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Bearing fault impulses are easily submerged by background noise, resulting in the inconspicuous fault feature and affecting the accuracy of the fault diagnosis. This paper presents a novel method for bearing the fault feature enhancement and diagnosis based on the statistical filtering and the 1.5-dimensional symmetric difference analytic energy operator (1.5D-SDAEO) to solve the problem. 1). The statistical filtering is used to filter the background noise under the standard distinction index. 2). The 1.5D-SDAEO is used to enhance the signal impulse, suppress the residual noise, and improve the SNR. 3). The dominant frequency in the energy spectrum is compared with the rolling bearing fault characteristic frequency to the fault diagnosis. The feasibility and the superiority of the presented method are verified by the simulation, engineering, and comparison experiments. All results show that the presented method can effectively enhance the fault feature and accurately diagnose the rolling bearing fault.

Fault feature extracted from fault signals plays a vital role in fault detection and diagnosis. However, in the early stage of defect, the fault signals (amplitudes and energy) are very weak and overwhelmed by large noise, and it is very difficult to extract fault feature effectively. To handle this problem, in this paper, an automatic filtering method based on an improved genetic algorithm (IGA) for extracting fault signals is presented. This method improves the binary coding generation of GA, so that each binary value matches a frequency of a fault signal frequency spectrum. Based on designed “statistical information evaluation function” as IGA fitness function, the similarity between normal state signal (noise signal) and IGA extracted noise signal is evaluated, and IGA employs genetic operations to search the optimal noise binary string. Negating noise binary string can obtain optimal fault signal binary string. It will then be multiplied by abnormal state vibration signal frequency spectrum, to yield a new frequency spectrum that only has fault components. This proposed method does not need to seek center frequency and bandwidth, and can automatically remove noise. The effective and advanced performance of the proposed method is evaluated by three different rolling bearing defects (outer race defect, inner race defect, and roller defect) in rolling bearing on rotating machine experimental bench and gearbox. Compared with rolling bearing fault characteristic frequency and results of denoising methods of bandpass filtering and wavelet decomposition, the proposed method can automatically filter noise and effectively extract fault signal. It proves that this method is feasible, effective, and advanced.