Finding Value for Customers in a Sea of Data
(Room Rheinsaal 6)
28 Jun 17
2:00 PM
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3:30 PM
Tracks:
Track B - Mastering the Digital Era
Digitalization is transforming the energy landscape. Each day power plants generate vast amounts of data. This data can provide insight into how an asset is operating, when maintenance will be needed and how assets can be optimized. But how do you find the right data to create meaningful insight? When data generated from your assets is combined with data from other sources such as the plant design details, global fleet data, parts and repair data, weather and energy market data and applying advanced analytics you have a more complete view of the state of your asset. Within this sea of data is value and with the right analysis and experts you will be able to achieve what matters to you most. At Siemens we are using new techniques and tools to combine existing parametric and physics-based engineering models with fleet statistical and empirical models to help optimize service schedule and scope, manage risk factors virtually and identify power or load gradient opportunities leading to more effective asset management, ultimately leading to improved plant profitability. We are leveraging our more than 160 data scientists, an expansive team of engineering experts and a robust analytics platform to enable the customer to achieve maximum performance. We work side-by-side with customers to interpret the data that will help them make intelligent and informed decisions on how to best operate and maintain their assets. Accurate analysis of the sea of data helps our customers efficiently plan maintenance and operation with the highest degree of availability and reliability and the best possible performance throughout the life of the equipment. We also use the data to identify ways to increase plant efficiency, reliability and availability translating into increased revenue and reduced cost for our customers. This paper will present Siemens solutions to actual customer situations using monitoring, diagnostics and data analytics.