Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter

Abstract

Defective rolling bearing response is often characterized by the presence of periodic impulses. However, the in-situ sampled vibration signal is ordinarily mixed with ambient noises and easy to be interfered even submerged. The hybrid approach combining the second generation wavelet denoising with morphological filter is presented. The raw signal is purified using the second generation wavelet. The difference between the closing and opening operator is employed as the morphology filter to extract the periodicity impulsive features from the purified signal and the defect information is easily to be extracted from the corresponding frequency spectrum. The proposed approach is evaluated by simulations and vibration signals from defective bearings with inner race fault, outer race fault, rolling element fault and compound faults, respectively. Results show that the ambient noises can be fully restrained and the defect information of the above defective bearings is well extracted, which demonstrates that the approach is feasible and effective for the fault detection of rolling bearing.

A review of electrostatic monitoring technology: The state of the art and future research directions

“…This paper will not cover the details of how signal processing techniques work, but the authors would like to note that de-noising is processing it. Wavelet transform techniques [75][76][77] have been successfully applied to process non-stationary signals in the past, such as the vibration signal of gear boxes [78], bearings [79] and other rotary mechanical systems [80]. Moreover, from the shape of the output signal (see Figure 1), the electrostatic monitoring signal can be observed to be similar to the electrocardiography (ECG) signal [81].…”

Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering

“…It can be employed to make translation matching and local correction of the original signal from front to back by constructing a certain structuring element (SE), and the morphological characteristics of the original signal are preserved while the noises are suppressed [18]. Currently, morphological filtering has been widely used in image processing [19,20], signal analysis [21], feature extraction [22], and other fields. In this paper, the MF is first introduced to reduce the noise of contaminated chaotic signal.…”

A Fault Feature Extraction Method Based on Second-Order Coupled Step-Varying Stochastic Resonance for Rolling Bearings

“…When the background noise of the vibration data is high and the signal-to-noise ratio (SNR) is low, it is very challenging to obtain the bearings' fault features. In the context of loud noise, the traditional bearing fault analysis methods usually start with noise reduction [5][6][7][8][9][10]. Although these methods can reduce the noise, they also can weaken the effective characteristic signal [10][11][12][13][14][15][16][17][18].…”