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| A Method for Fault Diagnosis of Rolling Bearings Based on Laplacian Eigenmap |
| The traditional linear diagnosis methods for rolling bearing fault with non-stationary and nonlinear running status was not effective. In order to monitor the rolling status accurately and timely, a new diagnosis method was put forward by applying the algorithm of Laplacian Eigenmap (LE) to the diagnosis of rolling. It fully used the advantage of LE algorithm for extracting nonlinear features and reducing dimension for characteristic space matrix in the time domain and frequency domain constructed by vibration signal，extracted the features of running status of external rolling and visualized the clustering results. The experiments using two parameters (between-class scatter and within-distance in pattern recognition) as the measurable indicators simulated four different faults of the bearings and the four different extent of the damage of balls in bearing. Compared with PCA & KPCA, LE clearly identifies the four different faults and the different extent of the damage of balls, and its recognition rate rises greatly. The effectiveness of LE has been Verified by testing samples. |
| College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China |
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Abstract The traditional linear diagnosis methods for rolling bearing fault with non-stationary and nonlinear running status was not effective. In order to monitor the rolling status accurately and timely, a new diagnosis method was put forward by applying the algorithm of Laplacian Eigenmap (LE) to the diagnosis of rolling. It fully used the advantage of LE algorithm for extracting nonlinear features and reducing dimension for characteristic space matrix in the time domain and frequency domain constructed by vibration signal，extracted the features of running status of external rolling and visualized the clustering results. The experiments using two parameters (between-class scatter and within-distance in pattern recognition) as the measurable indicators simulated four different faults of the bearings and the four different extent of the damage of balls in bearing. Compared with PCA & KPCA, LE clearly identifies the four different faults and the different extent of the damage of balls, and its recognition rate rises greatly. The effectiveness of LE has been Verified by testing samples.
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Received: 09 October 2013
Published: 15 March 2015
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