A novel dynamic machine learning-based eXplainable fusion monitoring: Application to industrial and chemical processes

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Abstract The complexity and fusion dynamism of the modern industrial and chemical sectors have been increasing with the rapid progress of IR 4.0 to 5.0. The transformative characteristics of Industry 4.0 to 5.0 have not been fully explored in terms of the fundamental importance of explainability. Traditional monitoring techniques for automatic anomaly detection, identifying the potential variables, and root cause analysis for fault information are not intelligent enough to tackle the intricate problems of real-time practices in the industrial and chemical sectors. This study presents a novel dynamic Machine Learning (ML) based eXplainable fusion approach to address the issues of process monitoring in industrial and chemical process systems. The methodology aims to detect faults, identify their key causes and feature variables, and analyze the root path of fault propagation with the time and magnitude of one cause variable to another impact. This study proposed using a time domain multivariate granger-entropy-aided dynamic independent component analysis (ICA) - distributed canonical correlation analysis (CCA) approach, incorporating the dynamics time wrapping supported time delay-signed directed graph. The proposed methodology utilized the application to industrial and chemical processes and verified using the Continuous Stirred Tank Reactor (CSTR) and Tennessee Eastman Process (TEP) as practical application benchmarks. The framework's validations and efficiency are evaluated using established techniques such as classic computed ICA and Dynamic ICA as standard model scenarios. The outcomes and results showed that the newly developed strategy is preferable to previous approaches regarding explainability and robust detection and identification of the actual root causes with high FDRs and low FARs.