Driving the reliability necessary to compete in the modern process manufacturing marketplace is more challenging than ever. Once upon a time, reliability teams could rely on expert personnel to regularly walk the plant and keep an eye and ear out for where machines were breaking down. These personnel used their decades of experience to quickly isolate, assess, diagnose, and repair issues, typically before they shut down operations.
Fast forward to modern manufacturing.
The expert personnel with decades of experience are gone—either retired or retiring. New workers taking their place have less expertise, and a hunger for access to data that will help them perform their tasks faster and more effectively. This is the new normal, and artificial intelligence (AI) has arrived on scene just in time to help navigate the new challenges.
However, as Erik Lindhjem explores in his recent article in Automation World, the AI journey can be challenging. AI technologies are in their infancy, and the constant state of evolution can make it difficult to select software that will bring value over a long period of time. There are strategies to help reliability teams navigate this complexity, however. As Erik explains,
“Fortunately, organizations can succeed by considering the reliability maturity model for predictive maintenance from data to decisions. By building a foundation of data integrity, following it up with awareness and predictability, and then feeding those competencies into optimization engines, plants can build the digitalization foundations that will prepare them to compete in an increasingly complex market.”
A data pipeline
It all starts with data. AI models consume tremendous amounts of data. That means the traditional reliability practice of manual rounds is no longer enough. Teams need data that is more frequent, standardized, and consistent in its collection. That means continuous condition monitoring. Sensing solutions such as the AMS Wireless Vibration Monitor, and AMS Asset Monitor are easy to install and deliver continuous condition monitoring data anywhere across the organization without losing its context.
More success with software
Another key tool putting critical insights and data into the hands of reliability personnel is machinery health software like AMS Machine Works. AMS Machine Works simplifies asset management and condition monitoring data to help personnel more easily monitor, diagnose, and resolve mechanical issues for rotating assets. Reliability personnel can use the software to keep a thumb on the pulse of both individual asset health and overall plant health.
Advanced prediction
Both the sensing technologies and machinery health software solutions feed into a higher level enterprise asset performance management and reliability platform like AMS Optics. AMS Optics delivers real-time, mobile insights into assets and processes, automating corrective action and fostering smarter, faster decisions that drive meaningful outcomes. The software not only unifies data from assets across the plant and enterprise but also collects critical data from AI/ML and analytics platforms, helping personnel more easily predict and prevent failures.
“If reliability personnel can see trouble coming, they can plan for those issues and be prepared, scheduling repairs proactively and ensuring they are properly equipped. Doing so not only prevents breakdowns, which reduces the overall cost of maintenance, but also helps teams better identify recurring problems and eliminate their root causes.”
AI the easy way
Most of the AMS condition monitoring tools leverage on-board AI to help turn raw data into actionable information. They not only simplify the asset health status, making it easier for less-experienced technicians to focus and prioritize their efforts, but they also offer expert guidance to help personnel find answers more quickly. And most importantly, those AI solutions are built into the workflows of the software seamlessly. Instead of layering complex, ever-changing AI tools on top of reliability software, teams can use AI tools built into the interfaces they already know and trust.
When AI tools are built and tested by a trusted automation solution provider and integrated seamlessly into their systems, teams can implement those technologies without fear of disruption. Trusted tools empower and upskill personnel, increasing confidence, productivity, and operational excellence across the plant and enterprise.
