COMPLICATED MECHANICAL EQUIPMENT DIAGNOSIS BASED ON BAYESIAN NETWORKS

Journal Title: Topics in Intelligent Computing and Industry Design (ICID) - Year 2017, Vol 1, Issue 2

Abstract

Mechanical equipment fault diagnosis is a complicated process. Due to the complex structure, the different operating environment, the different detection means and testing equipment, the difference between the operator and other factor, that well lead to many uncertainties. In order to solve these problems, this paper established a Bayesian Network-based mechanical equipment fault diagnosis model. The evaluation function and firefly algorithm are introduced to optimize the model. Introduce a priori knowledge to self-learning during model establishment, reduce the uncertainty caused by the test object information. Improve the reliability of mechanical equipment fault detection, finally verified by an example.

Authors and Affiliations

Chaoquan Chen, Xinrong Li, Xiaolan XIE

Keywords

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  • EP ID EP409824
  • DOI 10.26480/wsmce.01.2017.157.159
  • Views 71
  • Downloads 0

How To Cite

Chaoquan Chen, Xinrong Li, Xiaolan XIE (2017). COMPLICATED MECHANICAL EQUIPMENT DIAGNOSIS BASED ON BAYESIAN NETWORKS. Topics in Intelligent Computing and Industry Design (ICID), 1(2), 157-159. https://europub.co.uk/articles/-A-409824