Browsing by Author "Theunissen, Carl Daniel"

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    Fault pattern recognition in simulated furnace data
    (Stellenbosch : Stellenbosch University, 2021-03) Theunissen, Carl Daniel; Louw, Tobias M.; Bradshaw, S. M.; Auret, Lidia; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: Modern submerged arc furnaces are plagued by blowbacks; hazardous occurrences where hot, toxic furnace freeboard gases are blown into the environment. While common occurrences, their causes are currently unknown, hence they cannot be predicted with mechanistic models. Data-driven models use data recorded from modern processes, like submerged arc furnaces, to recognize specific process conditions. This project aimed to identify and compare fault pattern recognition models that could be used for detecting and recognizing blowback-preceding conditions. A simple submerged arc furnace model that emulates blowbacks was developed with which to generate large volumes of data for model comparison. This submerged arc furnace model was developed from mass- and energy balances over distinct furnace zones, and yielded a large dataset with dynamic- and nonlinear characteristics. This dataset contained observations from multiple distinct operating modes, and was deemed suitable for fault pattern recognition model evaluation. A semi-supervised learning approach was selected as most suitable for recognizing blowback preceding conditions. Semi-supervised fault pattern recognition models are trained on a set of only blowback-preceding observations; this fits the typical constraints imposed by industrial datasets, where data is poorly defined and only a few observation of the target fault are labelled as such. Principal component analysis (PCA), kernel PCA and input-reconstructing neural networks called auto-encoders are established semi-supervised pattern recognition methods. One-dimensional convolutional auto-encoders are neural network architectures that effectively compress multivariate time series, but their application to on-line fault pattern recognition is relatively novel. This work applied these methods to on-line fault pattern recognition for blowback prediction, and presented algorithms for applying these methods for semi-supervised fault pattern recognition tasks. Feature engineering has the largest impact on fault pattern recognition performance, therefore feature engineering techniques were applied as part of an overall approach to data-driven fault pattern recognition. The investigation into the above fault pattern recognition models showed that kernel PCA’s superior performance over standard PCA is limited to smaller datasets, and that large datasets must be compressed significantly before kernel PCA can be applied. Consequently this investigation found linear PCA to be superior to nonlinear kernel PCA for modelling large datasets. Both auto-encoders and the developed convolutional auto-encoders outperformed linear PCA modelling, highlighting the improved fault pattern recognition capabilities of nonlinear models. This investigation found that one-dimensional convolutional auto-encoders were far more effective than the other presented models when applied to raw multivariate time series data, confirming that one-dimensional convolutional auto-encoders are effective at processing time series. However, the best performance was observed for auto-encoders models when applied to feature engineered data. This highlighted the guiding role that feature engineering should have in developing and implementing fault pattern recognition models.