fault diagnosis of roller bearing using acoustic emission

Fault Diagnosis of Roller Bearing Using Acoustic Emission Technique and Fuzzy Logic

M.P.Badgujar PG student of S.S.G.B.C.O.E. & T; Department of Mechanical Engineering, Bhusawal-425203,

North Maharashtra University, Maharashtra, India.

A.V.Patil Faculty of S.S.G.B.C.O.E. & T; Department of Mechanical Engineering, Bhusawal-425203, North Maharashtra

University, Maharashtra, India.

Abstract- The experimental investigation reported here was centered on the application of the AE technique for identifying the presence and size of a defect on a radially loaded bearing. An experimental test rig was designed such that defects of varying sizes could be seeded onto the outer race of a test bearing. The high load carrying capacity bearing is taken for low load & various RPM, and analysis has been done.Comparisons between AE and vibration analysis over a range of speed and load conditions are presented. In addition, The peak value of amplitude called as kurtosis is considered as a criteria for analysis. Fuzzy logic technique is also one of the technique which predicts the presence of cracks. To use that technique here MATLAB 7.10 is used & the crack has been determined. Now adays fuzzy logic is gaining wide scope in determining crack. Here fuzzy logic predicts the crack size by comparing the amplitude coming out of the bearing at different loading conditions & different rotating speed.

Keywords – Roller Bearing, Healthy bearing, Defective bearing, Kurtosis.

I. INTRODUCTION

Roller bearing’s structure is simple and is widely applied. Acoustic emission (AE) is the phenomenon of transient elastic wave generation in materials under stress. When the material is subjected to stress at a certain level, a rapid release of strain energy takes place in the form of elastic waves which can be detected by transducers placed on it [1]. Plastic deformation and growth of cracks are among the main sources of acoustic emission in metals. AE technique is, therefore, widely used in nondestructive testing for the detection of crack propagation and failure detection in rotating machinery [3]. Failure to monitor the condition of machine components is not only intolerable but will also result in unnecessary maintenance costs. A crack in a structure induces a local flexibility which affects the dynamic behavior of the whole structure to a considerable degree.The position and depth of cracks. Most of the researches used in their studies are open crack models, that is, they assume that a crack remains always open during vibration. The assumption of an open crack leads to a constant shift of natural frequencies of vibration. By using several fuzzy rules the results will be obtained for crack depth and crack location in the Matlab Simulink environment.

II. BEARING THEORY : SKF CYLINDRICAL ROLLER BEARING MODEL- N 307

The Roller bearing SKF N 307 is used for analysis having specifications given above refered from standard design data book & SKF bearing specification data book.

Sr. No. Name Data1 Coeficient of Friction (µ) 0.0012 to 0.0060 2 Kn const value of bearing Grease lubricates 500000 For oil lubricated 630000 3 Basic load Rating Static (Co)=40.9 KN Dynamic(C)=46.8KN 4 Fatigue Load Fuf 8150 N 5 Permissible speed 9000 to 9500 rpm 6 Design Specification D=80 B=21, d=35 rmin=1.5, r1 = 1.1 E= 70.2 7 Bearing Material Chrome Steel – SAE 52100

International Journal of Latest Trends in Engineering and Technology (IJLTET)

Vol. 3 Issue 4 March 2014 170 ISSN: 2278-621X

III. PROBLEM DEFINITION Uptill now there are number of theories & paper were published on determination of defect size & location

with various techniques. Here the artificial crack has been generated of size 5x10mm, 10x10mm, 10x20mm & compared with Healthy bearing i.e. bearing without defect. The frequency resopnce curve obtained from experimentation can bring various amplitude for varrying crack sizes, these amplitudes can predicts the crack size.

IV. METHODOLOGY

Fig. 1 Test rig

1) Initially bearing without defect i.e. healthy bearing is applies to the test rig, at no load & different RPM readings are taken. Then for various load i.e. 0.5kg, 1kg, 1.5 kg & 2 kg and 1000, 1500 RPM radings are taken.

2) Secondly healthy bearing is replaced by 5x10 crack bearing & same procedure is followed, & frequency response curve are obtained.

3) The 5x10 crack bearing replaced by 10x10 crack bearing & same procedure is followed for obtaining frequency response curve.

4) The 10x10 crack bearing replaced by 10x20 crack bearing & same procedure is followed for obtaining frequency response curve.

