Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis

Due to the noise accompanied with rolling element bearing fault signal, it can reduce the accuracy of faulty diagnoses. In order to improve the robustness of a faulty diagnosis, this study proposed a fault diagnosis model based on modified local linear embedding (M-LLE) algorithm. Aiming at the characteristics of rolling element bearing fault data, the vibration signal was first analyzed in time domain and frequency domain to construct high dimension eigenvectors. Next, the high-dimensional eigenvectors can be reduced to low-dimensional eigenvectors by M-LLE algorithm. In the M-LLE algorithm, the Mahalanobis distance (MD) metric is adopted to replace Euclidean distance in traditional neighborhood construction and L1-norm is used to standardize weight matrix, which can enhance the anti-noise ability of the Local Linear Embedding (LLE) algorithm. Finally, fault diagnosis results can be obtained when low-dimensional rolling element bearing fault data is classified by K-Nearest Neighbor (KNN) classifier. By simulating the noisy artificial data sets in different degrees, the proposed algorithm can get the perfect local structure of manifolds. The effectiveness of M-LLE algorithm can be proved. In addition, experimental results of real rolling element bearing data which provided by the University of Cincinnati show that the accuracies of all kinds of faults can reach 100%. It can be deemed that the proposed fault diagnosis model can effectively improve the accuracy of fault diagnosis.