[Case history] Developing a new business model by selling supply

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 cloud-based Sigma Smart Air service from Kaeser Compressors has given the company the ability to sell compressed air as an alternative to selling machines. It allows Kaeser’s service personnel to monitor customer equipment and compressed air usage online in real time and deliver PdM and compressed air supply as needed.

The single source of information about products, components, spare parts, and service benefits Kaeser’s field-service personnel as well as connected suppliers, dealers, and customers, according to the company. Advantages to operators from the service efficiencies include lower compressed air production costs and enhanced compressed air availability.

Sigma Smart Air’s predictive analytics provide revealing asset health information, enabling well-timed maintenance and services for greater machine availability. The product’s digital twin capability allows an up-to-date digital replica of a customer’s specific physical compressed air station, configured to the operator’s requirements, to be accessible virtually at any time from any location.

Access to the digital twin enhances understanding of the asset’s physical state including its real-time operating conditions. “All historically important events, including technical documentation and business data, are visible, and not only to us, but also to partners and to customers,” says Falko Lameter, CIO at Kaeser Compressors. Kaeser now views ML capabilities as the next logical step for further improving the company’s processes.

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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