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INDUSTRY 4.0/IIoT A NEW LEVEL OF MAINTENANCE
Dan Rossek, regional marketing manager at Omron, explains how to improve your maintenance strategy by accessing and repurposing data that already exists
a wider ecosystem, even the failure of a single component can impact on productivity across the enterprise – affecting upstream and downstream operations. It can also have an adverse effect on product quality or could result in costly product losses. Ultimately, it can lead to lost revenue and damage to brand reputation. The most common cause of machine
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fatigue is mechanical component failure; for example, bearing wear, air cylinder failure, misalignment of guides and machine jams. Too many manufacturers still rely on the most basic maintenance strategy – run-to-failure – and experience unexpected downtime events as a result. A better solution is preventative
maintenance, where components are swapped out at set times in line with their expected operational lifetime. While this reduces unplanned stoppages, the downside is that the component may still have more life left in it, meaning that a preventative strategy does not always align well with an organisation’s sustainability goals. Replacing components before they reach the end of their useful lives can also be a costly solution. As many industries start the journey
towards smarter factories, it is also important to keep in mind the human impact of maintenance. Digital solutions can offer many benefits for maintenance teams, helping to relieve the huge pressure placed on them when there is a stoppage. The time taken to get a system back online will dictate how costly the downtime is to the enterprise. This can lead to time being prioritised over the quality of a fix, resulting in frequent machine stoppages. While it must be accepted that no
maintenance strategy can guarantee 100% machine uptime, both preventative and predictive solutions can help reduce the physical pressures on the team, as fixes can
38 DECEMBER 2021/JANUARY 2022 | PROCESS & CONTROL
nplanned downtime is the enemy of production, and because most manufacturing systems work as part of
Data can elevate the maintenance function from being a quick fix to keep production lines running, to being a vital tool for enterprise success
be planned in advance and undertaken during scheduled downtimes. In today’s digital world, predictive
maintenance can be achieved more easily than you may think. Essentially, a predictive maintenance strategy can utilise already- available data from components and analyse this information, highlighting spurious signals which can indicate a potential failure. For example, most components will run at a baseline vibration level or a regular operating temperature, monitoring these elements makes it possible to identify changes, which might be due to a component starting to fail. Looking more closely at the vector or velocity of change makes it possible to predict a timeframe to failure. The first step on a predictive maintenance
journey is to identify critical elements of the process. What historically are the major causes of unplanned stoppage and where on the system do they occur? This will enable you to create a baseline of the data collection capabilities of your systems. Often, the critical elements will be motors
and mechanical elements such as air solenoids. Adding predictive maintenance solutions onto these components is usually an easy task, and often there is no requirement
for networking to wider systems. Furthermore, there is usually no need to
add more technology, just data recording capability. Many inverter drives, for example, will already include current monitoring functionality, so the data is available. If your inverter doesn’t have this functionality, a simple solution is to deploy current transformers onto the motors. Vibration sensors can identify increased or changing levels of vibration which can help predict a failure, while air solenoids contain position sensors which can be used to determine the condition of the device by analysing the time it takes to retract and extend. One challenge, however, is understanding
that there is a need to interpret the data differently. For example, most machines will record the stop and start positions of the solenoid, but for predictive maintenance, it is necessary to record the time it takes to go from one point to another. This time should be consistent and any variance may indicate an issue with the cylinder. With your data gathering solutions
identified, the next step is to assess data collection options. Some ageing control systems will have no data capture capability, so a good solution is to add a secondary layer of automation architecture to collect data and interface to the supervisory system. This is not a costly undertaking, as there is no requirement for any control functions. There are various levels of predictive
maintenance, and once you start receiving huge amounts of data, the levels of complexity will increase. It can also be difficult to filter out readings that relate to normal interruptions that can cause data fluctuation – periodic loading of a machine, for example, can cause a spike in vibration, or there may be a repetitive process action which causes increases in vibration and thus
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