Decentralized Fault Diagnosis of Large-scale Processes Using Multiblock Kernel Principal Component Analysis
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摘要: 提出多块核主元分析算法, 基于此算法针对复杂过程提出了新的故障检测和诊断方法. 通过对整体过程分块统计残差实现非线性过程的分散故障诊断目的, 相应的控制限用来分离引起故障的位置或发现引起故障的变量. 提出的方法应用到田纳西过程得出的结论为: 该方法能够有效地提取块内和块间的非线性信息并显示出优越的故障诊断能力.Abstract: In this paper, a multiblock kernel principal component analysis (MBKPCA) algorithm is proposed. Based on MBKPCA, a new fault detection and diagnosis approach is proposed to monitor large-scale processes. In particular, definitions of nonlinear block contributions to T2 and the squared prediction error (SPE) statistics are first proposed in order to diagnose nonlinear faults. In addition, the relative contribution, which is the ratio of the contribution to the corresponding upper control limit, is considered to find process variables or blocks responsible for faults. The proposed method is applied to fault detection and diagnosis in the Tennessee Eastman process. The proposed decentralized nonlinear approach effectively captures the nonlinear relationship in the block process variables and shows superior fault diagnosis ability compared with other methods.
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