续上文美国IndustryWeek上智能制造的目标，本段分享各种催化技术，以及如何普及，供自学与分享。
Tag: 工业4.0、物联网、智能制造
驱动智能制造的催化剂
Catalysts Driving Smart Manufacturing
以上是智能制造的宏伟目标，但这需要下列各种催化技术和标准融合到一起，来支持这场革新。
•智能机器和高级机器人：智能机器和制造系统通讯，同时体现高度的自主能力。这些机器在没有人为介入下，可以识别产品配置和诊断信息，自动决策和解决问题。机器人有先进的传感器，传动装置和智能，同时无需预先编程，就可以通过经验学习从而执行任务。传感器让他们感知环境，保证周边人类安全
•工业物联网：带有网络和互联网连接能力的制造设备 – 从移动平板，智能货架，嵌入式自动化控制和智能机器 – 积极参与到基于互联网的事件驱动和自我连接。
• 云服务：通过互联网提供的云软件和平台服务保证了统一性，便利性，随时可用的可配置计算资源共享中心（例如网络，服务器，存储，应用和服务），这些服务可以快速部署和释放，从而最小化管理费用，同时减少麻烦服务商的次数。
• 企业级合成平台： 企业级的合成平台，像ESB，MSB和API managers，有能力通过安全的公开标准，接受各种设备的广播数据，分析和累积数据，同时带来过程控制，历史数据保存，工作流可以保证横跨企业和价值链系统的商业过程，从A2A，B2B公开标准，变成合成数据统一标准。
•大数据处理能力： 大数据工具，像Hadoop，R，Storm 和Streaming 分析可以处理连接设备带来的海量流数据，这可以支持运营可视性，以及具体资产，过程和供应链的分析和诊断。 善用大数据处理的能力，会让制造厂商不但可以分析以往趋势，同时可以预测设备寿命，产能波动以及需求模式等。
Those are ambitious goals for Smart Manufacturing, but the following catalyst technologies and integration standards are coming together to provide the building blocks for this new revolution:
Smart machines and advanced robotics– Smart machines communicate with manufacturing systems and display a high level of autonomy. These machines recognize product configurations and diagnostic information, and make decisions and solve problems without human intervention.Robots have enhanced sensors, dexterity and intelligence and perform tasks without being pre-programmed as they can learn from experience. Sensors make them aware of the environment and safer for the people around them.
·Industrial Internet of Things (IIoT)– Manufacturing devices with network and internet connectivity — from mobile tablets to smart shelves to sensors embedded in automation controls to smart machines — are active participants in event-driven, self- connectivity via the internet.
·Cloud services–Cloud software and platform services are delivered over the internet and enable ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage,applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.
·Enterprise integration- platforms -Enterprise integration platforms, like Enterprise Service Buses (ESBs), Manufacturing Service Buses (MSBs), and API Managers, have the ability to receive data broadcast from equipment via secure open standards, analyze and aggregate the data, and trigger process controls,history recording, and work flows enabling business processes across the enterprise and value chain systems to be integrated via A2A and B2B open standards into a digital thread.
·Big data processing capabilities– Big data tools, like Hadoop, R, Storm and streaming analytics enable processing of large streams of data coming outof connected devices to support operational visibility, analysis and diagnostics over physical assets, processes and supply chains. Harnessing the power of big data analytics will allow manufacturers to not only analyze trends but also to predict events such as equipment lifespan, capacity fluctuation,and demand patterns.
如何普及智能制造
Next Steps to Ubiquitous Smart Manufacturing
尽管有这些先进技术，企业却还在为智能制造的相关提议做大量工作，因为我们在一些关键领域水平还不够，无法达到制造过程中的理想连接效果。
如果我们想要这些解决方案在中小型企业中广泛应用，我们还需要下面的努力：
• M2M，A2A和B2B集成标准的广泛应用，可以保证从公开集成平台到互联网，多个供应商软硬件的兼容性，那些组织如ISO，IEC，NIST和OAGi在建立和推广连接标准方面会扮演重要角色。
• 数据传输标准建立了数据化的连接，从工程系统中的产品定义到多层供应链的生产，到产品维护应用。标准不光用于发布产品3D的定义，同时用来管理变化和记录生产记录，便于信息回溯和备份目的。
新的职业能力 – 智能工厂的需要。制造IT人员需要学习制造系统，设备合成协议以及制造数据如何流入商业智能系统和企业指标的。工人们也需要学习如何配置和维护智能机器和机器人。
今后的文章，我们会继续记录智能制造之旅中的挑战和发展。同时，赶上这潮流，同时和上文提到的一些倡议和相关机构联系。这是一种可以让你的组织比竞争者快一步实施的方法。
However, even with these great technical advances, organizations are working on Smart Manufacturing related initiatives because we are still falling short in some key areas to achieve the desired levels of connectivity in manufacturing processes. If we want these solutions to be broadly available to small and medium size manufacturing companies, we will need to work on the following areas:
• Broad adoption of machine-to-machine (M2M), application-to-application (A2A), and business-to-business (B2B) integration standards that will enable multi-vendor hardware and software plug and play solutions with open integration platforms to the internet. Organizations like ISO, IEC, NIST, and OAGi play a key role in establishing and promoting standards for connectivity.
• Data messaging standards that create a digital thread of communications from product definition in engineering systems to manufacturing in a multi-tier supply chain to product maintenance applications. Standards that not only distribute the product 3D definition but are also used to manage changes and record production history for traceability and archival purposes.
• New workforce skills will be required for the smart factory. Manufacturing IT personnel will need to learn about manufacturing systems, protocols for equipment integration, and how manufacturing data flows into business intelligence and corporate metrics. Workers will need to learn how to configure and maintain smart machines and robots.
In future articles, we will continue to chronicle the challenges and progress on the journey to Smart Manufacturing. In the meantime, hop on the bandwagon and get involved with some of the initiatives and organizations listed in this article. It is one way for your organization to be on the forefront of adoption and a step ahead of the competition.
原文可见：http://www.industryweek.com/systems-integration/journey-smart-manufacturing-revolution
作者：Conrad Leiva; VP Product Strategy and Alliances, iBaset
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