Paper SAS645-2017: Real-time Analytics at the Edge: Identifying Abnormal Behavior and Filtering Data near the Edge for Internet of Things Applications
نویسندگان
چکیده
This paper describes the use of a machine learning technique for anomaly detection and the SAS® Event Stream Processing engine to analyze streaming sensor data and determine when performance of a turbofan engine deviates from normal operating conditions. Turbofan engines are the most popular type of propulsion engines used by modern airliners due to their high thrust and good fuel efficiency (National Aeronautics and Space Administration 2015). For this paper, we intend to show how sensor readings from the engines can be used to detect asset degradation and help with preventative maintenance applications.
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