Abstract
IEEE Standard 1232 provides the D-matrix for diagnosing quality in models. The framework give the ability to detect dependency in relation to symptoms and failure modes [1]. This paper describes an approach to construct D-matrix by mining unstructured repair verbatim text. At first d-matrix is constructed for different dataset, and then we can form a combined d-matrix from different dataset to identify common patterns in it. In this proposed method training is performed by using different classification methods on unstructured verbatim (Combined D-Matrix) collected from the medical domain.
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References
Rajpathak, D.G., Singh, S.: An ontology-based text mining method to develop D-matrix from unstructured text. IEEE Trans. Syst. Man Cybern. Syst. 44(7), 966–977 (2014)
Sheppard, J., Kaufman, M., Wilmering, T.: Model based standards for diagnostic and maintenance information integration. In: Proceedings of IEEE Autitestcon Conference, pp. 304–310 (2012)
Singh, S., Holland, S.W., Bandyopadhyay, P.: Trends in the development of system-level fault dependency matrices. In: Proceedings of IEEE Aerospace Conference, pp. 1–9 (2010)
Sheppard, J.W., Butcher, S.G.W.: A formal analysis of fault diagnosis with D-matrices. J. Electron. Test. Theory Appl. 23, 309–322 (2007). Springer Science
Deb, S., Pattipati, S.K., Raghavan, V., Shakeri, M., Shrestha, R.: Multi-signal flow graphs: a novel approach for system testability analysis and fault diagnosis. IEEE Aerosp. Electron. Syst. 10(5), 14–25 (1995)
Gaeta, M., Orciuoli, F., Paolozzi, S., Salerno, S.: Ontology extraction for knowledge reuse: the e-learning perspective. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41(4), 798–809 (2011)
Strasser, S., Sheppard, J., Schuh, M., Angryk, R., Izurieta, C.: Graph based ontology-guided data mining for D-matrix model maturation. In: Proceedings of IEEE Aerospace Conference, pp. 1–12 (2011)
Zhong, N., Li, Y., Wu, S.-T.: Effective pattern discovery for text mining. IEEE Trans. Knowl. Data Eng. 24(1), 30–44 (2012)
Thombare, T.R., Dole, L.: D-matrix: fault diagnosis framework. Int. J. Innovative Res. Comput. Commun. Eng. 3(3) (2015)
Kulkarni, A., Nighot, J.: Text mining method to develop D-matrix for fault diagnosis. Int. J. Recent Innovation Trends Comput. Commun. 4(3) (2016)
Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Pearson Education
Acknowledgment
Amruta Kulkarni would like to thank to her guide Asst. Prof. Jyoti Nighot for her guidance and instructive comments on this paperwork. The authors would like to offer regards to all of those who supported in any respect during the completion of this paper.
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Kulkarni, A., Nighot, J., Ramdasi, A. (2016). Text Mining Methodology to Build Dependency Matrix from Unstructured Text to Perform Fault Diagnosis. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_64
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DOI: https://doi.org/10.1007/978-981-10-3433-6_64
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