Abstract
This paper presents an approach by combining robust fuzzy principal component analysis (RFPCA) technique with the multiscale principal component analysis (MSPCA) methodology. Thus the two typical issues of industrial data, outliers and changing process conditions are solved by resulting MS-RFPCA methodology. The RFPCA is proved to be effective in mitigating the impact of noise, and MSPCA has become necessary due to the nature of complex systems in which operations occur at different scales. The efficiency of the proposed technique is illustrated on a simulated benchmark of biological nitrogen removal process.
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Heloulou, N., Ramdani, M. (2014). Robust Statistical Process Monitoring for Biological Nutrient Removal Plants. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 442. Springer, Cham. https://doi.org/10.1007/978-3-319-08795-5_44
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DOI: https://doi.org/10.1007/978-3-319-08795-5_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08794-8
Online ISBN: 978-3-319-08795-5
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