ei3's Scientific Approach to Predictive Maintenance Wins Academic Recognition
July 25 2024 - 12:13PM
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.
Richa Patel
ei3
richa@ei3.com