Maurice O’Brien is the strategic marketing manager for industrial automation at Analog Devices. He is responsible for delivering industrial automation focused, system-level solutions. Prior to this role, Maurice spent 3 years working on Industrial Ethernet and 15 years working in applications and marketing roles in power management with Analog Devices. He has a B.Eng. Electronic Engineering from the University of Limerick, Ireland.
Brandon Bushey obtained a Bachelor of Science in Electrical and Computer Engineering from the University of Massachusetts Amherst in 2005. After graduating, he joined Analog Devices in July 2005 as a technical support engineer, where he interfaced with customers and their application challenges. In 2009 Brandon started working on a new reference design program entitled "Circuits from the Lab" in order to adapt to the changing industry trend in which engineers had to do more work with less time and resources available. He has since been primarily working on sensor and precision-based systems and applications designs for the past 10 years while managing a remote engineering team.
Travis Collins holds BS, MS, and PhD degrees in Electrical and Computer Engineering from WPI. He joined ADI in April 2017 in the System Development Group where he focuses on complete signal chain workflows and system architectures. Travis’ expertise includes digital signal processing, communications theory, radar, and high performance compute.
Given that unscheduled downtime can amount to nearly a quarter of total manufacturing costs, predictive maintenance has the potential to unlock significant savings and increased productivity. In this webinar, we will introduce a new condition-based monitoring platform from Analog Devices, designed to help accelerate condition monitoring hardware, software, and algorithm development. The CN0549 platform is a complete development platform that provides mechanically secure, wide bandwidth sensor data with robust, high fidelity data acquisition. Open source embedded software stream data directly up to MATLAB and Python-based data analysis tools, for real-time processing and machine learning algorithm development for predictive maintenance services. The flexibility of this platform allows engineers of all disciplines to get quickly started with their CbM application development and get their products to market faster.
Attendees will learn: