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The opportune fault detection of the rolling element bearings can avoid serious equipment accidents or even casualties. However, the early fault features of the bearings are weak and often submerged in heavy background noise and interferences. This paper proposes a novel approach for bearing fault diagnosis based on the hard thresholding fast iterative filtering (HTFIF), IMF selection index integrating L- kurtosis, correlation coefficient and autocorrelation function impulse harmonic to noise ratio (L-KCA), and improved k-value symmetrical difference analytic energy operator (k-SDAEO). As an adaptive and fast time-frequency analysis method, HTFIF is first adopted to process the bearing vibration signal and obtain a series of IMFs. After that, an alternative fusion index L-KCA is developed to select the sensitive IMF. Finally, a novel k-SDAEO with strong noise robustness is presented to process the selected IMF. With this method, the weak bearing fault signatures can be identified from the energy spectrum. The performance of the proposed method comparing to the traditional methods are investigated by numerical simulation and experimental studies. The results show that the proposed method has better fault feature extraction capability and higher efficiency, which can be implemented in the on-line and real-time fault detection.

An Improved k-Value Symmetrical Difference Analytic Energy Operator With HTFIF and L-KCA for Bearing Fault Diagnosis