Difference between amplitude values for different bearingsRPM Loading Healthy bearing 5x10 10x10 10x20

velocity kg Amplitude (mm)

Amplitude (mm)

Amplitude (mm)

Amplitude (mm)

1000

0 0.012 0.013 0.10 0.26 0.5 0.014 0.018 0.12 0.41 1 0.018 0.020 0.13 0.45

1.5 0.051 0.06 0.14 0.5 2 0.052 0.07 0.15 0.6

1500

0 0.046 0.046 0.18 0.78 0.5 0.047 0.052 0.19 0.81 1 0.048 0.053 0.20 0.88

1.5 0.049 0.086 0.21 1.0 2 0.051 0.091 0.22 1.2

RESULT TABLE 4.1

V. RESULT AND DISCUSSIONHere Helthy bearing’s frequency response curves has been presented & the cracked bearing curve results are tabulated at Table 4.1



FIG 5.1 Load- 0 kg

FIG 5.2 Load- 0.5 kg

FIG 5.3 Load- 1 kg

FIG 5.4 Load- 1.5 kg

FIG 5.5 Load- 2 kg



VI. RESULTS OF FUZZY LOGICFOR HEALTHY & VARIOUS CRACKED BEARING ROLLER BEARING

FIG 6.1 Membership Function for Varrying Load & 1000 RPM

Fig. 6.2 Membership Function for Healthy Bearing

SURFACE VIEWER – RESULT

For 1000 RPM

Fig. 6.3 Amplitude Vs Load At 1000 Rpm Fig. 6.4 Amplitude Vs Load At 1000 Rpm

For Healthy Bearing For 5X10 Crack Bearing




For 10X10 Crack Bearing For 10X20 Crack Bearing

For 1500 RPM


For Healthy Bearing For 5X10 Crack Bearing

Fig. 6.7 Amplitude Vs Load At 1500 Rpm Amplitude Vs Load At 1500 Rpm

For 10X10 Crack Bearing For 10X 20 Crack Bearing

From above results few curves of Healthy bearings has been shown, & the amplitude of those results are compared by fuzzy logic. The fuzzy logic technique generates 3-D surface which helps us to understand the variation of amplitude with respect to various velocity in RPM & varrying load.�

VI.CONCLUSIONThe healthy bearings i.e. bearing without crack is having less amplitude as compared to the remaining

cracked or faulty bearings. Hence crack size is directly proportional to kurtosis i.e. peak amplitude, obtained in frequency analysis curves. The fuzzy logic surface view shows its heighest peak values for faulty bearing compared to Healthy bearing. From above analysis it has been clear that for High load carrying capacity bearings at low speed & low loading conditions crack can also be determined.

REFERENCES�

[1] Abdullah M. Al-Ghamda, David Mba, “A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size”, Cranfield University, 20 (2006) 1537–1571

[2] B. Eftekharnejad n, M.R. Carrasco, B. Charnley, D. Mba, “ The application of spectral kurtosis on Acoustic Emission and vibrations from a defective bearing” Mechanical Systems and Signal Processing 25 (2011) 266–284

[3] B. Kilundu, X. Chiementin, J. Duez, D. Mba, “Cyclostationarity of Acoustic Emissions (AE) for monitoring bearing defects” Mechanical Systems and Signal Processing 25 (2011) 2061–2072



[4] M. Elforjani *, D. Mba, Accelerated natural fault diagnosis in slow speed bearings with Acoustic Emission.(2011)1525-1570 [5] Abdullah M. Al-Ghamd, “ D. Mba A comparative experimental study on the use of Acoustic Emission and vibration analysis for bearing

defect dentification and estimation of defect size” Cranfield University, 20 (2006) 1537–1571 [6] D. Mba, “Acoustic Emissions and monitoring bearing health”, Tribology Transactions, 46 (3), pp. 447-451, 2003. [7] B. Eftekharnejad n, M.R.Carrasco,B.Charnley,D.Mba, The application of spectral kurtosis on Acoustic Emission and vibrations from a

defective bearing Cranfield University 25 (2011) 266–284 [8] A.W. Warren, Y.B. Guo, “Acoustic emission monitoring for rolling contact fatigue of super finished ground surfaces” International

Journal of Fatigue 29 (2007) 603–614. [9] Saad Al-Dossary, R.I. Raja Hamzah, D. Mba, “Observations of changes in Acoustic emission waveform Of or varying seeded

defect sizes in a rolling element bearing” Applied Acoustics 70 (2009) 58–81. [10] Yongyong He, Xinming Zhang, Michael I. Friswell, “Defect Diagnosis for Rolling Element Bearings Using Acoustic Emission” DOI:

10.1115/1.4000480. [11] Chaochao Chenn, George Vachtsevanos, “Bearing condition prediction considering uncertainty: An intervaltype-2 fuzzy neural

network approach” Robotics and Computer-Integrated Manufacturing 28 (2012) 509–516. [12] V. Sugumaran a, K.I. Ramachandran b, “Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus

on automatic rule learning” Expert Systems with Applications 38 (2011) 4901–4907. [13] G.N. Marichal, Mariano Arte, J.C. Garcıa, Prada, O. Casanova, “Extraction of rules for faulty bearing classification by a Neuro-

Fuzzy approach” Mechanical Systems and Signal Processing 25 (2011) 2073–2082. [14] Paul M. Frank and Birgit Kiippen-Seliger, “Fuzzy Logic and Neural Network Applications to Fault Diagnosis,” Gerhard-Mercator-

Universitiit-GH Duisburg, Duisburg, Germany [15] V. Sugumaran , K.I. Ramachandran, “Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing”

Mechanical Systems and Signal Processing 21 (2007) 2237–2247.



fault diagnosis of roller bearing using acoustic emission

Documents