Abstract

This study focuses on the defect detection of rolling element bearings using a novel method. In the bearing fault diagnosis, the fault feature extraction is also a key issue for successful fault detection. However, the vibration signals acquired by accelerometers are often mixed with noise signals. These mixtures may influence the fault feature extraction and hence deteriorate the performance of the fault diagnosis. To address this problem, a novel method is presented in this study to enhance the performance of the fault diagnosis of rolling element bearings. In this new method, the Kernel Independent Component Analysis (KICA) was firstly employed to fuse multi-sensor signals to eliminate noise signals. Then a RBF network was used to classify the fault patterns. To improve the fault identification, the Genetic Algorithm (GA) was adopted to optimize the parameters of the RBF network. Experiment tests on a rolling bearing fault diagnosis set-up have been carried out to verify the performance of the proposed method. The test results show that show the KICA can remove the noise effectively and the GA optimized RBF network can provide accurate fault detection results. In addition, the new method has been compared with the single RBF network model and ICA-RBF model. The comparison indicates that the proposed KICA-RBF model outperforms the other two rivals with a fault detection rate of 92.3%.

Keywords:

Fault diagnosis, KICA, RBF, rolling element bearings,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.