Bearing fault diagnosis based on an improved morphological filter

The extraction of repetitive impacts from vibration signals plays an essential role in bearing fault detection. Among different signal processing algorithms, morphological filter (MF) has attracted lots of attention because it could directly extract the geometric structure of the impulsive feature and only needs little computation. However, the conventional MF and some current improvements are based on the local optima of the raw signal to de-noise the noisy signal and its faulty feature extracting capability would be greatly affected by the noise. In this paper, a new improved MF algorithm is proposed to overcome such deficiency. Firstly, morphological gradient (MG) operator is selected in this paper due to its capability of picking up both positive and negative impulses. Then, based on the relationship between the defect induced impulse and a harmonic function with the resonant frequency, the harmonic waveform in a period is adopted to instruct the construction of structuring element (SE). The improved MF can obtain the fault feature from low SNR signals. The processing results of a simulation signal and two sets of experimental signals and a set of comparisons verify the effectiveness and robustness of the proposed method.