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The results indicate that the proposed methodology is robust to reduction of the size of the training set. Then multi-class logistic regression with adaptive lasso is used to select features from the selected sensors to predict centerbody degradation. This methodology utilizes multi-class logistic regression with adaptive group lasso penalty to select an optimal subset of sensors for monitoring. A combustor test-rig is equipped with ten acoustic sensors which can be categorized as longitudinal (sensors mounted on the tubes which carry the fuel/air mixture to the combustion chamber) and transverse (sensors mounted on the support beams perpendicular to the tubes. The second project describes a hierarchical feature selection methodology for diagnosing the degradation state of the combustor centerbody, a component that provides stability for the combustor flame and protects the hardware of the combustor from the flame. A probability distribution is fitted to these precursors to develop a measure of blowout risk that can be utilized by turbine or aircraft operators. The first project involves utilizing a control chart to detect precursors to lean blowout, an important operational fault that can cause costly powerplant outages and compromise the safety of aircraft passengers. Zinn Combustion Laboratory at Georgia Tech. These projects utilize industry-class combustors located in the Ben T. Thus, predictions of the fault-mode combination can be mapped to the overall system degradation.Ĭhapter 3 discusses two projects related to the gas turbine combustor. The space of fault-mode combinations is partitioned into degrees of overall degradation.
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Then ensemble methods are proposed that merge the results of these three models to create a more accurate model. Three regularized multinomial regression models are fitted, each using a unique feature extraction technique, to map profile features to the fault mode combination. The profiles are collected for various combinations of battery-motor state-of-health. This framework leverages functional data profiles collected from a 4-cylinder automotive engine test-rig. In Chapter 1, the proposed data analytics framework is outlined and various challenges associated with high-dimensional data are discussed.Ĭhapter 2 develops a severity-based diagnosis framework for electric-powered vehicle electric systems with multiple, interacting fault modes. This enables the validation of data-analytics techniques on real-world data. To collect this data, the projects discussed utilize either simulated data sets or state-of-the-art, industry-class test-rigs. Therefore, this dissertation presents a data analytics framework for performing these condition monitoring tasks using high-dimensional data. However, the information valuable for performing these tasks is often clouded in noise and must be mined from high-dimensional data structures. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault detection, diagnosis, and prognostics. Modern industrial systems are now fitted with several sensors for condition monitoring. Time: Friday, December 3 rd at 10:00 AM ET Timothy Lieuwen, School of Aerospace Engineering (Georgia Tech) Nicoleta Serban, School of Industrial and Systems Engineering (Georgia Tech) Jianjun Shi, School of Industrial and Systems Engineering (Georgia Tech) Kamran Paynabar, School of Industrial and Systems Engineering (Georgia Tech) Nagi Gebraeel, School of Industrial and Systems Engineering (Georgia Tech) Title: Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems