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Outlier-aware unified adaptive-prior graph projection for robust process monitoring
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DOI：10.1016/j.psep.2026.108942.png)
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Abstract
En 中文
• Proposes a robust fault detection method under outlier contamination. • Decomposes corrupted data into clean data and outliers for robust projection. • Preserves sparse reconstruction to enhance adaptive low-dimensional learning. • Embeds geometry prior graphs to capture local and global data structures.
Keywords:
robust fault detection
outlier contamination
adaptive-prior graph
process monitoring
sparse reconstruction
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