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.

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Fig. 1

MOOG MaxForce EMA in test fixture

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Fig. 2

EMA fault detection architecture

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Fig. 3

Proposed feature extraction method

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Fig. 4

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

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Fig. 5

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

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Fig. 6

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

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Fig. 7

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

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Fig. 8

Contribution of each bin to the first two principal components

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Fig. 9

Feature plot of training set data with class probability densities

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Fig. 10

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

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Fig. 11

EMA laboratory signal diagram

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Fig. 12

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

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Fig. 13

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

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Fig. 14

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

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Fig. 15

BBD—condition 1: binned PSD

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Fig. 16

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

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Fig. 17

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

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Fig. 18

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

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Fig. 19

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

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Fig. 20

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

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Fig. 21

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

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Fig. 22

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



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