CIESC Journal ›› 2010, Vol. 61 ›› Issue (8): 1894-1900.
Previous Articles Next Articles
ZHANG Shaojie;WANG Zhenlei;QIAN Feng
Online:
Published:
张少捷;王振雷;钱锋
Abstract:
Dozens of advantages have been reported on using support vector data description (SVDD) in the fields of non-Gaussian process monitoring and fault diagnosis.However, during the SVDD model construction offline, usually the whole training data set is used.Due to the tremendous size of the data set, the computation burden for modeling is quite heavy, which leads to the difficulty in updating model online.Therefore, this paper proposes a fast SVDD algorithm on the basis of the feature samples.In this new algorithm, the feature samples are used in stead of the whole training data set for modeling in order to significantly reduce the computation complexity.Concurrently, PCA is replaced by the local tangent space alignment (LTSA) to extract the underlying manifold structure of the process data set, since the traditional dimension reduction methods, such as PCA, have poor capability to handle nonlinearity.Next SVDD is applied on the manifold.At last, corresponding statistical indices are used for fault detection purpose.The proposed method has been tested on the Tennessee Eastman (TE) process, while the simulation results show the efficiency of it.
Key words: SVDD, 特征样本, LTSA, 过程监控
SVDD,
摘要:
基于支持向量数据描述(SVDD)方法的非高斯过程监控和故障诊断具有众多优点。然而在对SVDD离线建模时需要在整个训练样本集上操作,对大样本集计算量相当大,也不利于在线操作时模型的更新。对此提出一种基于特征样本的SVDD(FS-SVDD),采用特征样本提取方法用少数几个特征样本代替原始数据集进行训练,显著降低了建模复杂度。同时,针对传统的线性降维算法如主成分分析(PCA)存在的提取过程数据非线性结构能力不足的缺点,首先用局部切空间排列(LTSA)方法提取出低维子流形,进行有效的维数约减;接着在这个低维子流形上执行SVDD算法;最后,利用相应统计指标进行过程监控。在TE过程上的仿真表明上述方法的有效性。
关键词: SVDD, 特征样本, LTSA, 过程监控
ZHANG Shaojie, WANG Zhenlei, QIAN Feng. FS-SVDD based on LTSA and its application to chemical process monitoring[J]. CIESC Journal, 2010, 61(8): 1894-1900.
张少捷, 王振雷, 钱锋. 基于LTSA的FS-SVDD方法及其在化工过程监控中的应用 [J]. 化工学报, 2010, 61(8): 1894-1900.
0 / / Recommend
Add to citation manager EndNote|Ris|BibTeX
URL: https://hgxb.cip.com.cn/EN/
https://hgxb.cip.com.cn/EN/Y2010/V61/I8/1894
Fault identification of Tennessee Eastman process based on FS-KPCA