<p>/PRNewswire/ -- Troy Mahr，罗克韦尔自动化的总监，解释了实现自主运营需要整合工业数据和人工智能，以消除信息孤岛，启用预测和自适应能力，并逐步实现从观察到企业自主决策的转变。罗克韦尔自动化公司（NYSE: ROK）是工业自动化和数字化转型的全球领导者。</p>
<p>我们从行业领导者那里听到的一致反馈是，需要在全球运营中实现实时可见性，这对确保其运营保持灵活和可扩展至关重要。然而，若不通过部署连接资产和上下文化数据来消除滞后的手动数据收集，这一目标是无法实现的。</p>
<p>通过消除数据孤岛并解锁工业数据和人工智能（AI）能力，公司可以实现优化成本、效率和生产韧性的自主决策。这使得他们的组织更接近实现自主运营。</p>
<p>在企业中实现自主性需要跨越整个智能光谱的能力，从观察和推理到决策和行动。这些能力在所有运营领域都具有相关性，包括产品设计、制造、供应链、分销、直接面向客户的渠道和需求预测。</p>
<p>特别是在制造运营方面，通过模型预测控制（MPC）取得了进展，该技术持续分析实时和预测数据，以在定义的约束内优化过程控制。虽然 MPC 在制造领域是一个强有力的例子，但更广泛的自主性要求在整个企业中扩展类似的智能系统。</p>
<p>这一旅程在工业 AI 成熟度金字塔中得以体现，该金字塔概述了从基本数据集成和可视化到预测分析、处方决策，最终实现自主运营的进程。随着组织在这座金字塔上攀登，他们采用机器学习、实时自动化和自学习系统。每个阶段不仅需要技术升级，还需要文化和结构的转型。</p>
<p><strong>资产监控：找出停机根本原因</strong></p>
<p>从工业 AI 成熟度金字塔来看，资产监控是从观察到解释的入门和过渡点。这是技术变化如何将用例转移到金字塔不同层级的一个很好的例子。有效的资产监控对于维持运营效率和最小化停机至关重要。通过更好地理解传感器数据趋势、报警和维护工单上下文，企业可以快速识别和解决停机的根本原因。</p>
<p>此外，比较多个工厂中类似设备的可靠性和性能，可以更明智地做出决策并优化资产利用。这种方法不仅有助于防止意外故障，还确保维护活动能够主动安排，从而延长资产的使用寿命并降低运营成本。</p>
<p><strong>质量控制：预测质量问题何时可能发生</strong></p>
<p>向上移动金字塔进入推理层通常涉及质量控制、自适应制造或预测性维护等能力。保持高产品质量对客户满意度和合规性至关重要。人工智能可以检测并建议修正影响产品质量的偏差，自动化检查过程，并预测质量问题何时可能发生。通过监控进料材料的质量，企业可以降低缺陷风险。</p>
<p>一个显著的例子是我们在 Twinsburg 制造工厂的应用，该工厂专注于电子组装。在这种情况下，工业 AI 提供潜在故障的警报，使团队能够采取主动行动。虽然这种方法并没有自己进行更改，但它显著增强了决策过程。能够在质量问题升级之前进行预测和处理，确保产品符合严格的质量标准，减少浪费并提高整体效率。</p>
<p><strong>自适应制造：围绕生产线调整支持资源</strong></p>
<p>自适应制造利用实时数据来调整生产计划、调配资源，并迅速应对需求变化。人工智能分析生产和市场条件，以实时自主调整计划、设备和工作流程。</p>
<p>虽然这种方法并不改变生产线上的实际情况，但它支持周围的资源。这一概念在需要根据下游反馈调整生产的场景中特别相关，确保最佳效率和响应能力。例如，如果在下游检测到减速，可以向上游发送信号以相应调整生产速度，从而防止瓶颈并保持操作的顺畅流动。</p>
<p>重要的是要强调，您正在管理生产的支持资源，而这正是您的自主制造开始的地方。</p>
<p><strong>预测性维护：自动化维修决策</strong></p>
<p>预测性维护是一种主动的维护调度方法，旨在提高资产利用率并降低成本。在这种方法下，人工智能分析历史数据和当前设备状态信息，以识别模式并进行预测，进一步优化维护计划并自动化维修决策。尽管人工智能并不直接进行维修，但它显著减少了计划外停机和相关成本。</p>
<p>这种方法类似于向团队提供故障可能发生的警报，使他们能够采取预防措施。通过预测维护需求，企业可以避免昂贵的中断，并延长设备的使用寿命，最终实现更高效和更可靠的运营。</p>
<p>每个组织都有一个维护部门，但每个部门的成熟度不同。然而，在采用先进解决方案时，许多企业面临与技能、人才保留和持续培训相关的挑战。随着边缘计算和分析技术的显著进步，现在有一个强大的机会可以通过机器学习将创新直接注入智能设备。</p>
<p>预测性维护提供了一个全面的解决方案。它将硬件、软件和服务无缝整合在一起，代表了状态监测技术的下一次演变。</p>
<p><strong>流程优化：识别变量并进行纠正</strong></p>
<p>正如我们之前讨论的，我们看到的一个常见应用是工业数据和人工智能在模型预测控制（MPC）领域的应用。通过利用工业数据和人工智能技术，企业可以做出更好、更快和更明智的决策，最终解锁人工智能能力，向金字塔的决策层迈进，为自主运营铺平道路。</p>
<p>对生产过程的详细洞察使得识别和解决低效问题成为可能。MPC 允许对工厂内特定操作进行建模，管理 PLC 内的设定点以控制设备，并利用数据科学实时进行纠正。MPC 系统提供一个反馈循环，持续调整生产参数，以保持最佳性能，即使在条件变化时。</p>
<p>通过 MPC，组织不仅从生产线和控制生产的 PLC 读取来自各种传感器的数据，还同时将数据写回 PLC，并根据需要发出更改生产线速率的指令。</p>
<p><strong>结论</strong></p>
<p>工业数据与人工智能的整合正在改变各个领域的运营，从资产监测到预测性维护。通过解锁工业人工智能能力，企业可以更接近实现自主运营，做出更好、更快和更明智的决策。随着技术的不断发展，完全自主运营的愿景变得越来越可实现，承诺带来更高的效率、可靠性和适应性。</p>
