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Distance Matrix Patterns for Visual and Interpretable Process Data Analytics
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AI
DOI：10.1021/acs.iecr.3c01481.png)
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Abstract
En 中文
A novel methodology for visual process data analytics based on distance matrices is proposed. A distance matrix is a two-dimensional representation that reflects intrinsic data patterns and is not constrained by a specific model structure. It is shown for the first time that fundamental patterns of process data, such as steps, drifts, and oscillations, can be clearly identified in distance matrices, allowing their use in process monitoring pipelines and as tools to aid process understanding and interpretation. The proposed approach has been applied to the context of visual exploratory data analysis and the formulation of an adaptive fault detection algorithm based on matrix dissimilarity. The effectiveness of the proposed methodology is illustrated through simulation cases, which involve a CSTR reactor and the Tennessee Eastman Process benchmark.
Keywords:
MULTIDIMENSIONAL VISUALIZATION
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3.9
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4.0W
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9.6W
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