Research Papers

Automated Operating Mode Classification for Online Monitoring Systems

[+] Author and Article Information
Markus A. Timusk

 Laurentian University, Sudbury, ON, P3E 2C6, Canadamtimusk@laurentian.ca

Michael G. Lipsett

 University of Alberta, Edmonton, AL, T6G 2G8, Canadamichael.lipsett@ualberta.ca

Jordan McBain

 Laurentian University, Sudbury, ON, P3E 2C6, Canada

Chris K. Mechefske1

 Queen’s University, Kingston, ON, K7L 3N6, Canadachrism@me.queensu.ca


Corresponding author. Also at Department of Mechanical and Materials Engineering, McLaughlin Hall, Queen’s University, Kingston, ON, K7L 3N6, Canada.

J. Vib. Acoust 131(4), 041003 (Jun 05, 2009) (10 pages) doi:10.1115/1.3142871 History: Received May 09, 2007; Revised April 15, 2009; Published June 05, 2009

Transient operation of machinery can greatly complicate the task of vibration-based online condition monitoring. Because the operating mode of a machine affects the physical response and hence the diagnostic parameters, real-time information regarding the operating mode is likely to improve the performance of an online fault detection system. This paper proposes a method for automated operating mode classification to augment the performance of vibration-based online condition monitoring systems for applications such as gearboxes, motors, and their constituent components. Experimental work has been carried out on the swing machinery of an electromechanical excavator, which demonstrates how such a method might function on actual dynamic signals gathered from an operating machine. Several variations of the system have been tested and found to be successful.

Copyright © 2009 by American Society of Mechanical Engineers
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Figure 8

Calculation of speed profile

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Figure 9

Spline fit of speed profile

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Figure 10

Combinations of system configurations used for testing

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Figure 11

Comparison of preprocessing methods (averaged across all features and all classifiers)

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Figure 12

Feature set comparison (averaged across all features and all classifiers)

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Figure 13

Classifier decision boundaries, acceleration speed features (0—empty; x—full)

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Figure 14

Comparison of classifiers—acceleration segment

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Figure 1

Block diagram of signal processing system

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Figure 2

P&H TS4100 electromechanical excavator

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Figure 3

P&H TS4100 swing gearbox and motor

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Figure 4

Spectrogram (top) and corresponding speed plot (bottom) of gearbox

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Figure 5

Plan view of machine enclosure for P&H TS4100 excavator

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Figure 6

Mode classification data flow using speed and vibration features

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Figure 7

General pattern recognition system for condition monitoring



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