An Adaptive Diagnosis Method of Bearing Fault Based on EMD and Tile Coding

To address the low accuracy of conventional bearing fault diagnosis methods that rely on empirical parameters in the scenarios with different noise degrees, this paper presents an adaptive bearing fault diagnosis method combining empirical mode decomposition (EMD) and tile coding. Firstly, the mature adaptive decomposition algorithm, namely the EMD, was employed to decompose the bearing vibration signal. Based on specific criteria, some components were selected as effective signals and further denoised based on spectral averaging to realize adaptive filtering of the bearing vibration signal. Secondly, feature extraction of the filtered signal was conducted using spectral coding based on tile coding. The coding results were input into the K-nearest neighbors (KNN) classifier as features. Finally, for validation, a test dataset with varying noise degrees was constructed to conduct a comparison between the proposed adaptive method and the classic adaptive filtering-based fault diagnosis methods. The results show that when the signal-to-noise ratio is greater than －10 dB, the diagnostic accuracy of the proposed approach exceeds 90% and outperforms that of other methods.