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STUDIES ON VIBRATION ANALYSIS OF ROLLING ELEMENT BEARINGS WITH LOCALIZED DEFECTS

This study explores the detection and analysis of localized defects in rolling element bearings using vibration signals. It covers various defect types, signal processing techniques, simulated signal generation, experimental setups, and analysis of theoretical vs. experimental results.

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STUDIES ON VIBRATION ANALYSIS OF ROLLING ELEMENT BEARINGS WITH LOCALIZED DEFECTS

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  1. STUDIES ON VIBRATION ANALYSIS OF ROLLING ELEMENT BEARINGS WITH LOCALIZED DEFECTS Achintya Choudhury Bhartiya Skill Development University Jaipur India

  2. Rolling Element Bearings • Bearings are highly engineered, precision-made components that enable machinery to move at extremely high speeds and carry remarkable loads with ease and efficiency. • Bearings are found in applications ranging from small hand-held devices to heavy duty industrial systems.

  3. Bearing Defects Localized defects Cracks Pits Spalls Cause : Fatigue on rolling surfaces • Distributed defects • Surface Waviness • Misaligned races • Off-size rolling elements • Cause : Manufacturing errors, Wear

  4. Applied Load IR Load Zone OR Localized defects in bearing elements REs

  5. Vibration Signal Processing 1.Time-domain approach 2. Freq.-domain approach 3. TF domain approach Stationary Signals Non-Stationary Signals

  6. Detection of localized defects on different bearing elements under dynamic loads Two Studies • Extraction of diagnostic features from a noisy signal using a coupled method of wavelet transform and FFT analysis

  7. Vibratory Model of Bearing Governing equations: Excitation vector with excitations due to defects on bearing elements =

  8. Block Diagram for Generation of Simulated Signal Simulated signal yf = Impulses due to defects ybs = Response function for bearing vibratory elements yq nt = Additive noise = Radial load

  9. Frequency B – spline Wavelet m = Integer Order fb = Frequency bandwidth fc = Central frequency

  10. Flowchart for Defect Detection

  11. Detection of Inner Race Defect Vibration response of NJ305 bearing with defect on the inner race at 45 Hz (a) Without noise (b) with noise level Wavelet coefficients of the noisy vibration signal Retained Wavelet coefficients over the threshold level Maximum Wavelet coefficients Frequency spectrum of retained Wavelet coefficients Peaks at inner race defect freq. with sidebands at shaft freq.

  12. Experimental setup

  13. Defect Detection from Experimental Signal

  14. Dynamic Load on Bearing Applied Load Applied Load Static Load Harmonic Load Load Zone Random Load

  15. Dynamic Load on Bearing Modulation caused by dynamic load often results in additional spectral components Load Zone

  16. Signal Analysis Simulated Signal in time domain Signal is high pass filtered to remove low frequency disturbances High pass filtered signal is band-pass filtered around a prominent resonant frequency Envelope detection using Hilbert Transform

  17. Signal Analysis Spectrum of Vibration response for Outer race defect with harmonic load Spectrum of Vibration response for Inner race defect with harmonic load Spectrum of Vibration response for Roller defect with harmonic load Spectrum of Vibration response for Inner race defect with random load

  18. Experimental Set-up

  19. Experimental Results Raw Signal (a) Time (b) Frequency High pass filtered Signal (c) Time (d) Frequency Band-pass filtered Signal (e) Time (f) Frequency

  20. Experimental Results Spectrum for outer race defect Envelope in time domain Spectrum for Inner Race defect

  21. Theoretical Vs. Experimental Spectra for Outer Race Defect (a) (a) (b) (b) (c) (c) Frequency spectra for NJ 305 bearing with outer race defect at 2840 r.p.m, W = 196.2 N; and (a) Ah = 0 (b) Ah = W (c) Ah = 2W

  22. Theoretical Vs. Experimental Spectra for Inner Race Defect (a) (a) (b) (b) (c) (c) Frequency spectra for NJ 305 bearing with inner race defect at 2975 r.p.m, W = 196.2 N and (a) Ah = 0 (b) Ah = W (c) Ah = 2W

  23. Theoretical Vs. Experimental Spectral Components for Inner Race Defect

  24. Publications from these studies 1. Paliwal D., Choudhury A. and Govardhan T., Detection of Bearing Defects from Noisy Vibration Signals using A Coupled Method of Wavelet Analysis followed by FFTAnalysis, Journal of Vibration Engineering & Technologies, vol. 5, no. 1, 2017, pp 21 - 34. GovardhanT., Choudhury A. and Paliwal D., Vibration analysis of a rolling element bearing with localized defect under dynamic radial load, Journal of Vibration Engineering & Technologies, vol. 5, no. 2, 2017, pp 165 - 175. Govardhan T. and Choudhury A., Fault Diagnosis of Dynamically Loaded Bearing with Localized Defect Based on Defect-Induced Excitation, Journal of Failure Analysis and Prevention,Vol. 19, no. 3,2019, pp 844 – 857.

  25. Thank you

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