More than 38% of global energy is consumed by industry, and within industry almost 70% of electricity is consumed by electric motors. Given the huge energy cost associated with running these motors, even a 1% increase in operational efficiency will result in huge cost savings. Equipment maintenance is key to optimal motor performance. In general, equipment maintenance can be divided into two broad categories. In the first, equipment is taken out of service on a regular schedule (Preventative Maintenance- PM) and in the second, maintenance occurs when the equipment fails (Corrective Maintenance or "run-to failure"). One occurs too early, the other too late; both are wasteful in terms of time and resources. A third option is Condition-Based Maintenance/Condition-Based Monitoring (CbM). This places sensors where they can detect subsystem trends or excursions from baseline performance to alert process owners about specific problems- before failures occur- allowing replacement parts to be on hand at a conveniently planned repair time. The process owner is no longer replacing good parts on a scheduled maintenance plan, nor is he or she in crisis hair-on-fire mode because the equipment failed unexpectedly. Please join us as Richard Anslow, System Applications Manager within the Industrial Automation Business Unit at Analog Devices, gives us a fascinating introduction to CbM, and how it can improve the availability and reduce costs of systems that we support and work with! Presentation Overview: Background for CbM - What is CbM? - What are the benefits? What are the limitations? - Real life examples. - How do I spot faults from FFT signatures of a motor? Insights from Different CbM Sensor technologies - Vibration - Temperature - Magnetic Sensor types and Market outlook - Market outlook for wired and wireless sensors - Wireless sensor examples - Wired sensor examples - Cloud and Edge Artificial Intelligence Speaker(s): Richard Anslow, Agenda: 11:00 AM Technical Presentation 11:45 AM Questions and Answers 12:00 PM Adjournment Virtual: https://events.vtools.ieee.org/m/356460