This research investigates a novel data-driven approach to condition monitoring of electromechanical actuators (EMAs) consisting of feature extraction and fault classification. The approach is able to accommodate time-varying loads and speeds since EMAs typically operate under nonsteady 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 with a trained Bayesian classifier. The approach is based on signal analysis in the frequency domain of inherent EMA signals and accelerometers. For this work, two common failure modes, bearing and ball screw faults, are seeded on a MOOG MaxForce EMA. The EMA is then loaded using active and passive load cells with measurements collected via a dSPACE data acquisition and control system. Typical position commands and loads are utilized to simulate “real-world” inputs and disturbances and laboratory results show that actuator condition can be determined over a range of inputs. Although the process is developed for EMAs, it can be used generically on other rotating machine applications as a Health and Usage Management System (HUMS) tool.
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July 2014
Research-Article
A Data-Driven Methodology for Fault Detection in Electromechanical Actuators
Jason R. Kolodziej
Jason R. Kolodziej
1
Assistant Professor
Mem. ASME
Department of Mechanical Engineering,
e-mail: jrkeme@rit.edu
Mem. ASME
Department of Mechanical Engineering,
Rochester Institute of Technology
,Rochester, NY 14623
e-mail: jrkeme@rit.edu
1Corresponding author.
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Anthony J. Chirico, III
Jason R. Kolodziej
Assistant Professor
Mem. ASME
Department of Mechanical Engineering,
e-mail: jrkeme@rit.edu
Mem. ASME
Department of Mechanical Engineering,
Rochester Institute of Technology
,Rochester, NY 14623
e-mail: jrkeme@rit.edu
1Corresponding author.
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 30, 2013; final manuscript received February 6, 2014; published online April 28, 2014. Assoc. Editor: Qingze Zou.
J. Dyn. Sys., Meas., Control. Jul 2014, 136(4): 041025 (16 pages)
Published Online: April 28, 2014
Article history
Received:
April 30, 2013
Revision Received:
February 6, 2014
Citation
Chirico, A. J., III, and Kolodziej, J. R. (April 28, 2014). "A Data-Driven Methodology for Fault Detection in Electromechanical Actuators." ASME. J. Dyn. Sys., Meas., Control. July 2014; 136(4): 041025. https://doi.org/10.1115/1.4026835
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