Abstract
Multivariate statistical principles are introduced in this chapter as a basis for data-driven method for fault detection and isolation. Thus, Principal Component Analysis (PCA) properties for data projection and dimensionality reduction are exploited to model process behaviour based on historical data representing normal operating conditions. After formulating the PCA model in terms of projection and residual spaces, the method introduces the distance concept in both subspaces aiming to define fault detection criteria. Two statistics, Hotelling T\(^{2}\) and the square prediction error (SPE), are used with this purpose. Diagnosis functionalities are provided by the capability to describe the magnitude of both statistics in terms of contributions of the original variables. The chapter ends with an illustrative example of the method.
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Acknowledgements
This work has been developed within the eXiT (https://exit.udg.edu) research group (2017 SGR 1551) and supported by the CROWDSAVING project (Ref. TIN2016-79726-C2-2-R), funded by the Spanish Ministerio de Industria y Competitividad within the Research, Development and Innovation Program oriented toward the Societal Challenges.
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Melendez i Frigola, J. (2019). Data-Driven Fault Diagnosis: Multivariate Statistical Approach. In: Escobet, T., Bregon, A., Pulido, B., Puig, V. (eds) Fault Diagnosis of Dynamic Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-17728-7_8
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DOI: https://doi.org/10.1007/978-3-030-17728-7_8
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