been traditionally used for reliability monitoring and prediction through testing data. It has been criticized that the traditional reliability growth models are not only costly but also time consuming in terms of discovering and fixing potential failure modes. To tackle such challenges, accelerated reliability growth testing methods are adopted in industry. However, system level reliability growth testing can be still very challenging due to the stringent budget and tight product development cycle. On the other hand, reliability knowledge and information from the product development and design processes have not been well utilized for reliability prediction and management. These information include design simulation outcomes from finite element analysis (FEA) and computational fluid analysis (CFD), physics-of-failure (PoF) failure models and failure analysis from field failure events, and manufacturing defect reports. Such reliability information are usually included in the design failure mode and effect analysis (DFMEA) reports. This research explores a method for system reliability prediction by aggregating all the above mentioned reliability knowledge embedded in DFMEA.