Author

Sean Otto

About the Author: Dr. Sean Otto currently leads business development for Cyient’s Advanced Analytics team, focused on designing AI and machine learning models to improve the functionality and reliability of equipment and systems in health care. Leveraging the expertise of Cyient (www.cyient.com), a global equipment engineering and manufacturing service provider, and the growing advantages of “internet of things” and “connected devices,” Dr. Otto and his analytic teams bridge the needed gap between technology, operations and business. His focus is in completing the “last mile” of AI and connected experiences in the Internet of Things where most of the long-term value is realized for businesses and their customers.

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News November 15, 2018 Sean Otto
Predictive maintenance to improve asset efficiency
Equipment manufacturers, engineering, procurement and construction (EPC) companies, and power and process plant owners and operators commonly face the challenge of keeping their fleet, machinery, and other assets working efficiently, while also reducing the cost of maintenance and time-sensitive repairs. Considering the aggressive time-to-market required for industrial products and services, it is crucial to identify the cause of potential faults or failures before they have an opportunity to occur. Emerging technologies like the Internet of Things, big data analytics and cloud data storage are enabling more vehicles, industrial equipment and assembly robots to send condition-based data to a centralized server, making fault detection easier, more practical and more direct.