Reliability teams need AI-ready digital blueprints now more than ever

It has become crystal clear that the process manufacturing world is experiencing a massive workforce shortage. In fact, the U.S. Census Bureau reports that, as of 2020, nearly one-fourth of the manufacturing workforce is age 55 or older.

As experienced personnel retire in droves, less experienced workers are taking their place—and bringing with them a new dynamic for how maintenance and reliability personnel work, learn, communicate, and collaborate.

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This new reality often creates a disconnect in the plant and on the factory floor. Organizations need to quickly upskill new workers to stay nimble and competitive in a global marketplace, but legacy tools and strategies they have on hand to perform that upskilling often are not up to the task, nor are they the tools a new generation of digital workers is comfortable using.

The solution, as most organizations already know, is automation technology and digital transformation. What technology is the right technology? Many organizations had already begun modernizing their automation to meet coming challenges as generative AI exploded in popularity, claiming it will soon meet all their needs. So, should reliability teams abandon their traditional modernization strategies and focus on the many new AI solutions emerging in the marketplace?

The answer is a resounding “no.” While AI tools are exciting, they are in their infancy, particularly in the industrial reliability space. Moreover, the comprehensive, integrated asset health systems today’s reliability teams are implementing as part of their digital transformation journeys are built on a strong foundation of machine learning, which itself is the foundation of AI.

Modern, trusted integrated machinery health solutions will continue to provide the best and most reliable decision support to lean, growing teams as they navigate the coming years and decades of process manufacturing change. These solutions also will help those teams navigate the brave new world of AI through seamless implementation of proven AI technologies that will better stand the test of time.

Continuous data capture

Today’s reliability teams often are lean, forcing them to accomplish many tasks with a very limited number of personnel. The more time those personnel spend on low-value tasks, such as walking around the plant to view assets individually, the less time they have available to spend on more critical ones, such as improving performance, implementing new reliability strategies, and tracking and trending asset health to apply predictive—rather than reactive—maintenance.

However, reliability teams cannot simply abandon their visibility of plant assets. So how do they continue to gather the data they need while freeing up personnel for more valuable tasks? The answer is continuous condition monitoring. A great starting point is modern wireless vibration monitors, which collect data from plant assets 24/7, 365 days a year.

The most advanced wireless vibration monitors go beyond spectrum and waveform vibration data to provide a simple, reliable indication of equipment health via a single trend. Regardless of experience level, technicians can quickly glance at a report from the monitor to see instantly whether an asset is healthy, needs attention, or is beginning to fail.

In addition, wireless vibration monitors assist lean teams by untethering them from their workstations. Today’s more mobile digital natives can instantly and securely check asset health reports from wireless vibration monitors from anywhere—inside or outside the plant—using their mobile devices.

Instead of stopping other high value tasks to spend time walking around the plant collecting data, personnel can instead integrate monitoring into their other daily tasks.

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Moreover, with continuous condition monitoring, teams no longer need to worry about the standardization and cadence of collected data because machinery health data will come in at the same pace and using the exact same measurements, regardless of who is on staff at any given time.

Evolving the edge

Once teams have a network of wireless vibration monitors in place to bring them continuous visibility of asset health, they can focus on automation solutions that leverage machine learning for analytics at the edge.

Edge analytics devices not only monitor vibration—they also collect additional process variables—such as pressure, temperature, flow, and more.

After collecting the data, the edge analytics device applies built-in analysis tools, designed based on decades of their supplier’s domain knowledge, to automatically identify the most common issues with fans, motors gearboxes, pumps, and other rotating machinery, such as imbalance and lubrication issues.

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With edge analytics devices, much of the guesswork is taken out of reliability tasks. Inexperienced technicians gain the decision support necessary to help them identify what assets are failing and the root cause, speeding time to repair.

However, even experienced technicians gain value, no longer needing to pore over spectrum and waveform data, or collect and apply additional process values to diagnose an issue. Ultimately, all users, regardless of experience level, gain the benefit of instant visibility of asset health in the palm of their hand.

Comprehensive visibility and integrated AI

As an organization begins implementing more sensing technologies, it will need ways to bring that data together and embed more value. One tool helping accomplish this goal is a fit-for-purpose integrated machinery health platform.

Such platforms bring a wide range of sensing device data together to help technicians see the overall health of their plant in real-time, from one location, making it easier and faster to respond to emerging issues.

Reliability teams ready to look beyond the plant for improvement opportunities can further level up their capabilities by driving their continuous data collection to enterprise-level reliability software designed to seamlessly integrate with their asset management tools.

Such tools provide enterprise-wide visibility of plant health, empowering teams in central locations to track and trend health across the entire enterprise to drive better business decisions.

Enterprise-level reliability software solutions are also where today’s most effective organizations are employing AI tools to drive increased reliability. The most advanced enterprise-level reliability software seamlessly integrates with predictive and prescriptive asset health software featuring AI and machine learning-based agents.

These tools use pattern recognition algorithms that incorporate multivariate data, empowering them to predict asset health decline and impending failures based on decades of domain knowledge. Moreover, expert automation suppliers continue to add new, field-proven AI capabilities into their offerings.

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Such AI solutions are not complex or fragile bolt-on software packages that require AI domain expertise and many hours of complex configuration, nor are they solutions that may or may not stand the test of time. Rather, they are intuitive extensions of the tools reliability teams already understand and rely upon in their day-to-day tasks.

Building a foundation for success

In most cases, a process manufacturer’s digital transformation journey progresses gradually, but this does not mean it ignores the value of new technology developments.

For teams following a pragmatic modernization journey with the support of an automation partner with decades of experience, implementing new technologies is built into the process—at a cadence that will not disrupt operation and with a basis in field testing that helps future-proof investments.

The rise of AI does not signal a need to divert from an existing digital transformation roadmap. To the contrary, it signals that the need for that roadmap is greater than ever.