ei3's Scientific Approach to Predictive Maintenance Wins Academic Recognition

New York, NY – Jul 25, 2024 – ei3, a leader in industrial IoT solutions, announced today that its Data Science division's pioneering "iPID" method for detecting and quantifying wear and tear in industrial machines has been selected for presentation at the prestigious IEEE iThings-2024 conference in Copenhagen, Denmark, and publication in the conference proceedings. This recognition by the IEEE underscores the academic community's endorsement of ei3 Data Science’s innovative and ground-breaking approach to predictive maintenance.

Developed by ei3's data science team in Zurich, the iPID method, one component of ei3’s “ConnectedAI” suite of industrial-strength AI solutions, employs a unique approach that focuses on analyzing the behavior of a machine's digital control loops, which are integral to maintaining operational balance. By closely monitoring fluctuations in this balance, the iPID algorithm can anticipate mechanical issues before they escalate into costly unplanned downtime.

"Unplanned downtime is a significant financial burden for the global manufacturing industry, with annual costs estimated to exceed $1 trillion," notes Severin Pang, Senior Data Scientist at ei3 and the presenter at the IEEE conference. "iPID enables machine builders and operators to foresee potential failures and implement proactive maintenance strategies, significantly reducing downtime."

What sets iPID apart is its robust mathematical analysis of machine behavior, offering a solution that combines data-driven and engineering-based approaches, surpassing traditional methods that often rely on error-prone extrapolation of data sets that is often no more accurate than guesswork, or engineering models that are unmanageable for anything but the most basic machine elements.

The iPID algorithm is also a perfect complement to federated learning, a technique ei3 employs to allow fleet-wide analysis of machine data while maintaining the full data security and privacy of individual machine operators.

The iPID technology is already widely utilized by ei3 to provide predictive maintenance insights and solutions to customers worldwide. With its focus on delivering cutting-edge IoT solutions and data science services, ei3 continues to lead the way in enhancing industrial efficiency and reliability. Learn more at ei3.com/connectedai/ipid/

About ei3 Corporation:

ei3 offers a suite of no-code IIoT apps and AI-based solutions for the industrial manufacturing sector. With a focus on enhancing efficiency, sustainability, and cost savings, ei3 enables businesses to achieve predictive outcomes. Printing, Plastics, Packaging, and Commercial Real Estate are some of the company’s key market segments. ei3 is headquartered in New York with offices in Montreal and Zurich. For more information, please visit www.ei3.com.

About ei3 Data Science:

The ei3 Data Science Division, located in Zurich, Switzerland, develops data science, machine learning and AI solutions to improve efficiency and reliability of industrial processes and machines. Our solutions are in broad use with clients in more than 100 countries, covering manufacturing processes and lines in the plastics, electronics, and converting industry. From our office near Stauffacher square in central Zurich, the ei3 data science team works closely with clients all around the world and our corporate ei3 colleagues in New York and Montreal. For more information, please visit www.ei3.com/connectedai.