This research investigates a novel data driven approach to condition monitoring of Electro-Mechanical Actuators (EMAs) consisting of feature extraction and fault classification. The approach is able to accommodate time-varying loads and speeds since EMA’s typically operate under non-steady conditions. The feature extraction process exposes fault frequencies in signal data that are synchronous with motor position through a series of signal processing techniques. A resulting reduced dimension feature is then used to determine the condition of the EMA with a trained Bayesian Classifier. Signal data collected from EMAs in known health configurations is used to train the algorithms so that the condition of EMA’s with unknown health may be predicted. Although the process was developed for EMAs, it can be used generically on other rotating machine applications as a Health and Usage Management System (HUMS) tool.
- Dynamic Systems and Control Division
A Data Driven Frequency Based Feature Extraction and Classification Method for EMA Fault Detection and Isolation
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Chirico, AJ, III, Kolodziej, JR, & Hall, L. "A Data Driven Frequency Based Feature Extraction and Classification Method for EMA Fault Detection and Isolation." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 2: Legged Locomotion; Mechatronic Systems; Mechatronics; Mechatronics for Aquatic Environments; MEMS Control; Model Predictive Control; Modeling and Model-Based Control of Advanced IC Engines; Modeling and Simulation; Multi-Agent and Cooperative Systems; Musculoskeletal Dynamic Systems; Nano Systems; Nonlinear Systems; Nonlinear Systems and Control; Optimal Control; Pattern Recognition and Intelligent Systems; Power and Renewable Energy Systems; Powertrain Systems. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 751-760. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8749
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