Predictive Maintenance With Industrial Networks

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..

Let's examine how condition-based data,condition monitoring and diagnostics have all changed our world in the networked environment..

Condition-based data is gathering of information in real time on the state of affairs occurring on a machine.This data comes typically from low-level sensor devices on the plant floor and then,in the Industry 4.0 world,is fed back to the programmable logic controller (PLC) via IO-Link (a rapidly growing communication protocol).Such data can also come from the valve driver used on a pneumatic valve manifold,where process and parameter data is collected by the valve driver,also known as the network node..

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..

Every manufacturer offers different amounts and types 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 is distinct from condition-based data because condition monitoring searches for a change of state over a given period of time.Condition monitoring of plant equipment is often defined by a change of state in heat,acoustics and vibration.If it's louder and running hotter,you know its condition has changed—a likely indicator of wear.In industrial networks,condition monitoring can include cycle counting,cycle time changes and temperature rise for potential signs of impending failure..

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..

The Industrial Internet of Things (IIoT) brings with it many great functional and diagnostic tools.Manufacturers are building integrated smarts into valve drivers and network nodes.Integrated sensing is now available in many products or as add-on options.Ensure that you understand the function of these diagnostic capabilities to incorporate their use and don't forget to look at your safety network for hidden problems that can be turned into big cost savings with little expense or effort.Think big picture on how you can incorporate the technologies available across your existing equipment for a quick IIoT upgrade..

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科幻巨作背后的科学
施耐德电气Smart HVX 智能中压断路器
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