Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques
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Tipus de documentArticle
Data publicació2012-06
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Abstract
This paper investigates fault diagnosis in batch
processes and presents a comparative study of feature
extraction and classification techniques applied to a specific
biotechnological case study: the fermentation process
model by Birol et al. (Comput Chem Eng 26:1553–1565,
2002), which is a benchmark for advanced batch processes
monitoring, diagnosis and control. Fault diagnosis is
achieved using four approaches on four different process
scenarios based on the different levels of noise so as to
evaluate their effects on the performance. Each approach
combines a feature extraction method, either multi-way
principal component analysis (MPCA) or multi-way independent
component analysis (MICA), with a classification
method, either artificial neural network (ANN) or support
vector machines (SVM). The performance obtained by the
different approaches is assessed and discussed for a set of
simulated faults under different scenarios. One of the faults
(a loss in mixing power) could not be detected due to the
minimal effect of mixing on the simulated data. The
remaining faults could be easily diagnosed and the subsequent
discussion provides practical insight into the
selection and use of the available techniques to specific
applications. Irrespective of the classification algorithm,
MPCA renders better results than MICA, hence the diagnosis
performance proves to be more sensitive
CitacióMonroy, I. [et al.]. Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques. "Bioprocess and biosystems engineering", Juny 2012, vol. 35, núm. 5, p. 689-704.
ISSN1615-7591
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