[1]郑静,熊伟丽.基于互信息的多块k近邻故障监测及诊断[J].智能系统学报,2021,16(4):717-728.[doi:10.11992/tis.202007035]
ZHENG Jing,XIONG Weili.Multiblock k-nearest neighbor fault monitoring and diagnosis based on mutual information[J].CAAI Transactions on Intelligent Systems,2021,16(4):717-728.[doi:10.11992/tis.202007035]
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ZHENG Jing,XIONG Weili.Multiblock k-nearest neighbor fault monitoring and diagnosis based on mutual information[J].CAAI Transactions on Intelligent Systems,2021,16(4):717-728.[doi:10.11992/tis.202007035]
基于互信息的多块k近邻故障监测及诊断
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第4期
页码:
717-728
栏目:
学术论文—智能系统
出版日期:
2021-07-05
- Title:
- Multiblock k-nearest neighbor fault monitoring and diagnosis based on mutual information
- Keywords:
- mutual information; multi-block modeling; k-nearest neighbor; process monitoring; fault detection; Bayesian inference; fault diagnosis; Mahalanobis distance
- 分类号:
- TP277
- 摘要:
- 由于传统的k近邻故障监测不考虑过程的局部信息，只建立一个全局模型，因此提出一种基于互信息的多块k近邻故障监测方法。首先，考虑建模数据的非线性和非高斯等特性，基于变量间的互信息进行子块构建；然后，利用k近邻方法对每个子块进行建模与监测，子块中的k近邻模型反映了更多的过程局部特征；最后，将所有子块的监测结果通过贝叶斯推断方法进行融合，并采用基于马氏距离的故障诊断方法辨识故障源。通过对田纳西-伊斯曼过程和高炉炼铁过程中的应用仿真，监测结果表明所提方法的可行性和有效性。
- Abstract:
- The traditional k-nearest neighbor (kNN) fault monitoring does not take into account the process of local information and only builds a global model. Thus, a multi-block kNN fault monitoring algorithm based on mutual information is proposed. First, with the nonlinear and non-Gaussian characteristics of the modeled data taken into consideration, subblocks are constructed based on mutual information between variables. Then, the kNN algorithm is used to model and monitor each subblock, in which the kNN model reflects more local characteristics of the process. Lastly, the monitoring results of all subblocks are fused by the Bayesian inference method, and a fault diagnosis method based on Mahalanobis distance is used to identify the source of faults. Through the application simulation in the Tennessee Eastman process and the blast furnace ironmaking process, the monitoring results show the feasibility and effectiveness of the proposed method.
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备注/Memo
收稿日期:2020-07-22。
基金项目:国家自然科学基金项目(61773182)；国家重点研发计划子课题(2018YFC1603705-03)
作者简介:郑静，硕士研究生，主要研究方向为过程故障检测;熊伟丽，教授，博士生导师，主要研究方向为复杂工业过程建模与监控、智能软测量技术。主持国家自然科学基金面上项目、国家自然科学基金青年项目、江苏省产学研等省部级以上纵向项目等，获授权发明专利 近20项。发表学术论文近百篇.
通讯作者:熊伟丽.E-mail:greenpre@163.com
更新日期/Last Update:
1900-01-01