DOI: 10.3724/SP.J.1004.2010.00593

Acta Automatica Sinica (自动化学报) 2010/36:4 PP.593-597

Decentralized Fault Diagnosis of Large-scale Processes Using Multiblock Kernel Principal Component Analysis

In this paper, a multiblock kernel principal com-ponent analysis (MBKPCA) algorithm is proposed. Based on MBKPCA, a new fault detection and diagnosis approach is pro-posed to monitor large-scale processes. In particular, definitions of nonlinear block contributions to T2 and the squared predic-tion 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 responsi-ble for faults. The proposed method is applied to fault detection and diagnosis in the Tennessee Eastman process. The proposed decentralized nonlin-ear approach effectively captures the nonlinear relationship in the block process variables and shows superior fault diagnosis ability compared with other methods.

Key words:Multiblock kernel methods, nonlinear component analysis, process monitoring, fault detection, principal compo-nent analysis

ReleaseDate:2014-07-21 15:17:15

Funds:Supported by National Basic Research Program of China (973 Program) (2009CB320604) and the 111 Project

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