As its name implies, predictive maintenance is a prediction of early failure that guides your maintenance schedule and reduces downtime. In the past, predictive maintenance was merely a hypothesis in many plants and usually resulted in reactive maintenance instead of the proactive approach. Today, we take the guesswork out of predictive by basing our decisions on sound facts—on data we gather and process to drive our conclusions. Thanks to Industry 4.0 and advancements in industrial products, we’re now armed with the data to make better decisions.
让我们来检查如何在网络环境中改变了基于条件的数据，条件监控和诊断。
Condition-based data正在实时收集信息的现状。该数据通常来自植物地板上的低级传感器设备，然后在行业4.0世界中，通过IO-Link（快速增长的通信协议）反馈到可编程逻辑控制器（PLC）。这些数据也可以来自气动阀歧管上使用的阀驱动器，其中阀驱动器收集过程和参数数据，也称为网络节点。
Process data (also called cyclic data) is fed back to the PLC at regular intervals. This is your need-to-know-in-a-hurry data that you want to monitor, such as temperate warnings, over-voltages and shorts.
Parameter data (also called acyclic data) is nice-to-have data embedded in the electronics and must be retrieved if needed. Examples of parameter data include cycle counting and specific information such as which valve coil is shorting out.
每一个制造商提供不同数量和泰pes of process and parameter data. Therefore, a sound understanding of the diagnostics available on your in-plant equipment is essential to building your Industry 4.0 predictive maintenance strategy.
Condition monitoring与基于条件的数据不同，因为条件监视在给定的时间段内进行状态的变化。植物设备的情况监测通常由热量，声学和振动中的状态变化来定义。如果它更响亮，更热，你知道它的病情发生了变化 - 可能的磨损指示。在工业网络中，条件监测可以包括循环计数，循环时间变化和潜在的即将发生迹象的温度升高。
To make data acquisition work effectively, two areas that should not be overlooked are sensors and machine safety.
Sensors provide data. For example, continuous position sensors lend themselves well to diagnosing a cylinder with leakage and wear. Sensors provide great data at low cost and can literally be a lifesaver in hard-to-reach areas. With so many cost-effective sensors on the market, why not integrate a few into your predictive maintenance plans? Consider flow and pressure sensors, continuous-position sensors and even simple analog sensors. This way you’ll get a warning through the network that something has malfunctioned, along with the exact address of that sensor, which makes things much easier.
安全tells us a great deal about our equipment. How many times has the light curtain tripped? Why? Do production line workers hit the e-stop button frequently? Why? All of these things point to larger problems that result in wasted time, higher scrap yields and potential injury. Companies that monitor safety can uncover the underlying issues. Gathering cycle count data for a light curtain, for example, might bring surprising results. By determining the root cause of the breached light curtain, you can shed further light on production issues. Putting safety on the network allows you to monitor and manage such activity. Some manufacturers offer safe power-capable devices that apply safe power to components so that in the event of an emergency, power can be disconnected but communication remains on. There’s a great deal of value in thinking about safety on machine and over network. Gathering diagnostics can also ease some of the regulatory compliance requirements for reporting as required in larger facilities when the Occupational Safety and Health Administration (OSHA) wants to validate your compliance.
工业互联网（IIT）带来了许多巨大的功能和诊断工具。制造商正在将智能集成智能集成到阀门驱动程序和网络节点。综合感应现在可在许多产品或附加选项中提供。确保您了解这些诊断功能的函数来合并其使用，不要忘记查看您的安全网络以进行隐藏的问题，这可以以很少的费用或努力变成大成本节省。思考大局如何将现有设备中提供的技术纳入快速IIOT升级。
For more information, visit Parker Hannifin atwww.parker.com.
