Title :
Classification of rolling element defect by extraction of defect features using wavelet transform and ANN
Author :
Kumar, Anil ; Kumar, Rajesh
Author_Institution :
Precision Metrol. Lab.: Dept. of Mech. Eng., Sant Longowal Inst. of Eng. & Technol., Longowal, India
Abstract :
This paper presents a scheme for classification of rolling elements defect in a cylindrical roller bearing. Defect features are extracted from raw signal, and TMI graph. The extracted features are utilized to train the feed forward ANN. After training, test features are applied to ANN for the classification of defect. Accuracy of the proposed method in the classification of defects is 94%.
Keywords :
feature extraction; feedforward neural nets; learning (artificial intelligence); rolling bearings; signal classification; vibrational signal processing; wavelet transforms; cylindrical roller bearing; defect features feature extraction; feed forward ANN training; rolling element defect classification; test features; wavelet transform; Artificial neural networks; Continuous wavelet transforms; Time-frequency analysis; Vibrations; artifical neural network (ANN); cylindrical roller bearing; feature extraction; rolling element defect; wavelet transform;
Conference_Titel :
Industrial Instrumentation and Control (ICIC), 2015 International Conference on
Conference_Location :
Pune
DOI :
10.1109/IIC.2015.7150743
