Abstract
Make expert knowledge and experience to record a large number of diagnostic reports as research data, use Bayesian machine learning method to compute and find out the current status of mechanical device, which best matches the description of diagnostic suggestions and for experts to provide decision support. Exercise natural language processing methods to initialize the text, then Naive Bayesian methods is calculated the similarity with text of the device state description and diagnostic reports, thus draw the best device diagnostic suggestion to help expert decide. By using the Java language platform did simulation experiments of the algorithm, the final output fairly validate this approach based on similarity analysis, which can draw the best diagnostic recommendations.
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© 2013 Springer-Verlag London
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Jia, X., Li, N. (2013). A Device Diagnosis Algorithm Based on Naive Bayesian. In: Du, W. (eds) Informatics and Management Science I. Lecture Notes in Electrical Engineering, vol 204. Springer, London. https://doi.org/10.1007/978-1-4471-4802-9_17
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DOI: https://doi.org/10.1007/978-1-4471-4802-9_17
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