Advertisement

Knowledge Discovery and Knowledge Transfer

Chapter
  • 453 Downloads

Abstract

Reasoning-based methods have recently become popular since they overcome the knowledge-acquisition bottleneck during volume production and they can automatically generate an intelligent diagnostic system from existing resources [1, 2]. However, knowledge acquisition is a major problem for a reasoning-based method at the initial product ramp-up stage. Machine learning-based reasoning requires an adequate database for training the reasoning engine, and such a database becomes available much later in the product cycle. In this chapter, we propose a knowledge-discovery method and a knowledge-transfer method for facilitating board-level functional fault diagnosis at the initial product ramp-up stage. The proposed methods help address the knowledge gap between test design stage and volume production. First, an analysis technique based on topic model is used to discover knowledge from syndromes, which can be used for training a diagnosis engine. Second, knowledge from diagnosis engines used for earlier-generation products can be automatically transferred through root-cause mapping and syndrome mapping based on keywords and board-structure similarities.

Keywords

Board-level Early-generation product Fault diagnosis Functional failure Knowledge discovery Knowledge transfer Machine learning Topic modeling Transfer learning 

References

  1. 1.
    Fenton W, McGinnity T, Maguire L (2001) Fault diagnosis of electronic systems using intelligent techniques: a review. IEEE Trans Syst Man Cybern Part C: Appl Rev 31:269–281CrossRefGoogle Scholar
  2. 2.
    Watson I, Marir F (1994) Case-based reasoning: a review. Knowl Eng Rev 9(4):327–354CrossRefGoogle Scholar
  3. 3.
    Ye F, Zhang Z, Chakrabarty K, Gu X (2013) Board-level functional fault diagnosis using artificial neural networks, support-vector machines, and weighted-majority voting. IEEE Trans Comput-Aided Des Int Circuits Syst (TCAD) 32(5):723–736CrossRefGoogle Scholar
  4. 4.
    Ye F, Zhang Z, Chakrabarty K, Gu X (2012) “Adaptive board-level functional fault diagnosis using decision trees”. In: Proceedings of the IEEE Asian test symposium (ATS), pp. 202–207Google Scholar
  5. 5.
    Ye F, Zhang Z, Chakrabarty K, Gu X (2012) “Board-level functional fault diagnosis using learning based on incremental support-vector machines”. In: Proceedings of the IEEE Asian test symposium (ATS), pp. 208–213Google Scholar
  6. 6.
    Bolchini C, Quintarelli E, Salice F, Garza P (2013) “A data mining approach to incremental adaptive functional diagnosis”, In: Proceedings of the IEEE international symposium on defect and fault tolerance in VLSI systems (DFT), pp. 13–18Google Scholar
  7. 7.
    Eklow B, Hossein A, Khuong C, Pullela S, Vo T, Chau H (2004) “Simulation based system level fault insertion using co-verification tools”. In: Proceedings of the IEEE international test conference (ITC), pp. 704–710Google Scholar
  8. 8.
    Manley D, Eklow B, (2002) “A model based automated debug process”. In: Proceedings of the IEEE board test workshop, pp. 1–7Google Scholar
  9. 9.
    Feret MP, Glasgow JI (1997) Combining case-based and model-based reasoning for the diagnosis of complex devices. Appl Intell 7(1):57–78CrossRefGoogle Scholar
  10. 10.
    Wang L.-C (2013) “Data mining in design and test processes: basic principles and promises”. In: Proceedings of the IEEE international symposium on physical design, pp. 41–42Google Scholar
  11. 11.
    Klösgen W, Zytkow JM (2002) Handbook of data mining and knowledge discovery. Oxford University Press, OxfordGoogle Scholar
  12. 12.
    Steyvers M, Griffiths T (2007) Probabilistic topic models. Handb latent semant anal 427(7):424–440Google Scholar
  13. 13.
    Pan S, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359CrossRefGoogle Scholar
  14. 14.
    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Huawei TechnologiesSanta ClaraUSA
  2. 2.Department of Electrical and Computer EngineeringDuke UniversityDurhamUSA

Personalised recommendations