<p>/PRNewswire/ -- Troy Mahr，羅克韋爾自動化的總監，解釋了實現自主運營需要整合工業數據和人工智能，以消除信息孤島，啓用預測和自適應能力，並逐步實現從觀察到企業自主決策的轉變。羅克韋爾自動化公司（NYSE: ROK）是工業自動化和數字化轉型的全球領導者。</p>
<p>我們從行業領導者那裏聽到的一致反饋是，需要在全球運營中實現實時可見性，這對確保其運營保持靈活和可擴展至關重要。然而，若不通過部署連接資產和上下文化數據來消除滯後的手動數據收集，這一目標是無法實現的。</p>
<p>通過消除數據孤島並解鎖工業數據和人工智能（AI）能力，公司可以實現優化成本、效率和生產韌性的自主決策。這使得他們的組織更接近實現自主運營。</p>
<p>在企業中實現自主性需要跨越整個智能光譜的能力，從觀察和推理到決策和行動。這些能力在所有運營領域都具有相關性，包括產品設計、製造、供應鏈、分銷、直接面向客户的渠道和需求預測。</p>
<p>特別是在製造運營方面，通過模型預測控制（MPC）取得了進展，該技術持續分析實時和預測數據，以在定義的約束內優化過程控制。雖然 MPC 在製造領域是一個強有力的例子，但更廣泛的自主性要求在整個企業中擴展類似的智能系統。</p>
<p>這一旅程在工業 AI 成熟度金字塔中得以體現，該金字塔概述了從基本數據集成和可視化到預測分析、處方決策，最終實現自主運營的進程。隨着組織在這座金字塔上攀登，他們採用機器學習、實時自動化和自學習系統。每個階段不僅需要技術升級，還需要文化和結構的轉型。</p>
<p><strong>資產監控：找出停機根本原因</strong></p>
<p>從工業 AI 成熟度金字塔來看，資產監控是從觀察到解釋的入門和過渡點。這是技術變化如何將用例轉移到金字塔不同層級的一個很好的例子。有效的資產監控對於維持運營效率和最小化停機至關重要。通過更好地理解傳感器數據趨勢、報警和維護工單上下文，企業可以快速識別和解決停機的根本原因。</p>
<p>此外，比較多個工廠中類似設備的可靠性和性能，可以更明智地做出決策並優化資產利用。這種方法不僅有助於防止意外故障，還確保維護活動能夠主動安排，從而延長資產的使用壽命並降低運營成本。</p>
<p><strong>質量控制：預測質量問題何時可能發生</strong></p>
<p>向上移動金字塔進入推理層通常涉及質量控制、自適應製造或預測性維護等能力。保持高產品質量對客户滿意度和合規性至關重要。人工智能可以檢測並建議修正影響產品質量的偏差，自動化檢查過程，並預測質量問題何時可能發生。通過監控進料材料的質量，企業可以降低缺陷風險。</p>
<p>一個顯著的例子是我們在 Twinsburg 製造工廠的應用，該工廠專注於電子組裝。在這種情況下，工業 AI 提供潛在故障的警報，使團隊能夠採取主動行動。雖然這種方法並沒有自己進行更改，但它顯著增強了決策過程。能夠在質量問題升級之前進行預測和處理，確保產品符合嚴格的質量標準，減少浪費並提高整體效率。</p>
<p><strong>自適應製造：圍繞生產線調整支持資源</strong></p>
<p>自適應製造利用實時數據來調整生產計劃、調配資源，並迅速應對需求變化。人工智能分析生產和市場條件，以實時自主調整計劃、設備和工作流程。</p>
<p>雖然這種方法並不改變生產線上的實際情況，但它支持周圍的資源。這一概念在需要根據下游反饋調整生產的場景中特別相關，確保最佳效率和響應能力。例如，如果在下游檢測到減速，可以向上遊發送信號以相應調整生產速度，從而防止瓶頸並保持操作的順暢流動。</p>
<p>重要的是要強調，您正在管理生產的支持資源，而這正是您的自主製造開始的地方。</p>
<p><strong>預測性維護：自動化維修決策</strong></p>
<p>預測性維護是一種主動的維護調度方法，旨在提高資產利用率並降低成本。在這種方法下，人工智能分析歷史數據和當前設備狀態信息，以識別模式並進行預測，進一步優化維護計劃並自動化維修決策。儘管人工智能並不直接進行維修，但它顯著減少了計劃外停機和相關成本。</p>
<p>這種方法類似於向團隊提供故障可能發生的警報，使他們能夠採取預防措施。通過預測維護需求，企業可以避免昂貴的中斷，並延長設備的使用壽命，最終實現更高效和更可靠的運營。</p>
<p>每個組織都有一個維護部門，但每個部門的成熟度不同。然而，在採用先進解決方案時，許多企業面臨與技能、人才保留和持續培訓相關的挑戰。隨着邊緣計算和分析技術的顯著進步，現在有一個強大的機會可以通過機器學習將創新直接注入智能設備。</p>
<p>預測性維護提供了一個全面的解決方案。它將硬件、軟件和服務無縫整合在一起，代表了狀態監測技術的下一次演變。</p>
<p><strong>流程優化：識別變量並進行糾正</strong></p>
<p>正如我們之前討論的，我們看到的一個常見應用是工業數據和人工智能在模型預測控制（MPC）領域的應用。通過利用工業數據和人工智能技術，企業可以做出更好、更快和更明智的決策，最終解鎖人工智能能力，向金字塔的決策層邁進，為自主運營鋪平道路。</p>
<p>對生產過程的詳細洞察使得識別和解決低效問題成為可能。MPC 允許對工廠內特定操作進行建模，管理 PLC 內的設定點以控制設備，並利用數據科學實時進行糾正。MPC 系統提供一個反饋循環，持續調整生產參數，以保持最佳性能，即使在條件變化時。</p>
<p>通過 MPC，組織不僅從生產線和控制生產的 PLC 讀取來自各種傳感器的數據，還同時將數據寫回 PLC，並根據需要發出更改生產線速率的指令。</p>
<p><strong>結論</strong></p>
<p>工業數據與人工智能的整合正在改變各個領域的運營，從資產監測到預測性維護。通過解鎖工業人工智能能力，企業可以更接近實現自主運營，做出更好、更快和更明智的決策。隨着技術的不斷發展，完全自主運營的願景變得越來越可實現，承諾帶來更高的效率、可靠性和適應性。</p>
<p>通往自主運營的旅程涉及逐步推進，每一步都使企業更接近一個系統能夠獨立管理和優化流程的狀態，確保在競爭市場中持續增長和韌性。</p>
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<p><img src="https://imageproxy.pbkrs.com/https://rt.prnewswire.com/rt.gif/query-RGF0ZUlkPTIwMjUxMjIyJk5ld3NJdGVtSWQ9RU41MDg2OSZUcmFuc21pc3Npb25fSWQ9MjAyNTEyMjIwMjAwUFJfTkVXU19FVVJPX05EX19FTjUwODY5?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" alt="" original-src="https://imageproxy.pbkrs.com/https://rt.prnewswire.com/rt.gif/query-RGF0ZUlkPTIwMjUxMjIyJk5ld3NJdGVtSWQ9RU41MDg2OSZUcmFuc21pc3Npb25fSWQ9MjAyNTEyMjIwMjAwUFJfTkVXU19FVVJPX05EX19FTjUwODY5"/></p>

