Curvature enhanced bearing fault diagnosis method using 2D vibration signal

As a novel representation method, two dimensional (2D) segmentation is gaining ground as an effective condition monitoring method due to its high-level information descriptional ability. However, the accuracy of extracting frequency information is still limited by the finite gray-level and the extraction ability of distinguishable texture for each fault. To overcome these drawbacks, this research proposes a bearing fault diagnosis method using the converted 2D vibrational signal matrices. In this method, 1D vibration signals are converted into 2D matrices to exploit the fault signatures from the converted images. Curvature filtering (mean curvature) algorithm is applied to eliminate the overwhelming interfering contents and preserves the necessary edge information contained in the 2D matrix. In addition, the histogram of oriented gradients features is employed for the effective fault feature extraction. Finally, a one-versus-one support vector machine is utilized for the automatically fault classification. An experimental investigation was carried out for the performance evaluation of the proposed method. Comparison results indicate that the established method is capable of bearing fault detection with considerable accuracy.