Research Papers

Rolling Element Bearing Diagnostics Using Extended Phase Space Topology

[+] Author and Article Information
T. Haj Mohamad

Villanova Center for Analytics of Dynamic Systems,
Villanova University,
Villanova, PA 19085
e-mail: thajmoha@villanova.edu

M. Samadani

Villanova Center for Analytics of Dynamic Systems,
Villanova University,
Villanova, PA 19085
e-mail: mohsen.samadani@gmail.com

C. Nataraj

Villanova Center for Analytics of Dynamic Systems,
Department of Mechanical Engineering,
Villanova University,
Villanova, PA 19085
e-mail: c.nataraj@villanova.edu

Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received December 5, 2017; final manuscript received April 11, 2018; published online May 14, 2018. Assoc. Editor: Huageng Luo.

J. Vib. Acoust 140(6), 061009 (May 14, 2018) (9 pages) Paper No: VIB-17-1527; doi: 10.1115/1.4040041 History: Received December 05, 2017; Revised April 11, 2018

This paper introduces a novel method called extended phase space topology (EPST) for machinery diagnostics and pattern recognition. In particular, the research focuses on fault detection and diagnostics of rolling element bearings. The proposed method is based on mapping the vibrational response onto the density space and approximating the density using orthogonal functions. The method has been applied to vibration data of a rotating machine where the data were measured by proximity probes. The method was applied to two operating conditions: constant operating speed and variable operating speed. As will be shown, the proposed feature extraction method has an outstanding capability in characterizing the system response and diagnosing the system. The method is evidently robust to noise, does not depend on expert knowledge about the system, requires no feature ranking or selection, and can easily be applied in an automated process. Finally, a comparison with utilization of statistical features is performed for each operating condition, which demonstrates that the proposed method performs better than the traditional statistical methods.

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Fig. 1

An example of the phase space topology method: (a) a sample phase plane plot of a simulated nonlinear second-order system and (b) the density distribution

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Fig. 2

Rotating fault simulator machine

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Fig. 3

Defects in rolling element bearings

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Fig. 4

Orbit plots for various shaft rotation speeds and bearing conditions

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Fig. 5

The approximated density with Legendre polynomials of the horizontal vibration signals plotted on top of their estimated density distributions

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Fig. 6

Flowchart of bearing diagnostics algorithm (case B)



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