Abstract:
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Modern wind tunnel systems are constructed of various instruments containing hundreds and thousands of individual sensors. Each sensor requires continual quality monitoring, as corrupted data will increase experimental costs, wasted time, and lead to spurious engineering conclusions. Monitoring methodologies such as Gaussian Processes and Principal Component Analysis have been examined for anomaly detection in wind tunnel systems. However, these methodologies are limited due to continual instrumental movement, wind tunnel re-configurations, or low-levels of adverse observations in training datasets. In this presentation, we compare two robust system monitoring methodologies: (1) a method based on a robust Principal Component Analysis technique, and (2) a proposed method based on a Bayesian, heavy-tailed posterior distribution with Principal Component Analysis. Additionally, we extend our methodology to mixtures of signals in order to account for cases where sensors are intermittently collecting anomalous signals. Through the simulation and real wind tunnel experiments, we exemplify the need for anomaly detection methodologies using a robust, correlated, multi-type sensor approach.
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