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|Title:||HEALTH DIAGNOSIS OF HIGH SPEED BALL BEARING USING ACOUSTIC EMISSION TECHNIQUE|
|Authors:||Ganpati, Patil Pravin|
|Keywords:||MECHANICAL INDUSTRIAL ENGINEERING|
|Abstract:||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. Health diagnosis of high speed ball bearing can be done by using acoustic emission technique. We have aim to work on experimental setup for getting different AE results, it will help to fmd out the capability of AE technique for detection of bearing abnormalities. The most commonly measured AE parameters are peak amplitude, r.m.s, energy, overall acoustic, power content, ringdown counts and distribution of events by peak amplitude and ringdown counts and statistical parameters such as kurtosis, skewness, crest factor, impulse factor and shape factor. The Experiments are carried out for healthy as well as defective (inner race, outer race, ball, combined defect) bearings at various low speed and high speed. The statistical parameters as well as AE parameters (r.m.s., ringdown counts, distribution of events AE energy) on healthy as well as defective bearings has been evaluated. It is further evaluated the effect of unbalancing of rotor on AE signal characteristics. It is concluded that acoustic emission generated in healthy bearing is expected to be very low level and in case of damaged bearing, defects in bearing led to stress concentration in the bearing along the defect boundary results in drastic increase in the emission rate From physical meaning of AE parameters It is concluded that the rotating speed has strong influence on almost all the AE parameters. In rotor dynamics, AE signal characteristics has been strongly affected by running speed, unbalance mass and eccentricity. The position of the peak of the interval and normal distribution, which will be corresponds to the bearing Characteristic Defect Interval (CDI). ANN and SVM both are found to be good in classification accuracy. ANN tool can be helpful in Prediction of bearing class For the health diagnosis of the bearing by using acoustic emission techniques, effectiveness of loading condition, defect size, bore diameter, running condition parameters, position of sensor on characteristics of AE signal are to be considerable parameters in future|
|Appears in Collections:||MASTERS' DISSERTATIONS (MIED)|
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