چکیده مقاله
Ball bearing is one of the most widely used components in rotary machines Condition monitoring of such elements is counted as pattern recognition problem Pattern recognition has three main steps: feature extraction, feature reduction and classification We use features obtained from three different representations of measured signals which are time, frequency, and time frequency domains In this study smoothed pseudo wigner ville distribution is used for feature calculation in time frequency domain All of the features are extracted from vibration signals The signals from a piezo electric transducer are captured for the following conditions: healthy bearing and defective bearings with inner race, outer race and ball faults In addition, experiments are repeated under various load conditions After calculation of features, principal component analysis is employed for redundancy reduction Finally K NN classifier is built and tested in order to identify the condition of the ball bearing Experimental results demonstrate that the proposed method is effective
کلیدواژهها
نویسندگان
A Kahirdeh
Msc student of Mechanical engineering, IUST
M.S Safizadeh
Mechanical engineering Professor, IUST
شیوه ارجاع
Kahirdeh, A and Safizadeh, M.S,1386,Automatic Fault Diagnosis of Rolling Element Bearings via Principal Component Analysis and Nonlinear Classifier,02nd Tehran International Congress on Manufacturing Engineering (TICME2007),Tehran
ارائهشده در
مجموعه مقالات دومین کنفرانس بین المللی و هشتمین کنفرانس ملی مهندسی ساخت و تولید10 اسفند 1386 · تهران