The digital factory of the future places new demands on maintenance

The objective of maintenance measures is to secure technical accessibility for production and ensure our equipment operates when intended, at the right speed and with the right quality delivered. All in all, this places new demands on organisation and expertise for companies which are heading towards Industry 4.0. This is particularly true for machinery which, in many cases, includes dated mechanics as well as brand new connected equipment.

“This requires constant evolution of what we do and how we do it in relation to our maintenance programmes, methods and analyses around deviations, training and organisation,” explains Stieger.

“Although a lot of people claim not to be ready for the digital factory, many have already made the transition, for example, by obtaining process data via sensors. Increasingly sophisticated tools are also emerging, such as thermographic cameras, apps for belt alignment and SKF QuickCollect, which are easy to use and do not require specialist expertise. Smartphone apps enable connection with analysis functions which was not possible previously, and the threshold for scheduled maintenance is becoming lower and lower.”

However, implementing modern, digital production technology does not mean eliminating problems overnight, a fact highlighted by the new World Class Manufacturing facility in Gothenburg which opened last year.

“When you implement a new technology, new discoveries are constantly being made. We learn something new every day. The role of maintenance personnel has changed, from conducting multiple manual inspections to gathering facts in advance on factors which are about to deviate via sensors,” adds Stieger.

The right process data

The journey towards World Class Manufacturing means working in a slightly different way than before. These days, modern machinery contains a lot of process data in the control systems themselves which can be utilised. This can make a big difference according to Steiger, who uses the example of a machine with a sled which moves back and forth.

“The power output should be fairly consistent. If it isn’t, and the sled begins to consume more energy, this may be due to the lubrication or, if much greater output is suddenly required, a small collision may have occurred in the machine. This means things are no longer linear. In the past, perhaps you would continue running the machine until it broke down before carrying out troubleshooting and changing or aligning the sled.”

“Nowadays we try to identify deviations early on and take action before experiencing issues with quality or breakdown. Because our equipment is connected to a growing extent, we have more effective, better connected tools which enable us to, for example, measure and ensure that axles are linear, make sure our equipment is sufficiently lubricated and track the development of temperature and vibration. This provides solid data for determining what needs to be done, and when. The key challenge is not only to identify deviations in the machine, but eliminate the actual underlying cause and ensure the error does not occur again.”

Gaining access to more data from sensors, for example, facilitates analysis, with the key decisions based more on facts. There is no longer the same need to rely on individual experiences or subjective perception. Johnny Stieger puts it like this: “Thanks to reliable data, visualised in real-time, we are able to pinpoint deviations to a greater extent before they impact negatively on production. Nowadays there are tools which facilitate the introduction of Smart Maintenance and improve conditions for more effectively planned maintenance work.”