In recent years, economic pressures have increased across virtually every industry, leading to a greater emphasis on efficiency. In order to stay competitive, organizations need to produce high-quality goods quickly and reliably, while operating on a lean budget and with a reduced labor pool.
Economic uncertainty has increased the emphasis on maintaining machinery rather than replacing it. This has led to far greater adoption of connected tools like industrial Internet of Things (IIoT) sensors and AI analytics. Measuring asset health ensures plants can maintain and repair assets before they break down, keeping costs lower overall and continually meeting productivity targets. It also enables plants to run efficiently even when they are facing labor shortages.
A recent report1 revealed AI will be a high business priority for 93% of companies over the next 12 months. This is echoed by 90% of senior decision-makers and over 80% of maintenance managers. Currently, only 8% of respondents use a predictive maintenance strategy, but 77% aim to shift to predictive/proactive maintenance, viewing AI as a key tool for this transition.
Predictive Maintenance & Operational Efficiency
Predictive maintenance is a proactive strategy for staying ahead of machine failure and setting maintenance priorities. At its core, the strategy is simple: Instead of waiting for machines to fail before doing repairs, maintenance crews monitor key assets around the clock. By tracking and analyzing asset vibration levels, temperature and sound emissions, technicians can catch the first warning signs that an asset is developing a new defect. Predictive maintenance allows crews to stay ahead of machine failure and correct faults while they are still minor. It dramatically reduces downtime and allows managers to schedule repairs when they are most convenient.
But predictive maintenance would not be possible without today’s state-of-the-art digital tools, like IIoT sensors and analytic software. Leveraging the power of connected sensors and AI-driven analytics takes predictive maintenance to the next level.
Not only does it allow organizations to track the health of more assets, but advanced algorithms mitigate the risk of fault misdiagnosis, avoiding delays in troubleshooting and empowering maintenance teams to make the right decisions at the right time.
The Power of AI
AI excels in two key areas: automation and precision. That is part of what makes AI so well suited to predictive maintenance.
AI and automation
The typical modern plant is drowning in data. In fact, most managers say they do not have the time or the dedicated workforce to sift through all the asset health data they are collecting.
AI-enabled software can read through reams of data at an incredible pace. The best AI software can accurately diagnose hundreds of distinct machine faults using condition monitoring data—all without human help. This leaves technicians free to focus on more complex jobs that AI intelligence is not capable of doing.
For teams operating with a limited workforce, this is a game changer. Using AI to sift through condition monitoring data means teams will not miss any important information, and technicians will not get bogged down with tedious jobs. Using AI to automate processes prevents human error, saves money and makes it possible to scale maintenance approaches.
However, it is important to remember that AI is not only for automating processes. The best AI tools can carry out complex diagnostics and help set maintenance priorities, too.
AI and precision
A good AI diagnostic engine is meticulous and precise. It sifts through condition monitoring data and catches even the smallest deviation from a machine’s baseline. Any deviation in vibration levels or temperature can indicate a new or developing fault in the machine. If there is a change, AI will notice it.
But next-generation AI tools go beyond flagging high vibration levels and issuing alerts. Today’s AI can carefully study condition monitoring data in context and compare the data to work order history, the age and utilization of the particular asset and even environmental factors. The result is a highly precise, reliable diagnosis.
What About Prescriptive Maintenance?
Predictive maintenance can save teams time and resources, but the next step forward is even more exciting: a strategy called prescriptive maintenance.
Prescriptive maintenance uses software and sensors to diagnose the root causes of machine defects and make detailed recommendations for maintenance crews to follow. Prescriptive maintenance does not just predict machine breakdowns; it looks for ways to keep equipment running at peak performance by recommending steps to resolve predicted issues and extending the time between events.
It is a double win for efficiency. Using AI takes the pressure off of technicians and gives them the ability to focus on
more complex tasks and problem-solving. And when AI helps set maintenance priorities, it ensures maintenance crews can make the best possible use of their time and resources.
Implementation: Where to Begin?
- AI and connected IIoT tools are here to stay. Integrating them into maintenance practices can help increase efficiencies and maintain a competitive edge by:
- Enabling predictive maintenance so teams can stay ahead of machine failure and unplanned downtime
- Reducing maintenance costs by addressing asset faults early
- Scheduling necessary maintenance tasks during off hours instead of interrupting production
- Reducing the burden on technicians
Of course, implementing a new approach can be overwhelming. Starting with a pilot program, instead of introducing the new tools across a whole plant, can be a helpful approach. Begin small and track progress so it is possible to measure exactly what is working and what needs adjustment.
Finally, a word of caution: Not all AI tools are created equal. Make sure to look for an AI-powered diagnostic engine that has been fully and deeply trained on enough data that it can recognize the subtle differences between a healthy asset and a deteriorating one. The best AI engines have been trained on at least a trillion distinct data points. That is what gives them diagnostic precision. Used correctly, AI and IIoT tools can dramatically increase efficiency, extend asset life spans and cut maintenance costs—a big win in all directions.
References
info.fluke.com/frs-0624-download-value-of-ai-lp-1-a