Abstract
During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments. In these environments, the vibration signals are first de-noised with the median filter (MF) method and the de-noised signals are separated with an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm. The simulation results found here verify the effectiveness of the proposed method. Finally, the vibration signal of the hybrid rotor is effectively separated by the proposed method. A new separation approach is thus provided for vibration signals in strong pulse noise environments.
Highlights
In the process of rotating machinery operation, the vibration signals measured by vibration sensors are often composed of the vibrations of multiple components [1,2]
When the signal separated by the independent component analysis (ICA) algorithm is close to the corresponding source signal, the closer the value of ρi to 1, the better the separation effect
Since there may be multiple potential source signals in the process of rotor rotation, such as the vibration signal of ball bearings, axial vibration signals, and noise signals from shafts, and since the sensor is measuring at the same time, the signal measured by the sensor is the mixed vibration signal
Summary
In the process of rotating machinery operation, the vibration signals measured by vibration sensors are often composed of the vibrations of multiple components [1,2]. It is very difficult to analyze and process these sensor signals directly, which is bound to cause a lot of difficulties in mechanical condition monitoring and fault diagnosis [4]. Huang et al [9] is a non-stationary signal analysis method that can find the hidden feature information in a signal, and it is widely used in the fault extraction and noise reduction of rotating machinery [10,11,12]. The minimum entropy deconvolution [13,14] designs the optimal filter to eliminate the random noise in the bearing impact signal under the condition of maximizing the kurtosis value. The adaptive filter [15,16] needs to introduce an additional noise signal and extract the bearing fault impact signal by designing an optimal filter. According to the characteristics of bearing fault vibration and impact, the matching trace [17,18]
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