Run-to-failure is increasingly reserved for rare and unique circumstances. This trend started when increasingly capable condition inspection and monitoring tools shifted the asset management focus from “fix what’s broken” to “keep it from breaking down.” Today, unprecedented opportunities afforded by the industrial internet of things (IIoT) have further changed the playing field, and there are potential benefits yet to be realized.
For example, predictive maintenance (PdM), originally based on selected asset condition data, has grown to accommodate online, real-time streams of multiple types of condition data received via sensors and even drones. Some companies are applying machine learning (ML) to further refine their predictive analytics and prognostics.
The newest opportunity, prescriptive maintenance (RxM), is a multivariate approach that merges asset condition data with any combination of operating, environmental, process safety, engineering, supplier, or other related data to better diagnose conditions and prescribe specific options for corrective action. The advanced analytics, pattern recognition, modeling, ML, and artificial intelligence (AI) that empower RxM may help companies finally greatly curtail, if not eliminate, the need for reactive maintenance on critical equipment.
- Read all 7 case histories from our April 2019 cover story, "How 7 companies are accelerating PdM and RxM at their plants."
Borealis, a European chemicals producer, deployed RxM software with ML and prescriptive analytics capabilities following a successful proof-of-concept project for a business-critical hyper compressor. Previously, repeating failures of the hyper compressor had resulted in high maintenance costs and unplanned shutdowns. With ML, the plant received advance warning of the failures about four weeks before they occurred, allowing time to take necessary actions.
The proof-of-concept project’s tangible benefits persuaded the company to move forward with the software. “Borealis has embarked on a digital journey, and the ability to bring transparency to all our operating processes is a priority for us,” explains Martijn van Koten, executive vice president of operations at Borealis. “We will have the time to work collaboratively to mitigate the losses of unplanned downtime and to minimize disruptions to our customers.”
With the software’s ML and data analytics – including pattern recognition and early anomaly detection – providing significantly earlier warning of failure with detailed prescribed actions, Borealis also has the capability to improve safety, quality, reliability, and overall performance.
