Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM
On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings，in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted，the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.
Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo
W. G. Zhao and L. Y. Wang, "Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM", Advanced Materials Research, Vols. 121-122, pp. 813-818, 2010