复杂工业过程中贝叶斯方法与LDA特征提取的联合故障诊断

Wenbing Zhu, Guangzao Huang, Jinting Guan, Guoli Ji, Sun Zhou
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引用次数: 2

摘要

贝叶斯方法是一类数据驱动的故障诊断方法,是一个具有重要实用价值的课题。为了提高诊断准确率和减少计算量,在进行贝叶斯诊断之前,采用线性判别分析(LDA)提取特征。它可以最大化显式函数,以达到类内数据点尽可能接近,类间数据点尽可能远离的目的。利用田纳西伊士曼挑战(Tennessee Eastman Challenge, TE)来验证所提出方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault diagnosis of joint Bayesian method and LDA feature extraction in complicated industrial process
Bayesian method is a class of data-driven fault diagnosis method which is a topic of significant practical interest. In order to improve diagnosis accuracy and reduce computation load, linear discriminant analysis (LDA) is employed to extract features before performing Bayesian diagnosis. It can maximize the explicit function to achieve the goal that within-class data points as close as possible and between-class data points as far as possible. Tennessee Eastman Challenge (TE) is utilized to verify the effectiveness of the proposed method.
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