<p>通往自主运营的旅程涉及逐步推进，每一步都使企业更接近一个系统能够独立管理和优化流程的状态，确保在竞争市场中持续增长和韧性。</p>
<p>Logo - https://mma.prnewswire.com/media/1981317/Rockwell_Automation_Logo.jpg</p>
<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>

罗克韦尔自动化

<p>罗克韦尔自动化的董事 Troy Mahr 讨论了工业数据与人工智能的整合，以实现自主操作。这涉及消除数据孤岛、实现预测能力，并朝着自主决策迈进。这个过程包括从数据整合到自主操作的各个阶段，需要技术和文化的转型。关键领域包括资产监控、质量控制、自适应制造和预测性维护，所有这些都旨在优化效率和降低成本</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，罗克韦尔自动化的总监，解释了实现自主运营需要整合工业数据和人工智能，以消除信息孤岛，启用预测和自适应能力，并逐步实现从观察到企业自主决策的转变。罗克韦尔自动化公司（NYSE: ROK）是工业自动化和数字化转型的全球领导者。</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>我们从行业领导者那里听到的一致反馈是，需要在全球运营中实现实时可见性，这对确保其运营保持灵活和可扩展至关重要。然而，若不通过部署连接资产和上下文化数据来消除滞后的手动数据收集，这一目标是无法实现的。</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>通过消除数据孤岛并解锁工业数据和人工智能（AI）能力，公司可以实现优化成本、效率和生产韧性的自主决策。这使得他们的组织更接近实现自主运营。</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>在企业中实现自主性需要跨越整个智能光谱的能力，从观察和推理到决策和行动。这些能力在所有运营领域都具有相关性，包括产品设计、制造、供应链、分销、直接面向客户的渠道和需求预测。</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>特别是在制造运营方面，通过模型预测控制（MPC）取得了进展，该技术持续分析实时和预测数据，以在定义的约束内优化过程控制。虽然 MPC 在制造领域是一个强有力的例子，但更广泛的自主性要求在整个企业中扩展类似的智能系统。</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>这一旅程在工业 AI 成熟度金字塔中得以体现，该金字塔概述了从基本数据集成和可视化到预测分析、处方决策，最终实现自主运营的进程。随着组织在这座金字塔上攀登，他们采用机器学习、实时自动化和自学习系统。每个阶段不仅需要技术升级，还需要文化和结构的转型。</p>
</div><p><strong>Asset Monitoring: Find downtime root causes</strong></p>
<div class="lb-trans"><p><strong>资产监控：找出停机根本原因</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>从工业 AI 成熟度金字塔来看，资产监控是从观察到解释的入门和过渡点。这是技术变化如何将用例转移到金字塔不同层级的一个很好的例子。有效的资产监控对于维持运营效率和最小化停机至关重要。通过更好地理解传感器数据趋势、报警和维护工单上下文，企业可以快速识别和解决停机的根本原因。</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，比较多个工厂中类似设备的可靠性和性能，可以更明智地做出决策并优化资产利用。这种方法不仅有助于防止意外故障，还确保维护活动能够主动安排，从而延长资产的使用寿命并降低运营成本。</p>
</div><p><strong>Quality Control: Predict when quality issues are likely to occur</strong></p>
<div class="lb-trans"><p><strong>质量控制：预测质量问题何时可能发生</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>向上移动金字塔进入推理层通常涉及质量控制、自适应制造或预测性维护等能力。保持高产品质量对客户满意度和合规性至关重要。人工智能可以检测并建议修正影响产品质量的偏差，自动化检查过程，并预测质量问题何时可能发生。通过监控进料材料的质量，企业可以降低缺陷风险。</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>一个显著的例子是我们在 Twinsburg 制造工厂的应用，该工厂专注于电子组装。在这种情况下，工业 AI 提供潜在故障的警报，使团队能够采取主动行动。虽然这种方法并没有自己进行更改，但它显著增强了决策过程。能够在质量问题升级之前进行预测和处理，确保产品符合严格的质量标准，减少浪费并提高整体效率。</p>
</div><p><strong>Adaptive Manufacturing: Change supporting resources around the production line</strong></p>
