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The proficiencies of Fisher discriminant analysis (FDA), support vector machines (SVM), and proximal support vector machines (PSVM) for fault diagnosis (i.e. classification of multiple fault classes) are investigated. The Tennessee Eastman process (TEP) simulator was used to generate overlapping datasets to evaluate the classification performance. When all variables were used, the datasets were masked with irrelevant information, which resulted in poor classification. With key variables selected by genetic algorithms and the contribution charts, SVM and PSVM outperformed FDA and demonstrated the advantage of using nonlinear technique when data are overlapped. The overall misclassification for the testing data using FDA dropped from 38 to 18%; while those using SVM and PSVM dropped from 44–45 to 6%. The effectiveness of the proposed approach is increased in PSVM by saving significant computation time and memory requirement, while obtaining comparable classification results. For auto-correlated data, the incorporation of time lags into SVM and PSVM improved classification results. The added dimensions decreased the degree to which the data overlap and the overall misclassification for the testing set using SVM and PSVM decreased further to 3%. [Copyright &y& Elsevier]

Recent developments in deep learning have brought huge breakthroughs in the image processing area, which triggered numerous successful applications and positively impacted the current big data context of Industry 4.0. On the other hand, it is widely known that large amounts of training data are required to train a deep learning model with millions of parameters, which limits its application in many industrial applications where sufficient data resources are lacking. Transfer learning is one of the practical solutions to reduce the data required for training, which tries to reuse learned knowledge for similar tasks. Nevertheless, many technical details of transfer learning implementation are not well documented. Therefore, in this work, two datasets collected from plastics manufacturing processes were studied to investigate different transfer learning approaches and implementation details for high-performance model building under the constraint of limited available training data. Different transfer learning implementations are compared and important technical details are also discussed in this study. Through this study, the minimum number of training samples can be estimated. Transfer learning is compared with the newly developed few-shot learning approach as a brief comparative study. Finally, this work summarizes practical guidelines for the development of image classification models with limited data resources. • Transfer learning techniques were investigated in this work on industrial datasets. • Implementation details of transfer learning in deep convolutional neural networks including fine-tuning level, model selection etc. were discussed. • The minimum training data size required for image classification was estimated using the transfer learning approach. • A comparison with few-shot learning was studied to evaluate the feasibility of different approaches. • Practical guidelines were summarized for using transfer learning to build image classification models with limited training data. [ABSTRACT FROM AUTHOR]

This paper explores dimensionality reduction (DR) approaches for visualizing high dimensional data in chemical processes. Visualization provides powerful insight and process understanding in the industrial context, and accelerates process troubleshooting. A diverse array of existing, easy-to-use DR methods are evaluated in three case studies on large-scale industrial manufacturing plants. Supervised and unsupervised cases are presented with the objective of solving typical industrial problems related to unplanned events, plant performance improvement, and quality underperformance troubleshooting. For the unsupervised case, the evaluation aims to identify approaches that provide insight beyond those of PCA (Principal Component Analysis), and also examines quality metrics of the reduced (latent) space which characterize the degree of trust in the DR. UMAP (Uniform Manifold Approximation and Projection) outperforms other techniques, bringing new insights when comparing with other methods. For the supervised case, UMAP is combined with traditional variable selection methods, such as VIP (Variable Influence on Projection) weights from PLS-DA (Partial Least Squares Discriminant Analysis), in order to improve latent space visualization by increasing separation between classes. • Dimensionality reduction techniques are applied to three industrial case studies. • UMAP has outperformed other methods that solve unsupervised problems. • Combining UMAP with feature selection techniques improves latent space visualization. [ABSTRACT FROM AUTHOR]