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Fault Prediction Algorithm for Multiple Mode Process Based on Reconstruction Technique

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  • Jie Ma
  • Jianan Xu

Abstract

In the framework of fault reconstruction technique, this paper studies the problems of multiple mode process fault detection, fault estimation, and fault prediction systematically based on multi-PCA model. First, a multi-PCA model is used for fault detection in steady state process under different conditions, while a weighted algorithm is applied to transition process. Then, describe the faults quantitatively and use the optimization method to derive the fault amplitude under the sense of fault reconstruction. Fault amplitude drifts under different conditions even if the same fault occurs. To solve the above problem, consistent estimation algorithm of fault amplitude under different conditions has been studied. Last, employ the support vector machine (SVM) to predict the trend of the fault amplitude. Effectiveness of the algorithms proposed in this paper has been verified using Tennessee Eastman process as the study object.

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  • Jie Ma & Jianan Xu, 2015. "Fault Prediction Algorithm for Multiple Mode Process Based on Reconstruction Technique," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-8, June.
    • Handle: RePEc:hin:jnlmpe:348729
    DOI: 10.1155/2015/348729
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    Cited by:

    1. Soleimani, Morteza & Campean, Felician & Neagu, Daniel, 2021. "Integration of Hidden Markov Modelling and Bayesian Network for fault detection and prediction of complex engineered systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).

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