Abstract
The diagnosis accuracy of reasoning-based diagnosis engine may be significantly reduced when the repair logs are fragmented and some errors, or syndromes, are not available during diagnosis. Since root-cause isolation for a failing board relies on reasoning based on syndromes, any information loss (e.g., missing syndromes) during the extraction of a diagnosis log may lead to ambiguous repair suggestions. In this chapter, we propose a board-level diagnosis system with the feature of handling missing syndromes using the method of imputation. The syndromes from a faulty-board log are analyzed and imputed with appropriate values in a preprocessing engine before root-cause isolation. We utilize several imputation methods and compare them in terms of their effectiveness in handling missing syndromes.
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References
Bushnell M, Agrawal V (2000) Essentials of electronic testing for digital, memory, and mixed-signal VLSI circuits. Springer, New York
O’Farrill C, Moakil-Chbany M, Eklow B (2005) Optimized reasoning-based diagnosis for non-random, board-level, production defects. In: Proceedings IEEE international test conference (ITC), pp 173–179
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 Integr Circuits Syst (TCAD) 32(5):723–736
Zhang Z, Gu X, Xie Y, Wang Z, Chakrabarty K (2012) Diagnostic system based on support-vector machines for board-level functional diagnosis. In: Proceedings IEEE European test symposium (ETS), pp 1–6
Ye F, Zhang Z, Chakrabarty K, Gu X (2012) Adaptive board-level functional fault diagnosis using decision trees. In: Proceedings IEEE Asian test symposium (ATS), pp 202–207
Vo T, Wang Z, Eaton T, Ghosh P, Li H, Lee Y, Wang W, Jun H, Fang R, Singletary D, Gu X (2006) Design for board and system level structural test and diagnosis. In: Proceedings IEEE international test conference (ITC), pp 1–10
Zhang Z, Chakrabarty K, Wang Z, Wang Z, Gu X (2011) Smart diagnosis: efficient board-level diagnosis and repair using artificial neural networks. In: Proceedings IEEE international test conference (ITC), pp 1–10
Amati L (2012) Test and diagnosis strategies for digital devices: methodologies and tools. PhD dissertation, Politecnico di Milano, Italy
Pelckmans K et al (2005) Handling missing values in support vector machine classifiers. Neural Netw 18(5):684–692
Chechik G et al (2008) Max-margin classification of data with absent features. J Mach Learn Res 9:1–21
Saar-Tsechansky M, Provost F (2007) Handling missing values when applying classification models. J Mach Learn Res 8:1625–1657
Hruschka ER et al (2003) Evaluating a nearest-neighbor method to substitute continuous missing values. Advances in artificial intelligence. Springer, Berlin, pp 723–734
Rish I (2001) An empirical study of the naive bayes classifier. IBM New York workshop on empirical methods in artificial intelligence, vol 3(22), pp 41–46
Quinlan J (1986) Induction of decision trees. Mach Learn 1(1):81–106
Ding Y, Peng X, Fu X (2009) The research of artificial neural network on negative correlation learning. Advances in computation and intelligence. Springer, New York, pp 392–399
Prieto B, de Lope J, Maravall D (2005) Reconfigurable hardware implementation of neural networks for humanoid locomotion. Artificial intelligence and knowledge engineering applications: a bioinspired approach. Springer, New York, pp 395–404
Vapnik V (1995) The nature of statistical learning theory. Springer, New York
Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology, vol 2, pp 27:1–27:27. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
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Ye, F., Zhang, Z., Chakrabarty, K., Gu, X. (2017). Handling Missing Syndromes. In: Knowledge-Driven Board-Level Functional Fault Diagnosis. Springer, Cham. https://doi.org/10.1007/978-3-319-40210-9_6
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DOI: https://doi.org/10.1007/978-3-319-40210-9_6
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