羅克韋爾自動化

<p>羅克韋爾自動化的董事 Troy Mahr 討論了工業數據與人工智能的整合，以實現自主操作。這涉及消除數據孤島、實現預測能力，並朝着自主決策邁進。這個過程包括從數據整合到自主操作的各個階段，需要技術和文化的轉型。關鍵領域包括資產監控、質量控制、自適應製造和預測性維護，所有這些都旨在優化效率和降低成本</p>

<p>, /PRNewswire/ -- Troy Mahr, Director, Rockwell Automation, explains how achieving autonomous operations requires integrating industrial data and AI to eliminate silos, enable predictive and adaptive capabilities, and progressively move from observation to autonomous decision-making across the enterprise. Rockwell Automation Inc. (NYSE: ROK), is a global leader in industrial automation and digital transformation.</p>
<div class="lb-trans"><p>/PRNewswire/ -- Troy Mahr，羅克韋爾自動化的總監，解釋了實現自主運營需要整合工業數據和人工智能，以消除信息孤島，啓用預測和自適應能力，並逐步實現從觀察到企業自主決策的轉變。羅克韋爾自動化公司（NYSE: ROK）是工業自動化和數字化轉型的全球領導者。</p>
</div><p>What we consistently hear from industry leaders is the need for real-time visibility across their global operations, which is key to ensuring their operations stay agile and scalable. However, achieving this isn't possible without removing laggy manual data collection through the deployment of connected assets and contextualized data.</p>
<div class="lb-trans"><p>我們從行業領導者那裏聽到的一致反饋是，需要在全球運營中實現實時可見性，這對確保其運營保持靈活和可擴展至關重要。然而，若不通過部署連接資產和上下文化數據來消除滯後的手動數據收集，這一目標是無法實現的。</p>
</div><p>By eliminating data silos and unlocking industrial data and artificial intelligence (AI) capabilities, companies can enable autonomous decision-making that optimizes costs, efficiency, and production resilience. This moves their organization closer to achieving autonomous operations.</p>
<div class="lb-trans"><p>通過消除數據孤島並解鎖工業數據和人工智能（AI）能力，公司可以實現優化成本、效率和生產韌性的自主決策。這使得他們的組織更接近實現自主運營。</p>
</div><p>Achieving autonomy across an enterprise requires capabilities that span the full intelligence spectrum, from observation and inference to decision-making and action. These capabilities are relevant across all operational areas, including product design, manufacturing, supply chain, distribution, direct-to-customer channels, and demand forecasting.</p>
<div class="lb-trans"><p>在企業中實現自主性需要跨越整個智能光譜的能力，從觀察和推理到決策和行動。這些能力在所有運營領域都具有相關性，包括產品設計、製造、供應鏈、分銷、直接面向客户的渠道和需求預測。</p>
</div><p>Manufacturing operations, in particular, have seen progress through Model Predictive Control (MPC), which continuously analyzes real-time and forecasted data to optimize process control within defined constraints. While MPC is a strong example within manufacturing, broader autonomy demands extending similar intelligent systems across the enterprise.</p>
<div class="lb-trans"><p>特別是在製造運營方面，通過模型預測控制（MPC）取得了進展，該技術持續分析實時和預測數據，以在定義的約束內優化過程控制。雖然 MPC 在製造領域是一個強有力的例子，但更廣泛的自主性要求在整個企業中擴展類似的智能系統。</p>
</div><p>This journey is captured in the industrial AI maturity pyramid, which outlines a progression from basic data integration and visualization to predictive analytics, prescriptive decision-making, and ultimately, autonomous operations. As organizations climb this pyramid, they adopt machine learning, real-time automation, and self-learning systems. Each stage requires not just technological upgrades but also cultural and structural transformation.</p>
