March 2020

scalable means of determining maintenance schedules. This eliminates some of the guesswork from maintenance schedule, which means that plant managers can devise maintenance schedules that are more strategic. For example, a SCADA system might pick up a dwindling performance in a motor. If the SCADA system is connected to an MES, the plant manager can easily access this information remotely and make maintenance of that motor a priority. For perfectly healthy systems, they can be maintained only when their performance data indicates they need it. The only downside to this is that if a plant contains thousands of connected data sources, it can simply be too much data for a plant manager to reasonably analyse to determine the ideal maintenance schedule. More advanced MESs resolve this problem, a good example being GE Digital’s Predix, which incorporates machine learning artificial intelligence (AI) into the system. This AI can identify correlations and trends in data sets, allowing it to alert managers when maintenance should be conducted on a system. In effect, the AI learns the indicators of poor equipment health and can facilitate a shift to a predictive maintenance model, reducing unnecessary labour and time usage. Although this makes life easier for plant managers, it does little to simplify the maintenance process itself for engineers. This is where AR comes in, by using the data within the MES and a purpose-built industrial internet of things (IIoT) platform to reimagine maintenance. Bringing AR to dairy A contributing factor to the complexity of 20 | Plant & Works Engineering www.pwemag.co.uk March 2020 Maintenance Matters Focus on: Maintenance Software maintaining some systems is a matter of design. Engineers need to know the most efficient and easiest way of accessing the components that need attending to, and this is not always an easy task — not least because it requires prior knowledge of the specific parts that are under- performing. Take a milk pasteuriser for example. If the problem is that the flow rate of milk is lower than it should be, it could be a problem with the centrifugal pump, the valve or even the flowmeter measuring flow. With enough performance data from each of these parts, maintenance engineers can easily know which to inspect. And what better way to access this data than a digital overlay showing real-time performance data of each part? This becomes possible with an industrial AR application like those available through PTC’s ThingWorx 8 IIoT platform, offered by Novotek UK and Ireland. Sean Robinson, service leader at food and beverage digitalisation specialist Novotek UK and Ireland says this platform allows dairy engineers to build apps that are specifically designed for their plant and applications, ensuring that the app is suitable for any set up. With that in place, Robinson comments that maintenance technicians and engineers can either use AR headsets or their mobile phones to access the application. He explains that by simply holding their phone up to a pasteuriser, engineers could see real-time performance data and could zoom in deeper to see specific parts. With a virtual representation of the pasteuriser’s centrifugal pump on the screen, engineers can inspect and identify if it is the part causing problems. If it is, Robinson says the AR app can show the easiest way to access and maintain it. And if a problem is particularly puzzling and the maintenance engineer isn’t sure how to address it, AR applications, according to Robinson, make it possible for specialist technicians to remotely view and advise on the issue. This encourages the sharing of specialist knowledge and improves the effectiveness of overall plant maintenance. The value offered by AR is applicable to almost every connected system in a dairy plant. Let’s say that a dairy plant’s manufacturing execution system highlights that a rotary evaporator, used to standardise the dry matter of milk in the early production stages, requires maintenance. As the evaporator consists of several components, a maintenance engineer could use AR to see a virtual representation of the components in the evaporator and identify which needs attending to. By using a purpose-built AR application, the engineer can view real time system data from the ThingWorx IIoT platform and see which components are performing inefficiently. In this case, it could be that the evaporator’s compressor requires lubrication. The engineer can then resolve this in the least disruptive way possible, minimising the impact that necessary maintenance has on production. Crucially, this technology maximises uptime and improves overall equipment effectiveness in the most efficient, effective and easy way possible. If a dairy manufacturer is looking to make their operations as lean and efficient as possible, AR seems like the ideal tool to help achieve precisely that. For further information please visit: www.novotek.com/uk/