InStep Software to Present on Asset Performance Monitoring at the ISA POWID Symposium

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Company to Discuss Using Predictive Analytics Software to Improve Equipment Reliability

CHICAGO (May 21, 2014) – Sharing a strategy for achieving reliability goals while minimizing avoidable costs, InStep Software will discuss how nuclear power plants, electric power providers and other industrial organizations across the globe can take advantage of the rapidly growing amounts of operational data with a predictive analytics solution at the ISA POWID Symposium.

The symposium takes place June 1-6 in Scottsdale, Ariz. and will educate power generation industry leaders on the latest innovations in controls, cyber security, SmartGrid, regulatory issues and energy technologies. At the event, InStep Software’s Director of Strategic Development Sean Gregerson will present on “Using the Power of Predictive Asset Analytics to Increase Nuclear Equipment Reliability.” The company will also be exhibiting at Booth No. 28.

The InStep presentation will focus on how power plant operators and electric power providers can use predictive asset analytics solutions to increase efficiency, reduce unscheduled downtime, better plan for maintenance and improve overall equipment reliability. Gregerson will highlight how the implementation of a predictive analytics solution for centralized asset performance monitoring leads to immediate and compound benefits, such as increased situational awareness, early warning of potential failures and extended equipment life.

“The ability to effectively manage assets and forecast issues is critical in the power industry,” Gregerson said. “Power plant operators can meet that need by supplementing a preventative and condition-based maintenance program with a predictive analytics solution that transforms raw sensor data into important health and performance information in real-time. By using the data acquired from equipment and sensors for analysis, alerting, diagnosis and reporting, organizations can make smarter decisions that significantly improve reliability.” 

InStep’s predictive analytics solution, PRiSM, uses advanced pattern recognition and diagnostics to identify subtle changes in system behavior that are often the early warning signs of equipment performance and health problems. The software is able to holistically monitor existing equipment sensors integrated with historian or plant control and monitoring systems and is not limited to a specific equipment type or manufacturer.

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