<div class="lb-trans"><p>這一旅程在工業 AI 成熟度金字塔中得以體現，該金字塔概述了從基本數據集成和可視化到預測分析、處方決策，最終實現自主運營的進程。隨着組織在這座金字塔上攀登，他們採用機器學習、實時自動化和自學習系統。每個階段不僅需要技術升級，還需要文化和結構的轉型。</p>
</div><p><strong>Asset Monitoring: Find downtime root causes</strong></p>
<div class="lb-trans"><p><strong>資產監控：找出停機根本原因</strong></p>
</div><p>Looking at the Industrial AI Maturity Pyramid, asset monitoring is an entry and transition point from observation into explanation. This is a great example of how changes in technology have shifted use cases into different layers of the pyramid. Effective asset monitoring is crucial for maintaining operational efficiency and minimizing downtime. By better understanding sensor data trends, alarming, and maintenance work order context, businesses can quickly identify and address root causes of downtime through engineering analysis.</p>
<div class="lb-trans"><p>從工業 AI 成熟度金字塔來看，資產監控是從觀察到解釋的入門和過渡點。這是技術變化如何將用例轉移到金字塔不同層級的一個很好的例子。有效的資產監控對於維持運營效率和最小化停機至關重要。通過更好地理解傳感器數據趨勢、報警和維護工單上下文，企業可以快速識別和解決停機的根本原因。</p>
</div><p>Additionally, comparing the reliability and performance of similar equipment across multiple plants allows for more informed decision-making and optimized asset utilization. This approach not only helps in preventing unexpected failures but also ensures that maintenance activities are scheduled proactively, thereby extending the lifespan of assets and reducing operational costs.</p>
<div class="lb-trans"><p>此外，比較多個工廠中類似設備的可靠性和性能，可以更明智地做出決策並優化資產利用。這種方法不僅有助於防止意外故障，還確保維護活動能夠主動安排，從而延長資產的使用壽命並降低運營成本。</p>
</div><p><strong>Quality Control: Predict when quality issues are likely to occur</strong></p>
<div class="lb-trans"><p><strong>質量控制：預測質量問題何時可能發生</strong></p>
</div><p>Moving up the pyramid into the inference layer usually involves a capability like quality control, adaptive manufacturing or predictive maintenance. Maintaining high product quality is essential for customer satisfaction and regulatory compliance. AI can detect and suggest corrections for deviations that impact product quality, automate the inspection process, and predict when quality issues are likely to occur. By monitoring the quality of incoming materials, businesses can reduce the risk of defects.</p>
<div class="lb-trans"><p>向上移動金字塔進入推理層通常涉及質量控制、自適應製造或預測性維護等能力。保持高產品質量對客户滿意度和合規性至關重要。人工智能可以檢測並建議修正影響產品質量的偏差，自動化檢查過程，並預測質量問題何時可能發生。通過監控進料材料的質量，企業可以降低缺陷風險。</p>
</div><p>A notable example is our own application at our Twinsburg manufacturing plant, which focuses on electronic assembly. In this case, Industrial AI provides alerts for potential faults that allow teams to take proactive action. While this approach stops short of making the changes itself, it significantly enhances the decision-making process. The ability to predict and address quality issues before they escalate ensures that products meet stringent quality standards, reducing waste and improving overall efficiency.</p>
<div class="lb-trans"><p>一個顯著的例子是我們在 Twinsburg 製造工廠的應用，該工廠專注於電子組裝。在這種情況下，工業 AI 提供潛在故障的警報，使團隊能夠採取主動行動。雖然這種方法並沒有自己進行更改，但它顯著增強了決策過程。能夠在質量問題升級之前進行預測和處理，確保產品符合嚴格的質量標準，減少浪費並提高整體效率。</p>
