Equipment failure Bayesian network prediction method based on K2 algorithm

Abstract

The invention discloses an equipment failure Bayesian network prediction method based on a K2 algorithm, and is used for solving the technical problem of low searching efficiency of a conventional equipment failure Bayesian network prediction method. The equipment failure Bayesian network prediction method has the technical scheme that an FPBN (Failure Prediction Bayesian Network) structure learning algorithm based on a K2 searching algorithm is adopted for building an FPBN structure capable of really reflecting each variable incidence relation in a failure data set, so that an FPBN model is built. Finally, the actual operation state of equipment is predicted by uitlizing a parameter learning algorithm on the basis of a built failure prediction model. The method uses the K2 searching algorithm as the basis; the failure knowledge, the expertise and the failure data are effectively fused; and the problem of modeling difficulty in system to FPBN conversion in the equipment prediction process is solved. In addition, the FPBN-K2 algorithm calculation process totally adopts deterministic searching algorithms, and repeated searching for many times is not needed; the searching space is reduced; the number of scoring function calculation times is reduced; and the searching efficiency of the FPBN structure learning algorithm is improved.

Description

technical field [0001] The invention relates to a Bayesian network prediction method for equipment failure, in particular to a Bayesian network prediction method for equipment failure based on the K2 algorithm. Background technique [0002] Literature "Cai Z, Sun S, Si S, et al.Research of failure prediction Bayesian network model[C] / / Industrial Engineering and Engineering Management, 2009.IE&EM'09.16th International Conference on.IEEE,2009:2021-2025. "disclosed a failure prediction Bayesian network method (failure prediction Bayesian network, FPBN). This method first defines a failure prediction Bayesian network model (failure prediction Bayesian network, FPBN), and then transforms the fault system to be predicted one by one according to the definition of FPBN, and finally uses FPBN to calculate the system in each fault mode probabilities for fault prediction. The method introduces the fault detection information into the fault prediction process, and the effectiveness of...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the low search efficiency of the existing Bayesian network prediction method for equipment failure, the present invention provides a Bayesian network prediction method for equipment failure based on the K2 algorithm

Based on the K2 search algorithm, this method effectively integrates fault knowledge, expert experience and fault data, which can solve the problem of difficult modeling from system to FPBN in the process of equipment prediction

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