The Development of a Fault Diagnosis Model based on the Parameter Estimations of Partial Least Square Models 


Vol. 34,  No. 4, pp. 59-67, Aug.  2019
10.14346/JKOSOS.2019.34.4.59


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  Abstract

Since it is really hard to construct process models based on prior process knowledges, various statistical approaches have been employed to build fault diagnosis models. However, the crucial drawback of these approaches is that the solutions may vary according to the fault magnitude, even if the same fault occurs. In this study, the parameter monitoring approach is suggested. When a fault occurs in a chemical process, this leads to trigger the change of a process model and the monitoring parameters of process models is able to provide the efficient fault diagnosis model. A few important variables are selected and their predictive models are constructed by partial least square (PLS) method. The Euclidean norms of parameters of PLS models are estimated and a fault diagnosis can be performed as comparing with parameters of PLS models based on normal operational conditions. To improve the monitoring performance, cumulative summation (CUSUM) control chart is employed and the changes of model parameters are recorded to identify the type of an unknown fault. To verify the efficacy of the proposed model, Tennessee Eastman (TE) process is tested and this model can be easily applied to other complex processes.

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  Cite this article

[IEEE Style]

이광오 and 이창준, "The Development of a Fault Diagnosis Model based on the Parameter Estimations of Partial Least Square Models," Journal of the Korean Society of Safety, vol. 34, no. 4, pp. 59-67, 2019. DOI: 10.14346/JKOSOS.2019.34.4.59.

[ACM Style]

이광오 and 이창준. 2019. The Development of a Fault Diagnosis Model based on the Parameter Estimations of Partial Least Square Models. Journal of the Korean Society of Safety, 34, 4, (2019), 59-67. DOI: 10.14346/JKOSOS.2019.34.4.59.

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