</div><p><strong>Adaptive Manufacturing: Change supporting resources around the production line</strong></p>
<div class="lb-trans"><p><strong>自適應製造：圍繞生產線調整支持資源</strong></p>
</div><p>Adaptive manufacturing leverages real-time data to adapt production schedules, shift resources, and pivot quickly to changes in demand. AI analyzes production and market conditions to autonomously adjust schedules, equipment, and workflows in real-time.</p>
<div class="lb-trans"><p>自適應製造利用實時數據來調整生產計劃、調配資源，並迅速應對需求變化。人工智能分析生產和市場條件，以實時自主調整計劃、設備和工作流程。</p>
</div><p>While this approach does not change what happens on the production line, it supports the resources around it. This concept is particularly relevant in scenarios where production needs to be adjusted based on downstream feedback, ensuring optimal efficiency and responsiveness. For instance, if a slowdown is detected further downstream, signals can be sent upstream to adjust production rates accordingly, preventing bottlenecks and maintaining a smooth flow of operations.</p>
<div class="lb-trans"><p>雖然這種方法並不改變生產線上的實際情況，但它支持周圍的資源。這一概念在需要根據下游反饋調整生產的場景中特別相關，確保最佳效率和響應能力。例如，如果在下游檢測到減速，可以向上遊發送信號以相應調整生產速度，從而防止瓶頸並保持操作的順暢流動。</p>
</div><p>It's important to highlight that you're managing supporting resources for production, and this is really where your autonomous manufacturing begins.</p>
<div class="lb-trans"><p>重要的是要強調，您正在管理生產的支持資源，而這正是您的自主製造開始的地方。</p>
</div><p><strong>Predictive Maintenance: Automate decision for repair</strong></p>
<div class="lb-trans"><p><strong>預測性維護：自動化維修決策</strong></p>
</div><p>Predictive maintenance is a proactive approach to scheduling maintenance, improving asset utilization, and lowering costs. Under this approach, AI analyzes historical data and current state equipment information to recognize patterns and make predictions, further optimizing maintenance schedules and automating decision-making for repairs. Although AI does not conduct the repairs itself, it significantly minimizes unplanned downtime and associated costs.</p>
<div class="lb-trans"><p>預測性維護是一種主動的維護調度方法，旨在提高資產利用率並降低成本。在這種方法下，人工智能分析歷史數據和當前設備狀態信息，以識別模式並進行預測，進一步優化維護計劃並自動化維修決策。儘管人工智能並不直接進行維修，但它顯著減少了計劃外停機和相關成本。</p>
</div><p>This approach is similar to providing alerts to the team that a fault could occur, allowing them to take pre-emptive action. By anticipating maintenance needs, businesses can avoid costly disruptions and extend the operational life of their equipment, ultimately leading to more efficient and reliable operations.</p>
<div class="lb-trans"><p>這種方法類似於向團隊提供故障可能發生的警報，使他們能夠採取預防措施。通過預測維護需求，企業可以避免昂貴的中斷，並延長設備的使用壽命，最終實現更高效和更可靠的運營。</p>
</div><p>Every organization has a maintenance department, each at a different stage of maturity. However, when adopting advanced solutions, many face challenges related to skills, talent retention, and ongoing training. With significant progress in edge computing and analytics, there's now a powerful opportunity to infuse innovation directly into intelligent devices through machine learning.</p>
<div class="lb-trans"><p>每個組織都有一個維護部門，但每個部門的成熟度不同。然而，在採用先進解決方案時，許多企業面臨與技能、人才保留和持續培訓相關的挑戰。隨着邊緣計算和分析技術的顯著進步，現在有一個強大的機會可以通過機器學習將創新直接注入智能設備。</p>
