A roller bearing fault diagnosis method based on EMD energy entropy and ANN

Wavelet Entropy-Based Traction Inverter Open Switch Fault Diagnosis in High-Speed Railways

“…Essentially speaking, the EMD method is a kind of smoothing process, which separates the different scale signals to generate several series. Each series is called an IMF, which must satisfy the following constraints [26]:…”

“…Essentially speaking, the EMD method is a kind of smoothing process, which separates the different scale signals to generate several series. Each series is called an IMF, which must satisfy the following constraints [26]: (1) In the whole data set, the number of extrema and the number of zero-crossings must either be equal or differ at most by one. (2) At any point, the mean value of the envelope defined by local maxima and the envelope defined by the local minima is zero.…”

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

“…In [8], the authors proposed a diagnosis method based on artificial neural networks (ANN). The entropy of each IMF was estimated to determine which IMF signal should be selected for the training process of the ANN network.…”

Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

“…Peng et al suggested an improved Hilbert-Huang transform using wavelet packet transform and applied an IMF selection based on correlation coefficients [12] and Yu et al proposed the concept of EMD energy entropy and utilized its value to identify different bearing fault types [13]. Junsheng et al exploited singular values of IMFs as fault feature vectors of support vector machines [14] and Ricci et al presented an automatic IMF selection method using a merit index [15].…”