Jixie chuandong (Jan 2016)
A New Time-frequency Domain Feature Extraction Method for Rolling Bearing Fault Diagnosis
Abstract
For fault diagnosis of rolling bearings,a new feature extraction strategy based on short time Fourier transform（ STFT） and bag of wordss（ BOW） is proposed. Based on the generate mechanism of bearing fault,the different bearing vibration signals have relevant energy distribution. But in the factory,some factors like signal interference or environment noise will destroy the energy distribution. When using BOW,it regards the distribution of energy in frequency domain each time frame as a word,so segments of signal will be documents which are made up of many words. It shows the energy distribution directly in data perspective. Then,with the new features and SVM classifier,the results of fault diagnosis can be known. At last,effectiveness of the proposed method is verified,vibration from SQI- MFS platform and CWRU platform are analyzed. The results in experiments shows that this method is better than RMS and WE&WEE. So the new feature can be used in fault diagnosis area.
