Automatic Systems Diagnosis Without Behavioral Models
Details
2014 March 1-8. Big Sky, Montana, USA
Speakers
Victoria M E Bellotti
Event
Automatic Systems Diagnosis Without Behavioral Models
Recent feedback obtained based diagnosis (MBD) in industry suggests that the costs in- volved in behavioral modeling (both expertise and labor) can outweigh the benefits of MBD as a high-performance diagnosis approach. In this paper, we propose an automatic approach, called AMADIOS, that completely avoids behavioral modeling. Decreasing modeling sacrifices diagnostic accuracy, as the size of the ambiguity group (i.e., components which cannot be discriminated because of the lack of information) increases, which in turn increases misdiagnosis penalty. AMADIOS further breaks the ambiguity group size by considering the components false negative rate (FNR), which is estimated using an analytical expression. Furthermore, we study the performance of AMADIOS for a number of logic circuits taken from the 74XXX/ISCAS benchmark suite. Our results clearly indicate that sacrificing modeling information degrades the diagnosis quality. However, considering FNR information improves the quality, attaining the diagnostic performance of an MBD approach.
Additional information
Focus Areas
Our work is centered around a series of Focus Areas that we believe are the future of science and technology.
Licensing & Commercialization Opportunities
We’re continually developing new technologies, many of which are available for Commercialization.
News
PARC scientists and staffers are active members and contributors to the science and technology communities.