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."
The R&D packaging machinery group (PMG) within Church & Dwight Co., a $4.1 billion consumer packaged goods manufacturer, is staffed with “proponents of the benefits of PdM and RxM along with the opportunities that come with it,” says Joe Giambrone, an associate machinery specialist in R&D PMG. The group tackles pilot projects with the goal of showcasing winning capabilities to their cross-functional partners.
One such project had PMG applying condition monitoring to help improve the engineering and tuning of vibratory feeders used in the finishing of gummy vitamins. Their feeders tended to require frequent servicing and had a relatively short life.
When two vibratory feeders were replaced, the new equipment exhibited a significant disparity in sugar feeding outputs. Operators shared this concern with PMG, who, after monitoring the situation and getting an understanding the plant’s planned maintenance schedule, conducted initial tuning with an OEM technician on site.
Over the next three months, PMG monitored and collected data about the operating state. After further tuning, tests and analysis showed better-than-expected results. “We were able to improve product quality and increase output capabilities simultaneously,” explains Giambrone. “PMG had improved the design of the vibratory feeders and was able to tune them correctly for huge gains. Reduced raw material waste, improved operator morale, improved maintenance schedules, increased equipment life span, and longer times between service are all tangible from this study.”