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Research Papers

A Novel Approach to Evaluation of Vibration Source Separation Based on Spatial Distribution of Sensors and Fourier Transforms

[+] Author and Article Information
Ali Mahvash, Aouni A. Lakis

Section of Applied Mechanics, Department of Mechanical Engineering, École Polytechnique de Montréal, Montréal, H3T 1J4, Canada

J. Dyn. Sys., Meas., Control 133(6), 061022 (Nov 23, 2011) (9 pages) doi:10.1115/1.4005277 History: Received April 09, 2011; Revised July 30, 2011; Published November 23, 2011; Online November 23, 2011

An obstacle in diagnosis of multicomponent machinery using multiple sensors to acquire vibration data is firstly found in the data acquisition itself. This is due to the fact that vibration signals collected by each sensor are a mixture of vibration produced by different components and noise; it is not evident what signals are produced by each component. A number of research studies have been carried out in which this problem was considered a blind source separation (BSS) problem and different mathematical methods were used to separate the signals. One complexity with applying such mathematical methods to separate vibration sources is that no metric or standard measure exists to evaluate the quality of the separation. In this study, a method based on statistical energy analysis (SEA) is proposed using Fourier transforms and the spatial distance between sensors and components. The principle of this method is based on the fact that each sensor, with respect to its location in the system, collects a different version of the vibration produced in the system. By applying a short time Fourier transform to the signals collected by multiple sensors and making use of a priori knowledge of the spatial distribution of sensor locations with respect to the components, the source of the peaks on the frequency spectra of the signals can be identified and attributed to the components. The performance of the method was verified using a series of experimental tests on synthetic signals and real laboratory signals collected from different bearings and the results confirmed the efficacy of the method.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 5

STFTs of the source signals

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Figure 6

Test setup with a PWC15 bearing mounted on the left end of the shaft

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Figure 7

Frequency representations of signals collected by four accelerometers

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Figure 1

A schematic depiction of a system with three subsystems

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Figure 2

Generated signals: Sine (50 Hz), Sawtooth (60 Hz), Chirp (10–40 Hz), and uniform noise

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Figure 3

Signal mixtures obtained by multiplying the signals by a mixing matrix

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Figure 4

Separation results in the frequency domain

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Figure 8

Separation results for case of PWC15 and 1217 K SKF bearings

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Figure 9

Separation results for case of 1216 K SKF and 1217 K SKF bearings

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Figure 10

Schema of the test rig at IMS, of University of Cincinnati (by courtesy of Ref. [18])

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Figure 11

Separation results for case of Rexnord bearings

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Figure 12

Separation results for the case of Rexnord bearings using an ICA technique

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