Research Papers

Looseness Diagnosis of Rotating Machinery Via Vibration Analysis Through Hilbert–Huang Transform Approach

[+] Author and Article Information
T. Y. Wu

Research Center for Adaptive Data Analysis, National Central University, Jhongli City, Taoyuan County 320, Taiwantianyauw@ncu.edu.tw

Y. L. Chung

Advanced Mechanical Technology Department, Industrial Technology Research Institute, Chutung, Hsinchu County 310, Taiwan

C. H. Liu

Department of Power Mechanical Engineering, National Tsing-Hua University, Hsinchu City 300, Taiwan

J. Vib. Acoust 132(3), 031005 (Apr 22, 2010) (9 pages) doi:10.1115/1.4000782 History: Received March 04, 2009; Revised June 22, 2009; Published April 22, 2010; Online April 22, 2010

The objective of this research in this paper is to investigate the feasibility of utilizing the Hilbert–Huang transform method for diagnosing the looseness faults of rotating machinery. The complicated vibration signals of rotating machinery are decomposed into finite number of intrinsic mode functions (IMFs) by integrated ensemble empirical mode decomposition technique. Through the significance test, the information-contained IMFs are selected to form the neat time-frequency Hilbert spectra and the corresponding marginal Hilbert spectra. The looseness faults at different components of the rotating machinery can be diagnosed by measuring the similarities among the information-contained marginal Hilbert spectra. The fault indicator index is defined to measure the similarities among the information-contained marginal Hilbert spectra of vibration signals. By combining the statistical concept of Mahalanobis distance and cosine index, the fault indicator indices can render the similarities among the marginal Hilbert spectra to enhanced and distinguishable quantities. A test bed of rotor-bearing system is performed to illustrate the looseness faults at different mechanical components. The effectiveness of the proposed approach is evaluated by measuring the fault indicator indices among the marginal Hilbert spectra of different looseness types. The results show that the proposed diagnosis method is capable of classifying the distinction among the marginal Hilbert spectra distributions and thus identify the type of looseness fault at machinery.

Copyright © 2010 by American Society of Mechanical Engineers
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Figure 1

Schematic of the rotor-bearing test bed

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Figure 2

Experiment setup of rotor-bearing system

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Figure 3

(a) All IMFs of measurement decomposed by the integrated EEMD method. (b) All IMFs of measurement decomposed by the pure EMD method.

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Figure 4

Instantaneous frequency of IMF6: (a) pure EMD method and (b) integrated EEMD method

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Figure 5

Significance test of all data sets

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Figure 6

Marginal Hilbert spectra of cases 1–4

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Figure 7

Marginal Hilbert spectra of cases 1 and 5–7

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Figure 8

Fault indicator indices between S¯i and S1j, S2j, S3j, and S4j

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Figure 9

Fault indicator indices between S¯i and S5j, S6j, and S7j



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