5. Conclusions and future work

Data-driven fault diagnosis of dynamic systems has advanced considerably over the last decade or more. In this chapter, the large variety of algorithms currently available has been discussed in terms of a feature extraction problem associated with the data captured by sliding a window across the time series or in some cases making use of a fixed window. These features could be used in statistical process monitoring frameworks that are well established for steady state systems.

In addition, extension of a recent approach to nonlinear time series analysis, namely recurrence quantification analysis, has been considered and shown to be an effective means of monitoring dynamic process systems, such as represented by the Tennessee Eastman benchmark problem in chemical engineering.

As mentioned in Section 4.4., a wide range of feature extraction algorithms can be used with unthresholded or global recurrence quantification analysis. In future work, the application of convolutional neural networks to extract features from global recurrence plots will be considered. This does not necessarily require a large amount of data, as pretrained networks, such as AlexNet, ResNet, and VGG architectures, and others could possibly be used as is, in what would essentially be a texture analysis problem, similar to the work done by Fu and Aldrich [103, 104] in the recognition of flotation froth textures, for example.

Author details

Western Australian School of Mines, Curtin University, Perth, WA, Australia

Process Fault Diagnosis for Continuous Dynamic Systems Over Multivariate Time Series

DOI: http://dx.doi.org/10.5772/intechopen.85456

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: chris.aldrich@curtin.edu.au

provided the original work is properly cited.

Chris Aldrich

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