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Digital tools to keep maintenance going during a pandemic


By Simon Jagers

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We’re now five months into the worldwide pandemic and even though some parts of the world are starting to see initial Covid-19 effects fade away, serious changes and restrictive measures are still here to stay–at least for the foreseeable future. The same applies to every industry around the world, including manufacturing and process industries. Even though we see positive signs of global manufacturing production steadily rising week by week (the latest figures show 94% of pre-pandemic production rates; see figure 1), there are still challenges and obstacles in place making it difficult to perform plant reliability and maintenance operations. For one, the number of workers is limited to ensure minimal employee contact on the floor.

Figure 1 Global manufacturing activity data set, according to Plex. Source: Jerry Foster (2020)

This leads us to a question: “How can you as a maintenance manager ensure that critical equipment won’t fail and disrupt already weakened manufacturing output?”

The answer might lie in the recent results of research studies performed across the globe. One of the sources concluded that the mention of adopting remote work-enabling technologies has increased by a factor of 10, compared to pre-Covid levels (figure 2).

Figure 2 What CEOs talked about during the pandemic, according to research by IoT Analytics. Source: Knud Lasse Lueth (2020)

A recent IFS survey on digital transformation investment indicates that despite the crisis, 52% of companies across the world will be increasing their spending on digital transformation. And what’s even more interesting, of those surveyed the ones who were more concerned with economic disruption were 20% more likely to spend on digital (figure 3). These results are not unfounded: companies utilizing digital technologies make better and more informed decisions. This also makes them better-positioned to rebound once the crisis is over.

Figure 3 Digital transformation investment in 2020 and beyond, according to global study by IFS. Source: IFS (2020)

So what digital tools are available for maintenance and reliability professionals to help you weather the storms of uncertainty and emerge even more prepared once operations scale up? We present three digital tools and methodologies, powered by artificial intelligence (AI) and machine learning, to help you make your work better, smarter and more efficient.

Traditional engineering methodologies scaled by analytics

Failure mode, effects and criticality analysis (FMECA) is not a new concept within the maintenance realm. It’s a very versatile methodology that has been adapted for many different purposes, including to optimize maintenance strategies and failure prioritization. 

Intelligent analytics tools upgrade FMECA with the ability to filter the data by assets, locations and operating units. Additionally, calculations for criticality parameters are automated and constantly being fed by real historical data stored in your computerized maintenance management system (CMMS). This allows you to make quicker and better decisions than if you were to do it manually in spreadsheets, and helps you track trends over time to prevent certain failure modes from future occurrence. Connection to the historical data in your CMMS and automated calculations definitely make intelligent analytics a remote work-enabling technology, as you can now perform parts of this analysis from the comforts of your home, and plan inspections and corrective work for when they are truly needed.

Mobile CMMS

One digital tool that could mitigate labor constraints on the plant floor is to take your CMMS mobile. Most industries these days make use of a CMMS to assign and track work orders, plan inventory and schedule preventive work. By adopting a mobile CMMS tool, technicians have all of the necessary information at their fingertips–saving them an unnecessary trip across the plant. Additionally, built-in communication features allow technicians to easily get in touch with other members of the team for advice–even if they’re working from home that day. These tools can ensure that safe distancing rules are followed within plants and mills by keeping minimal essential staff on the floor, and having the rest work remotely.

AI-based predictive maintenance

One of the most essential tasks in maintenance and reliability is to ensure that critical assets are available at all times to drive production forward. This is even more crucial now, when any equipment failure can shatter already crisis-affected production and throughput–leading to thousands if not millions in losses. But how do you continue monitoring and maintaining critical assets when there’s a limited workforce and cost efficiency dominates all discussions?

That’s where modern condition monitoring technology powered by AI and machine learning algorithms enters the stage. It makes it possible to collect and analyze asset health data 24/7, in order to predict weeks in advance when the equipment will fail. And the good news is that it’s all done automatically–no need for technicians to manually go through terabytes of data. This also saves on routine inspections and “just in case” maintenance, freeing up time to apply engineering expertise where it matters the most. Most importantly, this monitoring can now be done remotely. Maintenance personnel can track how equipment is faring from home, and if the system issues an alert, plan an onsite visit in advance. All of this leads to a more efficient and productive workforce, saving costs and time under Covid-19 restrictions with no sacrifice of quality, reliability or safety. 

Additionally, a new generation of condition monitoring systems based on motor current signature analysis (MCSA) have raised sensitivity and accuracy–detecting well over 90% of critical equipment failures (both mechanical and electrical) up to five months in advance. These systems are also much easier to install and scale up, as the sensors install inside the motor control cabinet (MCC), not directly on the asset. This gives maintenance and reliability staff even more power to monitor critical equipment remotely, especially if that equipment is in a hard-to-reach place or an ATEX zone, and to schedule repairs and replacements well in advance and only for the assets that really need care.

In conclusion

These three digital innovations create outstanding opportunities that could have long-lasting effects even after Covid restrictions lift (assuming they don’t forever change the way we operate). They not only enable operations to maintain production levels but also protect the company’s workforce. By accelerating the use of these AI and machine learning-based technologies, companies and their maintenance teams can emerge from the pandemic with operations that are even safer, smarter, more efficient and more resilient.

Ready for more? Download the business case for predictive maintenance to find out more about how AI-based PdM could benefit your organization during and after the pandemic.

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