An intelligent rolling bearing fault diagnosis method is proposed using empirical mode decomposition (EMD)–Teager energy operator (TEO) and Mahalanobis distance. EMD can adaptively decompose vibration signals into a series of intrinsic mode functions (IMFs), which are zero mean monocomponent AM–FM signals. TEO can estimate the total mechanical energy required to generate signals. Thus, TEO exhibits good time resolution and self-adaptive ability with regard to the transient components of the signal, which is an advantage in detecting signal impact characteristics. With regard to the impulse feature of the bearing fault vibration signals, TEO can be used to detect the cyclical impulse characteristic caused by bearing failure, gain an instantaneous amplitude spectrum for each IMF component, and then identify the characteristic frequency of a single, interesting IMF component in the bearing fault by means of the Teager energy spectrum. The amplitude of the Teager energy spectrum in the inner race and outer race fault frequencies, as well as the ratio of the energy of the resonance frequency band to the total energy, were extracted as feature vectors, which were then separately used as training samples and test samples for fault diagnosis. Thereafter, the Mahalanobis distances between the real measure and the different overall types of fault samples were calculated to classify the real condition of the rolling bearing. Finally, the Mahalanobis distances were converted into CV values, which assessed the current health state of the rolling bearing. Experimental results prove that this method can accurately identify and diagnose different fault types of rolling bearings.
Rolling Bearing Fault Diagnosis Based on SVDP-Based Kurtogram and Iterative Autocorrelation of Teager Energy Operator
