Functional diagnosis for complex systems can be a very time-consuming and expensive task, trying to identify the source of an observed misbehavior. We propose an automatic incremental diagnostic methodology and CAD flow, based on data mining. It is a model-based approach that incrementally determines the tests to be executed to isolate the faulty component, aiming at minimizing the total number of executed tests, without compromising 100% diagnostic accuracy. The data mining engine allows for shorter test sequences with respect to other reasoningbased solutions (e.g., Bayesian belief networks), not requiring complex pre- and post-conditions management. Experimental results on a large set of synthetic examples and on three industrial boards substantiate the quality of the proposed approach.

An Expert CAD Flow for Incremental Functional Diagnosis of Complex Electronic Boards

BOLCHINI, CRISTIANA;CASSANO, LUCA MARIA;QUINTARELLI, ELISA;SALICE, FABIO
2015

Abstract

Functional diagnosis for complex systems can be a very time-consuming and expensive task, trying to identify the source of an observed misbehavior. We propose an automatic incremental diagnostic methodology and CAD flow, based on data mining. It is a model-based approach that incrementally determines the tests to be executed to isolate the faulty component, aiming at minimizing the total number of executed tests, without compromising 100% diagnostic accuracy. The data mining engine allows for shorter test sequences with respect to other reasoningbased solutions (e.g., Bayesian belief networks), not requiring complex pre- and post-conditions management. Experimental results on a large set of synthetic examples and on three industrial boards substantiate the quality of the proposed approach.
File in questo prodotto:
File Dimensione Formato  
final.pdf

Accesso riservato

: Pre-Print (o Pre-Refereeing)
Dimensione 563.47 kB
Formato Adobe PDF
563.47 kB Adobe PDF   Visualizza/Apri
An Expert CAD Flow for Incremental Functional Diagnosis_11311-907155_Bolchini.pdf

accesso aperto

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 419.57 kB
Formato Adobe PDF
419.57 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/907155
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 6
social impact