AI-based IoT solutions extend predictive maintenance across entire production line

Historically, the use of condition monitoring solutions for predictive maintenance has been restricted to high-value, mission-critical equipment, leaving an average of 85% of a production facility’s equipment vulnerable to costly unexpected downtime and repairs. The problem with today’s solutions, says Brad M. Hopkins, director of Condition Monitoring Product Management with HID Global, is that they either are not aligned well with plant requirements or they are considered prohibitively expensive to deploy facility-wide. Now, a new class of condition monitoring solutions combines low-cost, low-power Internet of Things (IoT) technology with Artificial Intelligence (AI)-driven cloud analytics to reduce the cost and deployment complexity of predictive maintenance across an entire asset fleet. The high cost of failure Although motors are generally categorised as critical/expensive, semi-critical, or part of the “balance of plant” (BoP), the reality is that all equipment is operationally critical. Regardless of which category of equipment fails and causes downtime, the associated hourly costs can potentially range from US$30,000 in food processing plants to $87,000 in the petrochemical industry and as high as $200,000 at an automotive factory.