STUDIES ON VIBRATION ANALYSIS OF ROLLING ELEMENT BEARINGS

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

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.

Bearing Defects Localized defects Distributed defects i) Cracks ii) Pits iii) Spalls i) Surface Waviness ii) Misaligned races iii) Off-size rolling elements Cause : Manufacturing errors, Wear Cause : Fatigue on rolling surfaces

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

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

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

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

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

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

Flowchart for Defect Detection

Detection of Inner Race Defect Vibration response of NJ 305 bearing with defect on the inner race at 45 Hz (a) Without noise (b) with noise level Maximum Wavelet coefficients of the noisy vibration signal Retained Wavelet coefficients over the threshold level Frequency spectrum of retained Wavelet coefficients Peaks at inner race defect freq. with sidebands at shaft freq.

Experimental setup

Defect Detection from Experimental Signal

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

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

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

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

Experimental Set-up

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

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

Theoretical Vs. Experimental Spectra for Outer Race Defect (a) (b) (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 = 2 W

Theoretical Vs. Experimental Spectra for Inner Race Defect (a) (b) (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 = 2 W (c)

Theoretical Vs. Experimental Spectral Components for Inner Race Defect

Publications from these studies 1. Vibration Signals using A Coupled Method of Wavelet Analysis followed by FFT Analysis, Journal of Vibration Engineering & Technologies, vol. 5, no. 1, 2017, pp 21 - 34. 2. Govardhan T. , 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. 3. 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.

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