The outlook for industrial maintenance can seem daunting. Maintenance teams are increasingly plagued by severe labor shortages – particularly a lack of experienced technicians and a steady draining of institutional memory as the older generation reaches retirement age. Industry 5.0 may be able to provide solutions.
At the same time, just about every sector is operating under greater pressure than ever before, with less room for mistakes or unplanned downtime. Maintenance teams are on the front lines of this new reality.
Despite this, the future of maintenance is bright, thanks to a new revolutionary approach that tackles these problems with both people and technology: Industry 5.0. The next phase of the industrial revolution was agile, connected, and cooperative.
It harnesses the power of Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT) so that maintenance teams can operate more efficiently, even when they face labor and expertise constraints.
Industry 5.0 uses connectivity and smart data to reduce waste and cut costs while keeping equipment up and running for much longer. New technologies that enable predictive maintenance, for example, are becoming increasingly popular. The global market for these products is growing – by 2024, predictive maintenance alone will be a $23.5 billion industry, according to IoT Analytics.
Now is the time to learn how Industry 5.0 technologies, from AI to IIoT and predictive maintenance systems, are changing the industry.
The Emergence of Industry 5.0
The fifth industrial revolution, or Industry 5.0, is a new method of working. Its goal is to help every operation reach its maximum potential by allowing workers to focus on what they do best. Workflows move back and forth between humans and AI so that lean maintenance teams can give a higher level of care to more assets.
On some level, this will feel familiar to teams that have already joined the fourth industrial revolution and have been using a predictive maintenance strategy in their plants. Predictive maintenance uses vibration sensors to monitor critical assets continually.
Sensors pick up on changes in equipment vibration patterns or temperature; even tiny changes can indicate that a machine is developing a new defect. The sooner that maintenance teams spot these defects, the easier it is to make repairs.
Predictive maintenance and condition monitoring save maintenance teams time and costs. These Industry 4.0 maintenance technologies keep critical assets up and running for longer, and they improve productivity wherever they’re applied.
Once they have implemented predictive maintenance, 91 percent of businesses reduce repair time and unplanned downtime, CXP Group found. Industry 5.0 takes the predictive maintenance model and combines it with artificial intelligence to present a new concept for maintenance teams.
Extending the Reach and Scope of Condition Monitoring
Today, cloud computing and low-cost, high-performance edge technology are already making vibration sensors an affordable choice for most plants. Wireless technology also makes constant connectivity easier than ever before.
Modern teams can share data, feed it into analytics programs, and compare it to historical data at an astonishing speed. Workers at one plant can share data with decision-makers thousands of miles away. Experts can guide the clock.
The cloud, combined with modern CMMS software, keeps teams on the same page, breaking down silos and enabling consistent, far-reaching proactive maintenance strategies.
It’s Not Just Using the Same Approach in More Locations
Industry 5.0 also means expanding the use of analytic tools so that workers aren’t overburdened sifting through piles of data. Advances in AI and data analytics mean that cloud-based CMMS software can filter data, spot anomalies, and make recommendations about how to address them. Then, a human expert can come in and analyze the problem.
“By having AI assisting a human being, we make sure that maintenance teams are optimizing where they’re applying their expertise,” explained Aaron Merkin, Chief Technology Officer at Fluke Reliability. “There will always be a need for human expertise. But as plants grow larger and more complex, there simply aren’t enough hours in the day for workers to do everything. Algorithms cut out some of the preliminary work so that human experts can focus their energies on the toughest problems.”
Those algorithms are game changers. That’s the level of capability that many are calling prescriptive maintenance.
Prescriptive Maintenance
Prescriptive maintenance builds on the success of predictive maintenance. Instead of simply predicting breakdowns, prescriptive maintenance can diagnose the root causes of machine defects and can make recommendations for maintenance teams to follow.
John Bernet, Mechanical Application and Product Specialist at Fluke Reliability says that many maintenance teams misunderstand how the data works. “There’s a common misconception that data is nothing more than a number,” he says. “And if I start collecting data from a machine, it will give a baseline of what is good, and then once that number gets above a certain baseline, something is wrong. Then, we can go out and do some work to fix it.”
Of course, Bernet explains, it’s not that simple. Data doesn’t exist in a vacuum. It’s only valuable when it’s contextualized. Vibration levels mean different things depending on the asset, its age, and even its location.
Until recently, most analytics programs couldn’t make those distinctions. Today, sophisticated algorithms can look beyond the raw numbers and make determinations about specific assets.
This isn’t something that an algorithm or a CMMS can do on its own. Prescriptive maintenance requires ongoing input from human experts. The more input the algorithms receive, the more effective they get at spotting patterns or diagnosing issues.
Plants don’t have to have a deep bench of experienced technicians or data analysts. The same results can be achieved by outsourcing to experts in another location or partnering with a company that offers condition monitoring services, such as Fluke Reliability.
The Key to Industry 5.0: Smart Data Before Big Data
Not long ago, the maintenance industry was advised to collect as much data as possible. Big Data was supposed to be the big focus. Unfortunately, too many businesses today are overwhelmed by the data and struggling to extract any meaningful insights. The future of work doesn’t look like more data – it looks like smarter data.
Instead of constantly streaming every available piece of data, plants need to focus on actionable information that can be used to reduce downtime and cut costs. Sensor data can be a good example of “smart” data because it has an immediate, clear purpose.
The shift to smart data also means thinking about storage. Adopters need to consider how long will each piece of data hold its value. Some information – like vibration measurements and temperature – is useful throughout a machine’s lifecycle since it can point to a pattern. But other information loses its value over time.
It’s a good idea to work with experts on building a data collection and storage plan. The bottom line is that data collection isn’t the end goal. The end goal is for maintenance teams to collect actionable data that makes their job easier and that makes the plant run more smoothly.
Changing the Future of Maintenance
Industry 5.0 will look a little different for each operation. The future of maintenance is flexible, personalized, and highly adaptable.
A one-size-fits-all solution is not suitable and maintenance teams should take the time to consult with experts, learn everything possible, and take the solutions that work best for that operation. With the implementation of Industry 5.0 solutions, plants can expect to see dramatic improvements to both uptime and overall productivity, as well as improvements in job satisfaction amongst maintenance teams.
- Artificial Intelligence
- Data Analytics
- Industrial Automation
- Industrial Internet of Things
- Predictive Maintenance
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