General Interpolated Fast Fourier Transform: A New Tool for Diagnosing Large Rotating Machinery

[+] Author and Article Information
D. F. Shi1

School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Simon Building, Manchester, M13 9PL, UK and School of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R. Chinadongfeng.shi@man.ac.uk

L. S. Qu

Research Institute of Diagnostics & Cybernetics,  Xian Jiaotong University, Xian 710049, P. R. Chinalsqu@xjtu.edu.cn

N. N. Gindy

School of Mechanical, Materials and Manufacturing Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UKnabil.gindy@nottingham.ac.uk


Corresponding author.

J. Vib. Acoust 127(4), 351-361 (Nov 30, 2004) (11 pages) doi:10.1115/1.1924643 History: Received February 05, 2004; Revised November 30, 2004

Vibration monitoring and diagnosis of rotating machinery is an important part of a predictive maintenance program to reduce operating and maintenance costs. In order to improve the efficiency and accuracy of diagnosis, the general interpolated fast Fourier transform (GIFFT) is introduced in this paper. In comparison to present interpolated fast Fourier transform, this new approach can deal with any type of window functions and possesses high accuracy and robust performance, especially coping with a small number of sampling points. Then, for the purpose of rotating machinery diagnosis, the harmonic vibration ellipse and orbit is reconstructed based on the GIFFT to extract the features of faults and remove the interference from environmental noise and some irrelevant components. This novel scheme is proving to be very effective and reliable in diagnosing several types of malfunctions in gas turbines and compressors and characterizing of the transient behavior of rotating machinery in the run-up stage.

Copyright © 2005 by American Society of Mechanical Engineers
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Figure 1

Amplitude, frequency (a), and phase (b) errors of sinusoidal signal in the FFT spectrum

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Figure 2

Flowchart of the GIFFT

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Figure 3

Variation of objective function (a) and phase (b) along with searching proceeds

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Figure 4

Estimation errors of frequency (a), amplitude (b), and phase (c) with different number of sampling points

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Figure 5

Flowchart of reconstructing harmonic ellipse and orbit via the GIFFT

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Figure 6

Original orbit (a) and reconstructed orbit (b) caused by misalignment

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Figure 7

Original orbit (a) and reconstructed orbit (b) caused by rubbing

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Figure 8

FFT spectra (a) and reconstructed harmonic ellipse (b) caused by pipe excitation

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Figure 9

FFT spectra (a) and reconstructed harmonic ellipse (b) caused by rotating stall

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Figure 10

FFT spectra (a) and reconstructed harmonic ellipse (b) caused by oil whirl

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Figure 11

Original orbit diagram obtained in run-up stages

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Figure 12

FFT spectra of vibration in run-up stages

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Figure 13

STFT of horizontal vibration

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Figure 14

Instantaneous purified orbit in run-up stages

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Figure 15

Instantaneous orbits in run-up stages

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Figure 16

Amplitude-frequency properties curve in run-up stage

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Figure 17

Rotating speed curve in run-up stages



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