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Monitoring of Nonlinear Time-Delay Processes Based on Adaptive Method and Moving Window

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  • Yunpeng Fan
  • Wei Zhang
  • Yingwei Zhang

Abstract

A new adaptive kernel principal component analysis (KPCA) algorithm for monitoring nonlinear time-delay process is proposed. The main contribution of the proposed algorithm is to combine adaptive KPCA with moving window principal component analysis (MWPCA) algorithm, and exponentially weighted principal component analysis (EWPCA) algorithm respectively. The new algorithm prejudges the new available sample with MKPCA method to decide whether the model is updated. Then update the KPCA model using EWKPCA method. And also extend MPCA and EWPCA from linear data space to nonlinear data space effectively. Monitoring experiment is performed using the proposed algorithm. The simulation results show that the proposed method is effective.

Suggested Citation

  • Yunpeng Fan & Wei Zhang & Yingwei Zhang, 2014. "Monitoring of Nonlinear Time-Delay Processes Based on Adaptive Method and Moving Window," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, July.
    • Handle: RePEc:hin:jnlmpe:546138
    DOI: 10.1155/2014/546138
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    Cited by:

    1. Melani, Arthur Henrique de Andrade & Michalski, Miguel Angelo de Carvalho & da Silva, Renan Favarão & de Souza, Gilberto Francisco Martha, 2021. "A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 215(C).

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