生產監控對於提升生產績效至關重要，透過即時數據的收集與分析，管理者能夠即時掌握生產狀況與趨勢，偵測潛在風險並迅速採取應對措施，以降低損失並確保生產流程的順暢。然而，傳統監控方式主要依賴人工經驗來診斷問題，當缺乏具備豐富經驗的專業人員時，異常處理的效率與效果將大幅下降，甚至可能導致問題無法解決或修復時間過長，進而影響整體生產效率與企業利益。
本研究的核心貢獻在於提出一套結合資料科學與人工智慧技術的智慧生產監控模式，整合當前生產監控的三大關鍵要素：即時異常檢測、準確的效能預測，以及根本原因分析，以期能迅速識別異常成因並持續優化生產流程。
本研究的方法論涵蓋以下幾個步驟：首先，融合多項資料科學概念，設計一套具備循環改善機制的智慧生產監控架構。其次，根據該架構進行具體設計，發展監控點的選擇與分析方法。接著，針對核心功能進行技術開發，包括監控點分析、標準設定、異常偵測與趨勢分析、預測預防與根本原因分析，並建立可解釋的因果分析模型。在技術選擇方面，透過比較多種適用模型，篩選出最佳效能者作為最終應用的方法。最後，透過公開的生產資料集進行模式實驗與驗證，以評估其有效性與實用性。
研究結果顯示，本模式能夠精確識別生產過程中的異常情況，準確預測生產效能異常，並提供具體的監控點特徵與成因分析，有助於管理者快速應對與解決生產異常問題。此模式為製造業的智慧化轉型提供了一套高效且可行的解決方案，提升企業的競爭力與生產效益。

Production monitoring is crucial for enhancing production performance. By collecting and analyzing real-time data, managers can immediately grasp production conditions and trends, identify potential risks, and take rapid countermeasures to minimize losses and ensure a smooth production process. However, traditional monitoring methods rely heavily on human expertise for problem diagnosis. In the absence of experienced specialists, the efficiency and effectiveness of anomaly handling are significantly reduced, sometimes leading to unresolved issues or prolonged repair times, which in turn adversely affect overall production efficiency and corporate profitability.
The core contribution of this study lies in proposing an intelligent production monitoring model that integrates data science and artificial intelligence techniques. This model consolidates three critical elements of current production monitoring—real-time anomaly detection, accurate performance prediction, and root cause analysis—to swiftly pinpoint the sources of anomalies and continuously optimize production processes.
The methodology of this study comprises several steps. First, multiple concepts from data science are synthesized to design an intelligent production monitoring framework featuring a cyclical improvement mechanism. Second, based on this framework, a detailed design is developed to guide the selection and analysis of monitoring points. Third, key functionalities are technically developed, including monitoring point analysis, standard setting, anomaly detection and trend analysis, predictive prevention, and root cause analysis, culminating in an interpretable causal analysis model. In terms of technology selection, various suitable models are compared to identify the one with the best performance for final implementation. Lastly, experiments and validations are conducted using publicly available production datasets to evaluate the model’s effectiveness and practical utility.
The results reveal that the proposed model accurately detects abnormalities in the production process, reliably predicts performance anomalies, and provides detailed insights into the characteristics of monitoring points and their underlying causes, thereby enabling managers to respond to and resolve anomalies promptly. By offering an efficient and feasible solution for the intelligent transformation of the manufacturing sector, this model enhances both corporate competitiveness and production efficiency.

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