A Prognostic and Data Fusion Based Approach to Validating Automotive Electronics

Paper #:
  • 2014-01-0724

Published:
  • 2014-04-01
DOI:
  • 10.4271/2014-01-0724
Citation:
Braden, D. and Harvey, D., "A Prognostic and Data Fusion Based Approach to Validating Automotive Electronics," SAE Technical Paper 2014-01-0724, 2014, https://doi.org/10.4271/2014-01-0724.
Pages:
7
Abstract:
There is a continual growth of test and validation in high reliability product applications such as automotive, military and avionics. Principally this is driven by the increased use and complexity of electronic systems deployed in vehicles, in addition to end user reliability expectations. Higher reliability expectations consequently driving increased test durations. Furthermore product development cycles continue to reduce, resulting in less available time to perform accelerated life tests. The challenge for automotive electronic suppliers is performing life tests in a shorter period of time whilst reducing the overall associated costs of validation testing.In this paper, the application of prognostic and health monitoring techniques are examined and a novel approach to the validation and testing of automotive electronics proposed which it is suggested may be more cost effective and efficient than traditional testing. The holistic method explored in this paper fuses real time test data obtained during the monitoring of products throughout an environmental exposure with key factors from manufacturing and product design. Factors such as printed circuit board warpage and board stresses introduced during the reflow process are known to affect solder joint longevity. Equally, previous work by the authors on component floor plan layout and constraint point influences showed in the case of area array packaged components a reduction of up to 56% in solder joint reliability [1,2,3].Inclusion of such factors in a real time prognostic engine is expected to yield not only improved reliability prediction accuracy but additionally offer shorter test execution times and resource savings.
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