GE Digital is undertaking a journey to optimize the reliability, availability, and efficiency of assets in the industrial sector and converge IT and OT. To do so, GE Digital is building cloud-based products that enable customers to analyze the asset data, detect anomalies, and provide recommendations for operating plants efficiently while increasing productivity.
In a energy sector such as oil and gas, power, or renewables, a single plant comprises multiple complex assets, such as steam turbines, gas turbines, and compressors, to generate power. Each system contains various sensors to detect the operating conditions of the assets, generating large volumes of variety of data. A highly scalable distributed environment is required to analyze such a large volume of data and provide operating insights in near real time.
Kishore Reddipalli explores how to stream data at a large scale from the edge to the cloud to the client, detect anomalies, analyze machine data in stream and rest in an industrial world, and optimize the industrial operations by providing real-time insights and recommendations using big data technologies. Kishore also shares the challenges encountered when analyzing the large volumes of data, explains how GE Digital tuned the performance of the analytics to leverage the highly distributed cluster in more efficient way, and outlines the lessons learned in the context of a industrial use case of heat rate gap analysis.
Kishore Reddipalli is a Director of Engineering at GE Digital for Predix operations optimization. Kishore has been working with various industrial business domains in GE including oil and gas, transportation, power, and renewables focusing on industrial usecases. He is an expert in building big data and AI Platform and Solutions. Prior to joining GE Digital, Kishore worked in GE Healthcare, where he helped build the next-generation Clinical data platform Qualibria.
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