How AI can improve asset reliability – interview with Fluke Reliability

Information Age speaks to Fluke Reliability president Ankush Malhotra and CTO Aaron Merkin on how AI can assist their customers to improve the lifespan of their industrial assets

If you’ve ever peeked into the toolkit of an electrician, factory engineer or lab technician – anyone who works with industrial equipment – you may have come across a yellow-cased instrument from Fluke.

The 75-year-old company specialises in industrial instruments and solutions. Its tools can monitor the power, temperature and vibrations of industrial equipment to detect any mechanical problems or wear.

Here, Information Age speaks to Ankush Malhotra and Aaron Merkin of Fluke Reliability – a business unit of Fluke which focuses on reliability and asset longevity using maintenance, critical data collection, alignment and machine learning and enable customers to adopt AI solutions to shift from reactive to predictive in their operations.

Throughout history, every great “advance” is perceived by the existing workforce as a threat to its livelihood, especially AI.

But there is an argument that they tend to create more job opportunities than they replace. What is your view? Is the evolution of AI likely to exacerbate, or mitigate, the global skills shortage?

Malhotra: “What we’re seeing now with AI – specifically generative AI – is from an impact and magnitude perspective as big as electrification and the introduction of the internet. It is huge and generative AI has made AI a lot more pervasive and accessible.

“In our context where we serve industrial facilities and improve outcomes for customers– which includes asset uptime, more sustainability, efficiency and driving longevity in their machines – we think there is a huge opportunity for AI.

“You have aging assets and there is a tremendous pressure to extend the longevity of those assets from a sustainability perspective and because they are expensive to replace. There’s also a huge labour shortage – 3.5 million manufacturing jobs are needed over the next decade and 2 million of these will go unfulfilled because the labour force will not have the skills required to do the job. AI is not going to take away jobs, it’s going to fill those gaps we aren’t able to fulfil.”

“It will also provide value in the form of copilots.  Take Azima DLI for example – our recent acquisition brings AI-powered vibration analytics and remote condition monitoring solution into our connected reliability offering, which helps customers make the shift from reactive to AI-enabled predictive maintenance.

Ninety-three per cent of the data ingested into Azima’s diagnostic library does not need human intervention – but that seven per cent does. We leverage technology but it doesn’t take the human away.”

Connected reliability: can you explain that concept in more detail? What are the benefits for companies and do you have customer examples of this in practice?

Merkin: “If you think about all the data that customers have in manufacturing, there’s a variety of different systems – connected reliability is about collecting all those systems and bringing them all together for better insights for customers.”

Malhotra: “One example of where we can see this concept in action is at Jack Daniel’s cooperage. They manufacture the barrels for Jack Daniels whiskey. The creation of these barrels produces a lot of dust, so they use vast dust collection systems which have to remain up and running – or production is halted. It’s critical this business doesn’t experience any downtime, as that can lead to waste or loss of production that could cost them hundreds of thousands of dollars. For this, we’ve deployed smart vibration sensors which collect data, send that to the cloud and reports any anomalies, then generates a work order within our CMMS eMaint for maintenance teams to fix it before any breakdown occurs. This is an example of how connected reliability connects workflows to drive asset efficiency and uptime.”

Breakthroughs in the field of AI, including machine learning, have the potential to drastically change the way we approach our working lives and you’ve no doubt seen it have an impact on both your business and that of your customers.

Is it noticeably changing your customers’ expectations, and how is it benefiting your customers specifically?

Malhotra: “It’s about collecting data – we’ve got trillions of data points on over 80,000 machines so we’re able to use the algorithms and data to predict when they will fail and why they will fail, and we can advise customers to take corrective action. Azima has been doing this for 30 years. It’s not using generative AI, but it is using machine learning.  

“For us, if you ask our customers if they want reactive maintenance or predictive maintenance – they’ll all choose predictive. There’s almost a hunger to do more predictive maintenance as it has a knock-on effect to so many other things, like spare parts planning and forecasting costs – this technology is allowing us to offer that as a solution. I think there’s an expectation and a clear need to do things more effectively to drive outcomes and I think traditional machine learning and generative AI are both helping our customers do that.”

Sustainability is pervasive in almost every new industrial planning initiative.

Are you seeing a shift in customers talking about sustainability alongside technology advancements? How are you working with customers to help achieve those sustainability goals?

Malhotra: “From a governance, shareholder and stakeholder perspective there’s a clear need for companies to be sustainable but it’s also the right thing to do. We see that shift, we see that more in some geographies and industries than others and that’s natural.

“For me, we have a role to play. How do we build the tools and software to enable our customers to meet those sustainability goals: to use their machines longer, ensure they’re more energy efficient and improve the longevity of the machines. It’s our job to educate our customers to see the connection between effective asset management and utilisation and sustainability.”

Merkin: “We’re very proud that 100 per cent of our portfolio contributes to sustainability goals. By being predictive rather than reactive, you’re reducing the damage to the assets and therefore reducing the amount of spare parts and commodities required – it’s about producing more with less. The key to well-performing machines is to have them properly aligned so you’re not having unnecessary friction and vibration.”

Everyone has been talking about Industry 4.0 alongside the IIoT, while some believe it is a made-up concept that doesn’t hold much ground. What are your thoughts here? Can we expect an Industry 5.0?

Merkin: “Industry 4.0 was about the incorporation of data in classic AI to provide better insights. Industry 5.0 is about the next generation of that. Whether that’s generating a data dashboard or incorporating copilot technology to provide better instructions on how a worker can perform tasks more effectively.

“For example, if I have someone who has worked on an asset for 30 years, that’s something they can do in 90 minutes. If you have someone new, they will be less efficient. When companies lose that expertise, you’re increasing the risk where what should be corrective maintenance, is actually doing damage to the asset. By using generative AI technologies, it can sweep the expertise so you can improve the efficiency of the outcome and upskill whilst doing so.

“Whether it’s Industry 4.0 or 5.0 – there will always be a hype with these sorts of things, but fundamentally it is a very real thing that is coming and will be providing great benefit to our customers. We want to be a part of that.”

Ankush Malhotra and Aaron Merkin are the president and CTO of Fluke Reliability

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Dom Walbanke

Dom Walbanke is a feature writer for and Small, focused on matters concerning start-ups and scale-ups. He has also been published in the Independent, FourFourTwo magazine...