市場前線｜全廠擾動源追溯技術的研究與創新應用

全廠擾動（plant-wide disturbances）是工業製程中的常見問題，可能導致效能降低、產品品質下降及能源消耗增加，甚至中斷生產。快速檢測與診斷擾動源以進行維護至關重要。雖然現有擾動源追溯技術具一定準確性，但因方法限制，難以應對複雜工廠環境，且在實際應用中缺乏整合性。本研究提出結合多種擾動根源拓樸演算法的架構，先對問題迴路群應用多種方法生成拓樸資訊，再透過馬可夫鏈資訊融合技術整合結果，生成更準確的整體拓樸資訊，提升追溯可靠度與準確度。本方法能高效定位擾動源，為工廠穩定運行及效能優化提供高效且實用的解決方案。

Plant-wide disturbances are common challenges in industrial processes, often leading to decreased efficiency, reduced product quality, increased energy consumption, and even production interruptions. Rapid detection and diagnosis of disturbance sources are essential for effective maintenance. Although existing disturbance tracing techniques achieve a certain level of accuracy, they face limitations in handling the complexity of industrial environments and often lack integration in practical applications. This study proposes a framework that integrates multiple disturbance root cause topology algorithms. Initially, various methods are applied to generate topology information for problematic loop groups. Subsequently, Markov chain-based information fusion techniques are employed to combine the results into a more accurate and comprehensive overall topology structure. This approach significantly enhances the reliability and accuracy of disturbance tracing, enabling efficient localization of disturbance sources. The proposed method offers a practical and effective solution to ensure operational stability and improve efficiency in industrial plants.