T如今，关于人工智能，云和认知计算，这里有很多讨论。

But what the heck do those things have to do with industrial automation, and why should automation professionals be getting up to speed on these technologies?

简单的答案：这些新技术资源需要消耗的数据才能真正证明某种价值，这将来自工业自动化，过程控制和制造设备，这些设备自动化工程师（也称为自动化工程师）operations technology professionals, work on every day.

但是，这些信息技术（例如认知计算）将如何为制造业等工业应用增加价值？

网站TechTarget.comdefines cognitive computing as, ”The simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works.”

认知计算系统，例如IBM Watson IoT Platform，使用高级机器学习算法to crunch massive volumes of data. Cognitive computing is what allows the Watson platform to actually learn from the data that’s fed into it.

但是一旦这些超级智能计算系统get access to the data from our manufacturing systems, what are they going to do with it?

数据进入后，在三个关键领域中，沃森（Watson）等认知计算系统可以改善制造应用：

• 在资产和设备中添加智力
• 提高流程，质量和运营效率
• Increasing manufacturing resource intelligence

在资产和设备中添加智力

一旦设备连接到认知计算系统并开始发布其数据进行分析，计算系统基本上就开始充当设备的认知大脑。

Imagine adding a supercomputer to your manufacturing equipment. Now that supercomputer can tap into information from sensors attached to equipment and perform predictive analytics on things like motor or bearing failure.

认知计算系统中的机器学习算法寻找系统性能和行为的异常。

假设您将传感器添加到一件设备中，该设备可以从旋转轴上监视振动。如果声音振动突然在认知计算系统确定正常状态的外部之外，则可以在发生完全零件故障之前自动提醒操作员，这可能会破坏整个机器。

At this point the equipment can literally schedule its own service call or even turn itself off before a catastrophic failure occurs. That's the power ofedge computing.

提高流程，质量和运营效率

随着系统连接到认知计算应用程序，可以针对制造业的性能和结果分析大量的过程和控制系统变量。

For example, the cognitive computing system can draw correlations between process variables like solder temperature and yield levels of a manufacturing run.

Systems like the IBM Watson IoT Platform can predict failure earlier than statistical process control and can mash up new data sources, like images and audio, to determine fault causes from non-traditional data sources.

Increasing manufacturing resource intelligence

Cognitive computing can make manufacturing resources more intelligent, from maintaining raw material quality to keeping people like line technicians and operators safer.

Tool availability and replacement part stock levels, for example, can be automatically tracked with sensors that feed information into cognitive computing systems.

The real promise of cognitive computing is getting to data-driven outcomes that actually effect change. That means we'll have less trial and error and more application of actual data collected from the real world. This data, coming through sensors and systems, will identify and autonomously address a problem, maybe even before it happens.

但是，我们如何为这些高级认知计算系统提供对我们制造设备生产的所有数据的访问？

Right now the majority of the data that exists in the world—the data our cognitive computing systems want to access—是所谓的dark data.

黑暗数据是当今世界上存在的信息，但嘈杂或以我们无法轻易分析的格式，至少是我们自己的大脑。这就是认知计算发挥作用的地方。

认知计算可以比我们的大脑更快地分析大量数据，以及我们可能无法识别的点相关性和模式。

Which is important, because today there’s a lot of dark data out there. Every day about 2.5 quintillion bytes of new data are generated. Researchers at IBM estimate that about 80% of that data is dark.

An example of dark data could be data from a manufacturing system that’s controlled by a non-standard or proprietary control system that cognitive computing systems can’t easily tap into.

那么，我们如何获得连接到制造系统黑暗数据的认知计算系统？

了解有关如何将数据从制造系统连接到认知计算系统（例如IBM Watson IoT平台）的更多信息Opto 22 IoT technology video.