This article presents the experimental and statistical methodology for localized fault analysis in the rotor-bearing system. These defects on outer race, on inner race, and on a combination of ball and outer race are considered. In this study speed, load and defects were considered as the essential process variables to understand their significance and effects on vibration response for the rotor-bearing system. Three factors at three levels were considered for experimentation, and the experiment was designed for L27 based on design of experiments (DOE) methodology. From the experiments, the vibration response results are recorded in terms of root mean square value for the analysis. Response surface methodology (RSM) is used for identifying the interaction effect of varying process parameters upon the response of vibrations by response surface plot. The rotor-bearing test setup is used for experimentation and is analyzed by using DOE. This study establishes the prediction of fault in the rotor-bearing system in combined parametric effect analysis and its influence with DOE and RSM.
Holm-HansenBTGaoRX. Vibration analysis of a sensor-integrated ball bearing. J Vib Acoust2002; 122(4): 384–392.
2.
GaoRXYanRShengS, et al. Sensor Placement and Signal Processing for Bearing Condition Monitoring. London: Condition Monitoring & Control for Intelligent Manufacturing, 2006, pp. 167–191.
3.
TyagiC. Comparative study of SVM classifiers and artificial neural networks application for rolling element bearing fault diagnosis using wavelet transform preprocessing. Int J Mech Mechatronics Eng2008; 2(7): 309–317.
RaiVKMohantyAR. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform. Mech Syst Signal Process2007; 21(6): 2607–2615.
6.
ShiDFWangWJQuLS. Defect detection for bearings using envelope spectra of wavelet transform. J Vib Acoust2004; 126(4): 567–573.
7.
CaoHFanFZhouK, et al.Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement2016; 82: 439–449.
8.
El-ThaljiIJantunenE. A summary of fault modelling and predictive health monitoring of rolling element bearings. Mech Syst Signal Process2015; 60-61: 252–272.
9.
GunerkarRSJalanAKBelgamwarSU. Fault diagnosis of rolling element bearing based on artificial neural network. J Mech Sci Technol2019; 33(2): 505–511.
10.
YuYYuDejieJunshengC. A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J Sound Vib2006; 294: 269–277.
11.
Al-RaheemKFRoyARamachandranKP, et al.Application of the Laplace-Wavelet combined With ANN for rolling bearing fault diagnosis. J Vib Acoust2008; 11(4): 18–24.
12.
ChowM-yMangumPMYeeSO. A neural network approach to real-time condition monitoring of induction motors. IEEE Trans Ind Electro2002; 38(6): 448–453.
13.
AlguindigueIELoskiewicz-BuczakAUhrigRE. Monitoring and diagnosis of rolling element bearings using artificial neural networks. IEEE Trans Ind Electron1993; 40(2): 209–217.
14.
SamantaBAl-BalushiKR. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech Syst Signal Process2003; 17(2): 317–328.
15.
SamantaBAl-BalushiKRAl-AraimiSA. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng Appl Artif Intelligence2003; 16(7–8): 657–665.
16.
SugumaranVMuralidharanVRamachandranKI. Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mech Syst Signal Process2007; 21(2): 930–942.
17.
WidodoAYangB-S. Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process2007; 21(6): 2560–2574.
18.
HuQHeZZhangZ, et al.Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mech Syst Signal Process2007; 21(2): 688–705.
19.
OcakHLoparoKA. HMM-Based fault detection and diagnosis scheme for rolling element bearings. J Vib Acoust2005; 127(4): 299–306.
20.
JalanAKMohantyAR. Model based fault diagnosis in rotating machinery. Int J Performability Eng2011; 7(6): 515–523.
21.
JalanAKMohantyAR. Model based fault identification of unbalance and misalignment simultaneously present in a rotor system. Adv Vib Eng2013; 12(1): 23–32.
22.
GunerkarRSJalanAK. Classification of ball bearing faults using vibro-acoustic sensor data fusion. Exp Tech2019; 43: 635–643. DOI: 10.1007/s40799-019-00324-0, 1-9.
23.
WangWQGolnaraghiMFIsmailF. Prognosis of machine health condition using neuro-fuzzy systems. Mech Syst Signal Process2004; 18(4): 813–831.
24.
ChenJRandallRB. Intelligent diagnosis of bearing knock faults in internal combustion engines using vibration simulation. Mechanism Machine Theor2016; 104: 161–176.
25.
MyersRHMontgomeryDC. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. 2nd ed. New York, USA: Wiley, 2002.
26.
GunerkarRJalanABelgamwarS. Experimental study of rolling element bearing with localized defect using response surface methodology. In: International conference on advances and soft computing applications in design and manufacturing, Ashok Rajpath, Patna, 4–6 June 2018.
27.
PatilMSMathewJRajendrakumarPK, et al.Experimental studies using response surface methodology for condition monitoring of ball bearings. J Tribology2010; 132(41–6): 044505.
28.
KankarPKSharmaSCHarshaSP. Fault diagnosis of high-speed rolling element bearings due to localized defects using response surface method. J Dynamic Syst Meas Control2011; 133(31–14): 031007.
29.
GoyalDVanrajPablaBS, et al.Non-contact sensor placement strategy for condition monitoring of rotating machine-elements. Eng Sci Tech Int J2019.
30.
SinghSHowardCQHansenCH. An extensive review of vibration modelling of rolling element bearings with localised and extended defects. J Sound Vib2015; 357: 300–330.
