NEW APPROACH OF CLASSIFICATION OF ROLLING ELEMENT BEARING FAULT USING ARTIFICIAL NEURAL NETWORK
The paper presents a new approach to the classification of rolling element bearing faults by implementing Artificial Neural Network. Diagnostics of rolling element bearing faults actually represents the problem of pattern classification and recognition, where the key step is feature extraction from the vibration signal. Characterization of each recorded vibration signal is performed by a combination of signal's time-varying statistical parameters and characteristic rolling element bearing fault frequency components obtained through the frequency spectrum analysis method. The experimental data is collected for four bearings at three different speeds. The sensor is located at three different positions for each bearing. Both time domain and frequency domain signals were measured. Thus the data was three time spectrums and three frequency spectrums for each speed for a bearing. The entire data set comprised of 72 (6 x 3 x 4) data. The time domain signal was comprised of 8192 samples and extracting these features from a huge data set was difficult. To overcome this difficulty the 8192 samples were split into 32 bins each containing 256 samples. Two Network RBFN and PNN are used to classify the bearing defects. The entire process of splitting and evaluating the seven features was coded in MATLAB. From these seven features the most suitable features are for explaining the intensity of the defect is discussed.
Key Words: Feature Extraction; Fault Frequencies; Roller Bearing; Bearing fault; Crest Factor; Variant;
Radial Basis Function Network (RBFN); Probabilistic Neural Network (PNN)
Journal of Mechanical Engineering, Vol. ME 40, No. 2, December 2009 119-130