<div class="lb-trans"><p><strong>自适应制造：围绕生产线调整支持资源</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>自适应制造利用实时数据来调整生产计划、调配资源，并迅速应对需求变化。人工智能分析生产和市场条件，以实时自主调整计划、设备和工作流程。</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>虽然这种方法并不改变生产线上的实际情况，但它支持周围的资源。这一概念在需要根据下游反馈调整生产的场景中特别相关，确保最佳效率和响应能力。例如，如果在下游检测到减速，可以向上游发送信号以相应调整生产速度，从而防止瓶颈并保持操作的顺畅流动。</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>重要的是要强调，您正在管理生产的支持资源，而这正是您的自主制造开始的地方。</p>
</div><p><strong>Predictive Maintenance: Automate decision for repair</strong></p>
<div class="lb-trans"><p><strong>预测性维护：自动化维修决策</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>预测性维护是一种主动的维护调度方法，旨在提高资产利用率并降低成本。在这种方法下，人工智能分析历史数据和当前设备状态信息，以识别模式并进行预测，进一步优化维护计划并自动化维修决策。尽管人工智能并不直接进行维修，但它显著减少了计划外停机和相关成本。</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>这种方法类似于向团队提供故障可能发生的警报，使他们能够采取预防措施。通过预测维护需求，企业可以避免昂贵的中断，并延长设备的使用寿命，最终实现更高效和更可靠的运营。</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>每个组织都有一个维护部门，但每个部门的成熟度不同。然而，在采用先进解决方案时，许多企业面临与技能、人才保留和持续培训相关的挑战。随着边缘计算和分析技术的显著进步，现在有一个强大的机会可以通过机器学习将创新直接注入智能设备。</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>预测性维护提供了一个全面的解决方案。它将硬件、软件和服务无缝整合在一起，代表了状态监测技术的下一次演变。</p>
</div><p><strong>Process Optimization: Recognize variables and course correct</strong></p>
<div class="lb-trans"><p><strong>流程优化：识别变量并进行纠正</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>正如我们之前讨论的，我们看到的一个常见应用是工业数据和人工智能在模型预测控制（MPC）领域的应用。通过利用工业数据和人工智能技术，企业可以做出更好、更快和更明智的决策，最终解锁人工智能能力，向金字塔的决策层迈进，为自主运营铺平道路。</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>对生产过程的详细洞察使得识别和解决低效问题成为可能。MPC 允许对工厂内特定操作进行建模，管理 PLC 内的设定点以控制设备，并利用数据科学实时进行纠正。MPC 系统提供一个反馈循环，持续调整生产参数，以保持最佳性能，即使在条件变化时。</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>通过 MPC，组织不仅从生产线和控制生产的 PLC 读取来自各种传感器的数据，还同时将数据写回 PLC，并根据需要发出更改生产线速率的指令。</p>
</div><p><strong>Conclusion</strong></p>
<div class="lb-trans"><p><strong>结论</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>工业数据与人工智能的整合正在改变各个领域的运营，从资产监测到预测性维护。通过解锁工业人工智能能力，企业可以更接近实现自主运营，做出更好、更快和更明智的决策。随着技术的不断发展，完全自主运营的愿景变得越来越可实现，承诺带来更高的效率、可靠性和适应性。</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>通往自主运营的旅程涉及逐步推进，每一步都使企业更接近一个系统能够独立管理和优化流程的状态，确保在竞争市场中持续增长和韧性。</p>
</div><p>Logo - https://mma.prnewswire.com/media/1981317/Rockwell_Automation_Logo.jpg</p>
<div class="lb-trans"><p>Logo - https://mma.prnewswire.com/media/1981317/Rockwell_Automation_Logo.jpg</p>
</div><p><img src="https://imageproxy.pbkrs.com/https://rt.prnewswire.com/rt.gif/query-RGF0ZUlkPTIwMjUxMjIyJk5ld3NJdGVtSWQ9RU41MDg2OSZUcmFuc21pc3Npb25fSWQ9MjAyNTEyMjIwMjAwUFJfTkVXU19FVVJPX05EX19FTjUwODY5" alt="" original-src="https://rt.prnewswire.com/rt.gif?NewsItemId=EN50869&amp;Transmission_Id=202512220200PR_NEWS_EURO_ND__EN50869&amp;DateId=20251222"/></p>

罗克韦尔自动化：致力于实现自主运营