You May Be AI Ready

If you’ve been employing a proactive maintenance strategy, adopting AI tools should be a seamless and straightforward process.

Sensor-based proactive-maintenance operations are well positioned to benefit from artificial intelligence.

By Michael DeMaria, Fluke Reliability

The maintenance sector has seen a significant transformation in recent years. The industry has faced mounting pressure from workforce changes, increased consumer demand, and supply-chain complications. However, technology has emerged as a beacon of hope, offering intriguing tools that align with the realities of the contemporary workforce. Artificial Intelligence (AI) will be a game-changer in this technological revolution, promising a transformative impact on the sector.

AI’s role in the maintenance industry is not a sudden emergence. Rather, it’s a response to persistent challenges that have troubled the sector. In the past few decades, plants have increasingly adopted IIoT and predictive-maintenance technologies. Today, with the affordability of IIoT sensors and the convenience of cloud technology, maintenance teams are monitoring more assets more frequently than ever.

However, the large volume of generated data poses a significant challenge for human workers. Plants are overwhelmed with more data from more assets, especially as data is being captured more frequently. As operations grow, data increases exponentially, challenging human abilities to sift through and make sense of it all. This is compounded by a lack of resources and/or a lack of expertise.

Here, AI emerges as a savior. Its speed, automation, and pattern-finding capabilities allow faster data processing, thereby transforming maintenance operations and providing powerful insights into risks to production for executives.

AI’s ability to diagnose underlying anomalies, root causes, and emerging faults takes its functionality a step further. It can help managers make informed decisions about scheduling repairs, manage their spare-parts inventory, and comply with relevant regulations. In some instances, AI tools can even guide technicians through the necessary steps to rectify an issue, commonly called prescriptive maintenance, all shortening the downtime.

Implementing AI

AI’s pattern-recognition capability, honed by continuous data intake, enhances its ability to diagnose the root causes behind changes in condition-monitoring data. Additionally, generative AI could soon enable effective communication with human maintenance teams.

This combination of diagnosing faults and providing precise guidance, coupled with effective communication, positions AI as a potential game-changer.

Implementing AI, as with any new strategy, requires careful consideration and a deliberate approach. Strategic teams often start with a small pilot program using a few assets. They establish clear benchmarks and communicate goals well within the team.

Regular tests and check-ins with employees ultimately ensure that there is comfort with the new tools. After a pilot program has been successfully completed, teams have the knowledge and confidence needed to expand their AI implementation.

Maintenance teams that have already adopted predictive-maintenance tools and data-driven decisions are well positioned to start working with AI. For those yet to adopt predictive maintenance, now is the time to make the shift. Getting advice from experts and using their knowledge can help maximize the advantages of this technology and avoid problems.

The Power Duo

Vibration sensors play a crucial role in condition monitoring by capturing essential data on machine health in near real time. By tracking vibrations, these sensors detect early signs of potential equipment failure, allowing proactive maintenance and preventing costly downtime. Wireless sensors provide added advantages in that they typically offer easy installation and remote monitoring in hard-to-reach or hazardous equipment.

AI analysis software takes the wealth of data gathered by the sensors and transforms it into actionable insights. AI’s advanced algorithms can quickly sift through vast amounts of data, identifying anomalies, diagnosing machine faults, and predicting potential failures. It learns from the data it processes, refining its predictive abilities over time. With more mature solutions, the system should already be trained on vast data lakes to recognize a large library of faults.

Together, vibration-sensor technology and AI analysis software create a seamless, efficient system for plant maintenance. The sensors provide the data and AI delivers the analysis. The result is improved operational efficiency, reduced maintenance costs, and enhanced equipment lifespan. This powerful duo is setting a new standard for plant maintenance in the digital age.

The ultimate advantage of AI is clear. It allows industrial operations to scale and expand while keeping costs low and making the most of limited resources. AI propels teams toward the goal of continuous improvement, reducing risk, and enhancing productivity, uptime, and reliability.

Wireless sensors provide added advantages in that they typically offer easy installation and remote monitoring in hard-to-reach or hazardous equipment.

The Power of AI

AI holds the power to radically transform maintenance operations across plants of all sizes. Naturally, the introduction of AI may raise concerns about expenses. Like all new technologies, AI requires an initial investment in terms of time, budget, and training before it can deliver tangible benefits.

The good news is that your facilities may already have the required infrastructure for AI implementation. Your teams might even already possess the necessary training to easily adopt AI tools. If you’ve been employing a proactive maintenance strategy, adopting AI tools should be a seamless and straightforward process, especially when you’re working with a solution provider with significant experience.

Using Existing Systems

AI technology can often seamlessly integrate with existing proactive-maintenance strategies such as predictive maintenance and condition monitoring. This data is a potential goldmine of insights into machine health and plant functioning.

Technicians can examine vibration patterns to identify subtle changes in machine performance that may indicate a new or developing defect. On a larger scale, they can analyze condition-monitoring data to make accurate predictions about asset failure or changing plant conditions.

For example, your plant may already have data siloed in Building Management Systems (BMS), Supervisory Control and Data Acquisition (SCADA) systems, or elsewhere. If your plant is already streaming this condition-monitoring data to the cloud, you have the infrastructure in place for AI implementation.

AI tools can be a missing piece in your plant’s maintenance approach that does not necessarily require an overhaul of your infrastructure. Instead, AI sits neatly on top of your condition-monitoring structure, providing an additional layer of data analysis and insight.

This reduces the drain on resources, as technicians no longer must study every single vibration table or keep an eye on every single machine. Instead, they are notified when anomalies occur and can focus their efforts on mitigating those risks.

Implementing AI should be for the right reasons, which means asking the right questions and setting the right goals. However, the foundational layer remains the same for every facility. Collecting and analyzing condition-monitoring data is a proven method of improving machine productivity and extending asset lifespans. When correctly used, AI can deepen and extend the reach of condition-monitoring programs, leading to continuous improvement and a more efficient use of resources. EP

Michael DeMaria is the Product Manager for Azima DLI, part of Fluke Reliability, Everett, WA (fluke.com), managing the hardware platforms and integrations, diagnostic software and AI tools, and user portal deliverables and business metrics. His background is in Navy nuclear engineering but he has been working in the vibration-analysis arena for more than 30 years.