</div><p>Predictive maintenance offers a comprehensive solution. It's hardware, software, and services brought together seamlessly under one roof, representing the next evolution in condition monitoring technology.</p>
<div class="lb-trans"><p>預測性維護提供了一個全面的解決方案。它將硬件、軟件和服務無縫整合在一起，代表了狀態監測技術的下一次演變。</p>
</div><p><strong>Process Optimization: Recognize variables and course correct</strong></p>
<div class="lb-trans"><p><strong>流程優化：識別變量並進行糾正</strong></p>
</div><p>As we discussed earlier, a common application for industrial data and AI we're seeing for our industry clients is within the model predictive control (MPC) space. By leveraging industrial data and AI technologies, businesses can make better, faster, and more informed decisions, ultimately unlocking AI capabilities, moving up to the decision layer of the pyramid and paving the way for autonomous operations.</p>
<div class="lb-trans"><p>正如我們之前討論的，我們看到的一個常見應用是工業數據和人工智能在模型預測控制（MPC）領域的應用。通過利用工業數據和人工智能技術，企業可以做出更好、更快和更明智的決策，最終解鎖人工智能能力，向金字塔的決策層邁進，為自主運營鋪平道路。</p>
</div><p>Detailed insights into production processes enable the identification and resolution of inefficiencies. MPC allows for the modelling of specific operations within a plant, managing set points within a PLC to control equipment, and using data science to course-correct in real-time. MPC systems provide a feedback loop that continuously adjusts production parameters to maintain optimal performance, even as conditions change.</p>
<div class="lb-trans"><p>對生產過程的詳細洞察使得識別和解決低效問題成為可能。MPC 允許對工廠內特定操作進行建模，管理 PLC 內的設定點以控制設備，並利用數據科學實時進行糾正。MPC 系統提供一個反饋循環，持續調整生產參數，以保持最佳性能，即使在條件變化時。</p>
</div><p>With MPC, organizations are not only reading data from various sensors on the production line and the PLC that controls production but are simultaneously writing back to the PLC and giving instructions to change the line rate as needed.</p>
<div class="lb-trans"><p>通過 MPC，組織不僅從生產線和控制生產的 PLC 讀取來自各種傳感器的數據，還同時將數據寫回 PLC，並根據需要發出更改生產線速率的指令。</p>
</div><p><strong>Conclusion</strong></p>
<div class="lb-trans"><p><strong>結論</strong></p>
</div><p>The integration of industrial data and AI is transforming operations across various domains, from asset monitoring to predictive maintenance. By unlocking Industrial AI capabilities, businesses can move closer to achieving autonomous operations, making better, faster, and more informed decisions. As technology continues to evolve, the vision for fully autonomous operations becomes increasingly attainable, promising a future of enhanced efficiency, reliability and adaptability.</p>
<div class="lb-trans"><p>工業數據與人工智能的整合正在改變各個領域的運營，從資產監測到預測性維護。通過解鎖工業人工智能能力，企業可以更接近實現自主運營，做出更好、更快和更明智的決策。隨着技術的不斷發展，完全自主運營的願景變得越來越可實現，承諾帶來更高的效率、可靠性和適應性。</p>
</div><p>The journey towards autonomous operations involves incremental steps, each bringing businesses closer to a state where systems can independently manage and optimize processes, ensuring sustained growth and resilience in a competitive market.</p>
<div class="lb-trans"><p>通往自主運營的旅程涉及逐步推進，每一步都使企業更接近一個系統能夠獨立管理和優化流程的狀態，確保在競爭市場中持續增長和韌性。</p>
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羅克韋爾自動化：致力於實現自主運營