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IoT: the biggest challenges facing its future

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What are the core predictive maintenance trends 2022 and beyond? We’ll look at the emerging tech that can make your in-service equipment smarter, proactive and more directly connected with your maintenance resources and management teams.

Predictive maintenance refers to the use of data-driven, proactive maintenance methods that are designed to analyse the condition of equipment and help predict when maintenance should be performed. Previously, this would have been manual inspection and servicing-based, but now, computers can automate much of this work for cost-benefit savings. According to McKinsey & Company, AI-based predictive maintenance can boost availability by up to 20% while reducing inspection costs by 25% and annual maintenance fees by up to 10%. But how will this change? What are our predictions for predictive maintenance trends in 2022? Here’s what we think.


AI-enhanced human skill

Artificial intelligence will continue to do what it does best – interpret data using consistently improving algorithms in a much shorter time. And the people employed at the [organisation] will use these insights and help their companies scale their existing technologies and processes to operate at a faster, much more efficient rate and at scale. So, in predictive maintenance, the robots aren’t coming for your jobs. They are coming to help you do your jobs better, faster and more accurately.


The rise of the smart factory

According to IIoT World, if we define “smart factory” as a facility utilising technology that enables management to make smart decisions based on data and insights, then predictive maintenance has definitely got to be a key component in getting there. Tools that make predictive maintenance possible can transform raw data into actionable insights, which in turn, lead to informed decisions. And that’s largely the deployment of connected sensors, machine learning and artificial intelligence support in tandem with easy-to-use interfaces for human operators on the ground or remotely. If you’re keen to explore what a smart factory would look like for your organisation, in the first instance talk to a company like konektio.


Government spending increases globally

FedTech reports that in the US public federal spending on AI/ML rose to nearly $1 billion in fiscal year 2020 — a 50 per cent increase from fiscal year 2018 — making it one of the fastest-growing, emerging technology investment areas. And that’s only expected to rise as the impact of the last two years continues to resonate across economies globally.


Gains in quick problem identification

A lot of time continues to be wasted searching for the exact failure point in a system or piece of equipment. However, that scenario is changing. There are products available that identify the specific sensors on a machine that are experiencing issues. That makes it far easier for a maintenance professional to start working on the problem efficiently and quickly. Additionally, these products often calculate the estimated impact of an identified issue. So that leadership can prioritise business critical remedial works over less impacting issues.

Certainly, 2022 will see more sophisticated solutions emerge
in the field of predictive maintenance with more use
cases identified.

“AI-based predictive maintenance can boost availability by up to 20% while reducing inspection costs by 25% and annual maintenance fees by up to 10%”