[Case history] Engineering a competitive advantage

April 16, 2019
Predictive maintenance: Pedal to the metal

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.

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.”

About the Author

Sheila Kennedy | CMRP

Sheila Kennedy, CMRP, is a professional freelance writer specializing in industrial and technical topics. She established Additive Communications in 2003 to serve software, technology, and service providers in industries such as manufacturing and utilities, and became a contributing editor and Technology Toolbox columnist for Plant Services in 2004. Prior to Additive Communications, she had 11 years of experience implementing industrial information systems. Kennedy earned her B.S. at Purdue University and her MBA at the University of Phoenix. She can be reached at [email protected] or www.linkedin.com/in/kennedysheila.

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