Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 Bayesian Belief Networks in Anomaly Detection, Fault Diagnosis & Failure.

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Presentation transcript:

Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 Bayesian Belief Networks in Anomaly Detection, Fault Diagnosis & Failure Prognostics Joseph Gehring Anthony Hadding Santiago Salazar Mentor: Dr. Janusz Zalewski FGCU, April 2012

Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 What is Bayesian Belief Network? Bayesian Belief Network is a graphical method of data analysis employing an algorithm based on the Bayes Theorem. It allows reasoning about data by deriving conclusions about causes of system behavior based on its symptoms.

Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 Bayesian Belief Networks Use data for known occurrences to determine probabilities of unknown conditions. These relationships can be created in a network, with each relationship building on the probabilities known before. Software tools help represent complex relationships in a simple, easy-to-use format

Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 Project Description Use Bayesian Belief Networks to analyze data from three different sources: Wireshark – to reason about network security based on anomaly detection Solar Power Plant – to diagnose device faults NASA Engine Degradation – to predict failures

Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 Wireshark Data Set Network packet information Analyzed for threats and anomalies Malformed packets Unknown source Broadcast destination Combinations of these Other packet information also analyzed How protocol influences packet length

Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 Wireshark Data Set

Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 Solar Plant Data Set Analyzed a month worth of solar plant sensor data, collected every 15 minutes Temperature, Wind Speed, Voltage, Current, Power, Solar Energy Analyzed for sensor faults and failures Failed temperature sensor Failed wind speed sensor

Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 Solar Plant Data Set

Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 Turbofan Engine Degradation Simulation Data 21 sensors, plus other values 100 different engines in the same network Failure prediction, using sensor input to determine Remaining Useful Life based on previously determined average life NASA Data Set

Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 NASA Data Set