Enhancing fault detection and diagnosis systems for a chemical process: a study on convolutional neural networks and transfer learning

Industrial chemical processes are characterized by highly non-linear dynamics, multivariable interactions, and complex transient behaviors, making early fault detection and diagnosis (FDD) critical for operational safety and efficiency. Traditional data-driven FDD methods often struggle with high-dimensional process data and require extensive labeled datasets for training deep learning models from scratch. To address these limitations, this research direction explores the application of convolutional neural networks (CNNs) integrated with transfer learning paradigms for chemical process monitoring. By transforming multivariate time-series process variables into 2D spatial representations or utilizing 1D temporal convolutions, CNNs can effectively extract spatial-temporal features indicative of process anomalies. Furthermore, transfer learning is leveraged to transfer knowledge from pre-trained models

or source domains with abundant data to target process conditions with limited labeled fault samples. This methodology-oriented framework provides structured guidance for evaluating model performance across diverse fault scenarios, such as sensor biases, valve sticking, and feed variations. The proposed implementation support focuses on evaluating diagnostic accuracy, computational efficiency, and generalization capabilities under varying noise levels, establishing a robust foundation for advanced process safety management and automated anomaly classification in complex chemical plants.

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