ID: TS6998
Abstract: Using machine-learning and machine-scale pattern recognition to identify anomalous startups and coastdowns on critical machinery in the power plant has multiple benefits for power generators. First, vibration analysts can be much more efficient in diagnosing problems because the potential issues are brought to their attention; analysts can deal with them instead of sifting through data for them or, worse yet, reacting to a breakdown. Second, machine health can be optimized by recognizing potential issues faster. At this session, SparkCognition presents a case study showing how NI InsightCM™ Enterprise data was used to realize these benefits for a large regional power generator.
Speakers:
Sumant Kawale, SparkCognition, Senior Director, Business Development
James Young, SparkCognition, VP of Products
Usman Shuja, SparkCognition, VP of Market Development
Keith Moore, SparkCognition, Product Manager
