Abstract
Condition based maintenance (CBM) is becoming more and more popular in equipment maintenance. A prerequisite to widespread deployment of CBM technology and practice in industry is effective diagnostics and prognostics. A dynamic Bayesian network (DBN) based prognosis method was investigated to predict the remaining useful life (RUL) for an equipment. First, a DBN based prognosis framework and specific steps for building a DBN based prognosis model were presented. Then, the corresponding inference algorithms for DBN based prognosis were provided. Finally, a prognosis procedure based on particle filtering algorithms was used to predict the RUL of drill-bits of a vertical drilling machine, which is commonly used in industrial process. Preliminary experimental results are promising.
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Foundation item: the Shanghai Pujiang Program (No. 05PJ14067)
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Dong, M., Yang, Zb. Dynamic Bayesian network based prognosis in machining processes. J. Shanghai Jiaotong Univ. (Sci.) 13, 318–322 (2008). https://doi.org/10.1007/s12204-008-0318-y
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DOI: https://doi.org/10.1007/s12204-008-0318-y