Abstract:

A new method of double-level local kernel principal component analysis (DLKPCA) was proposed to overcome challenges that traditional kernel principal component analysis (KPCA) method has encountered in incipient fault detection of nonlinear process. In this method, local information was obtained from data in variable-wise and sample-wise viewpoints to improve fault detection performance. First, all process variables were diced into several local variable blocks according to similarity of mutual information correlation among different variables and kernel principal components. Then, residual function was constructed from score vector and eigenvalue to mine local sample-wise information. Finally, the Bayesian fusion strategy was used to integrate results of each block. Simulation results on Tennessee Eastman standard process show that the proposed method can effectively detect incipient faults and has better fault detection performance than traditional KPCA method.

Key words: incipient faults detection, kernel principal component analysis, local information, variable block, Bayesian fusion strategy

摘要：

针对传统核主元分析（KPCA）方法难以有效检测微小故障的问题，提出一种基于双层局部核主元分析（double-level local kernel principal component analysis，DLKPCA）的非线性过程微小故障检测方法。该方法从变量和样本两个角度来挖掘数据内部的局部信息，以提高故障检测能力。首先，利用变量分块思想，基于不同变量与核主元之间互信息相关度的相似性，将所有过程变量划分多个局部变量块。然后，构建基于得分向量和特征值的残差函数以挖掘样本局部信息。最后利用贝叶斯融合策略对各块的结果进行融合。在田纳西-伊斯曼基准过程的仿真结果表明，在微小故障检测方面，本文所提方法具有比传统KPCA方法更好的故障检测性能。

关键词: 微小故障检测, 核主元分析, 局部信息, 变量分块, 贝叶斯融合策略