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Applications

Bearing Multiple Defects Detection Based on Envelope Detector Time Constant

[+] Author and Article Information
A. Mohammadi

Graduate Student
e-mail: amohammadi@MechEng.iust.ac.ir

M. S. Safizadeh

Assistant Professor
e-mail: Safizadeh@iust.ac.ir
School of Mechanical Engineering
Iran University of Science and Technology
Tehran, 16884Iran

1Corresponding author.

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received September 30, 2011; final manuscript received October 3, 2012; published online December 20, 2012. Assoc. Editor: Mohsen Nakhaeinejad.

J. Tribol 135(1), 011102 (Dec 20, 2012) (11 pages) Paper No: TRIB-11-1185; doi: 10.1115/1.4007806 History: Received September 30, 2011; Revised October 03, 2012

Rolling element bearing damage detection is one of the foremost concerns in rotating machinery. The difficulties in bearing defect diagnosis when the bearing has multiple defects increase, since unexpected changes occur in the amplitude of the bearing defect frequencies. In addition, the tendency toward condition-based maintenance (CBM) requires a better understanding of the fault progression due to the fact that multiple defects is one kind of fault development. In this paper, in order to detect multiple defects on one component of the bearing, a new method based on the high frequency resonance technique (HFRT) is introduced. The time constant in the envelope detector is used to find the pattern of the amplitude of defect frequency harmonics (ADFH) in the frequency domain. This method is based on a comparison of the ADFH with a curve, which is obtained from vibration modeling of the bearing. Two criteria are given for the diagnosis of multiple defects. The method is investigated with a simulation and a real experiment. Single and multiple defects are created on the outer race of the ball bearing at different angles. Additionally, the ADFH in the multiple faults experiments are calculated with the proposed mathematical modeling in order to check the accuracy of the model. The experimental results confirm the ability of the proposed method to diagnose multiple defects.

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References

Figures

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Fig. 1

Timeline for the operation of the equipment [3]

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Fig. 2

Three kinds of developed faults [22]: (a) spalling, (b) true brinelling, and (c) false brinelling

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Fig. 3

Transforming the signal to the frequency domain

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Fig. 4

Multiple defects model

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Fig. 5

ED of the impulse [16]: (a) time history, and (b) spectrum

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Fig. 6

General form of the envelope spectrum for a single point defect (fd is the defect frequency) [17]

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Fig. 7

Time constant region

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Fig. 8

Obtained time constants by changing the order of the filter from 1 to 10

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Fig. 9

Effect of the filter order on the slope of the ED: (a) order 1, and (b) order 10

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Fig. 10

Effect of changing the order of the filter on the defect frequency amplitude

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Fig. 11

Simulated single fault signal without noise: (a) simulated signal, (b) envelope spectrum, (c) E2 with H2, and (d) E3 with H3

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Fig. 12

The same time constant (0.00105 s) for the signal and the envelope detector

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Fig. 13

Simulated single fault signal with noise: (a) simulated signal, (b) envelope spectrum, (c) E2 with H2, and (d) E3 with H3

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Fig. 14

Simulated signal for multiple faults: 2nd defect delay 0.001 s, (a) simulated signal, and (b) envelope spectrum

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Fig. 15

Simulated signal for multiple faults: 2nd defect delay 0.005 s, (a) simulated signal, and (b) envelope spectrum

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Fig. 17

Obtained time constant by changing the order of the filter from 1 to 10

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Fig. 18

Single fault signal: (a) measured vibration signal, (b) power spectral density, and (c) magnitude of the envelope spectrum

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Fig. 19

Two distinct faults signals: (a) measured vibration signal, (b) power spectral density, and (c) magnitude of the envelope spectrum

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Fig. 20

Axial fault signal: (a) measured vibration signal, (b) power spectral density, and (c) magnitude of the envelope spectrum

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