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Abstract: Dow Chemical is committed to product quality consistency. To achieve this goal, a project on implementing multivariate quality control was launched. Robust principal component analysis (PCA) was applied to a historical data set of product quality to remove outliers. The remaining data, representing normal process variation, were used to build a PCA model. One advantage of multivariate analysis is that PCA can detect a change in variable correlation, which is undetectable using univariate control charts. The T 2 statistic is used for fault detection and the contribution chart is used for fault identification. The model has been implemented on-line and proven to be effective in detecting and identifying abnormal product lots and analytical issues. Practical issues such as long-term model performance, model maintenance, and model transfer are discussed in this paper. [Copyright &y& Elsevier]

Abstract: The data collected from a batch process over time from multiple sensors can be arranged in a matrix of J-variables× K-time points. Data collected on multiple batches can be arranged in a cube of I-batches× J-variables× K-time points. The analysis of a cube of data can be performed by unfolding in two different ways, batch unfolding giving an I × JK data matrix or observation unfolding resulting in an IK × J data matrix, followed by PCA. The data can also be analyzed directly using three-way methods such as PARAFAC or Tucker3. In the literature there is no clear agreement as to the most effective approach for the analysis of batch data. This paper makes detailed comparisons between the two unfolding approaches and the Tucker3 method. Batch data from a fermentation process at The Dow Chemical Company San Diego facility is used for this study. The three methods were found to be complementary to each other and a well-trained chemometrician/practitioner will find all three methods to be useful for batch data analysis. The batch unfolding MPCA is more sensitive to the overall batch variation while the observation unfolding MPLS is more sensitive to the localized batch variation. The Tucker3 method is in good balance in terms of detecting both variations. [Copyright &y& Elsevier]

Data-driven techniques based on multivariate statistics (such as principal component analysis (PCA) and partial least squares (PLS)) have been applied widely to chemical processes and their effectiveness for fault detection is well recognized. There is an inherent limitation on the ability for purely data-driven techniques to identify and diagnose faults, especially when the abnormal situations are associated with unknown faults or multiple faults. The modified distance (DI) and modified causal dependency (CD) are proposed to incorporate the causal map with data-driven approach to improve the proficiency for identifying and diagnosing faults. The DI is based on the Kullback–Leibner information distance (KLID), the mean of the measured variables, and the range of the measured variable. The DI is used to measure the similarity of the measured variable between the current operating conditions and the historical operating conditions. When the DI is larger than the predefined threshold, the variable is identified as abnormal. The CD, derived based on the multivariate T2 statistic, is used to measure the causal dependency of two variables. When CD is larger than the predefined threshold, the causal dependency of the two variables is broken. The proposed method requires a causal map and historical data associated with the normal operating conditions. A causal map containing the causal relationship between all of the measured variables can be derived based on knowledge from a plant engineer and the sample covariance matrix from the normal data. The DI/CD algorithm outperformed the purely data-driven techniques such as PCA for detecting and identifying known, unknown, and multiple faults using the data sets from the Tennessee Eastman process (TEP). [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]

Manufacturing analytics is of paramount importance in many plants today, and its relevance increases in the current big data context of Industry 4.0. The fields of statistics, chemometrics, and machine learning are expected to provide tools that effectively handle many of the characteristics of industrial data. In this paper, the task of image-based product classification is considered. This is a supervised learning problem where the input is an image and the output is a unique label attributed to the image from a finite set of labels corresponding to the available product classes. This is a prevalent and highly relevant industrial challenge and recent developments in deep learning have proven to be successful in increasing the image classification accuracy, providing state-of-the-art results. Thus, in this work, we leverage deep neural networks' (DNN) ability to automatically learn features from images and test their performance in a real industrial context for predicting the pellet shape. In order to accelerate the training of DNN, transfer learning is employed and a network previously developed for one task is adapted to predict pellet shape. Furthermore, other less complex techniques such as partial least squares discriminant analysis (PLS-DA) and random forests (RF) are also explored in order to assess the benefits of adopting DNN as opposed to current classifiers. An industrial image classification case study was utilized to compare PLS-DA, RF, and DNN models. Compared to the in situ classification system currently in use, increasingly complex models (PLS-DA and RF) were able to better utilize the same pre-defined features and improve prediction accuracy significantly. DNN obtained the highest accuracy on the independent test set, with the advantages of not requiring the a priori computation of image features since they are directly extracted from the raw images. Moreover, by visualizing the output of some layers of the DNN, it is possible to verify that activations occurred in regions that are indeed meaningful for the classification tasks, further supporting that DNN were effectively modelling the relevant features of the pellet. [ABSTRACT FROM AUTHOR]