Author details

In addition, the following general observations can be made not only with regard to the approach considered in this case study but also to other approaches reviewed

• Most of the nonlinear approaches used in steady state systems can be used in dynamic systems, and as a consequence, principal and independent component analysis and kernel methods have figured strongly in recent advances in

• With the routine acquisition of ever larger volumes of data and more complex processing, it can be expected that the field will continue to benefit from advances in machine learning. The application of deep learning methods in

• Likewise, dynamic process monitoring is also likely to continue to benefit from closely related fields, such as process condition monitoring, structural health monitoring, change point detection, and novelty detection in other engineering

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

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

Tennessee Eastman benchmark problem in chemical engineering.

The author declares no conflict of interest in this contribution.

the recognition of flotation froth textures, for example.

Conflict of interest

16

• As with steady state process monitoring, fault identification has received

particular is a highly promising emerging area of research.

in this chapter.

dynamic process monitoring.

Time Series Analysis - Data, Methods, and Applications

or technical systems.

5. Conclusions and future work

comparatively little attention to date.

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

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

© 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, provided the original work is properly cited.
