Research Papers

A Data-Driven Methodology for Fault Detection in Electromechanical Actuators

[+] Author and Article Information
Anthony J. Chirico, III

Aircraft Group,
MOOG, Inc.,
East Aurora, NY 14052
e-mail: tchirico2@moog.com

Jason R. Kolodziej

Assistant Professor
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 136(4), 041025 (Apr 28, 2014) (16 pages) Paper No: DS-13-1179; doi: 10.1115/1.4026835 History: Received April 30, 2013; Revised February 06, 2014

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.

Copyright © 2014 by ASME
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Fig. 1

MOOG MaxForce EMA in test fixture

Grahic Jump Location
Fig. 2

EMA fault detection architecture

Grahic Jump Location
Fig. 3

Proposed feature extraction method

Grahic Jump Location
Fig. 4

Generated data: (left)—defect signals, (right)—total signals

Grahic Jump Location
Fig. 5

PSD comparison: (left)—prior to resampling, (right)—after resampling

Grahic Jump Location
Fig. 6

PSD comparison: (left)—resampled and filtered PSD, (right)—binned PSD

Grahic Jump Location
Fig. 7

Principal Component contributions to the total training set variance (72% for first two)

Grahic Jump Location
Fig. 8

Contribution of each bin to the first two principal components

Grahic Jump Location
Fig. 9

Feature plot of training set data with class probability densities

Grahic Jump Location
Fig. 10

MOOG MaxForce EMA (G414-8xx): Technical specifications and cross section

Grahic Jump Location
Fig. 11

EMA laboratory signal diagram

Grahic Jump Location
Fig. 12

Ball Bearing Defect (BBD)—EMA test fixture at MOOG

Grahic Jump Location
Fig. 13

BBD—condition 1 (degraded): EMA position—(upper left), motor speed—(upper right), accelerometer—(lower left), PSD—(lower right)

Grahic Jump Location
Fig. 14

BBD—condition 1 (degraded): phase A current—(top), FFT of raw phase current (freq. resolution = 0.73)— (bottom)

Grahic Jump Location
Fig. 15

BBD—condition 1: binned PSD

Grahic Jump Location
Fig. 16

BBD—condition 1 (validation data): feature plot with Bayesian classification bounds (0%, 0% miss classification, respectively)

Grahic Jump Location
Fig. 17

BBD—condition 2 (degraded): EMA position—(upper left), motor speed—(upper right), phase current—(lower left), PSD—(lower right)

Grahic Jump Location
Fig. 18

BBD—condition 2 (validation data): feature plot With Bayesian classification bounds (0%, 15% miss classification, respectively)

Grahic Jump Location
Fig. 19

BSD—condition 1 (healthy): EMA position—(upper left), motor speed—(upper right), accelerometer—(lower left), PSD—(lower right)

Grahic Jump Location
Fig. 20

BSD—condition 1: classification—training data—(left), validation data—(right) (0%, 5% misclassification, respectively)

Grahic Jump Location
Fig. 21

BSD—condition 2: EMA position—(upper left), motor speed—(upper right), accelerometer—(lower left), PSD—(lower right)

Grahic Jump Location
Fig. 22

BSD—condition 2: classification—training data—(left), validation data—(right) (0%, 2.5% miss classification, respectively)




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In