Abstract
This paper presents an innovative approach to the extraction of an indicator for the monitoring of bearing degradation. This approach is based on the principles of the empirical mode decomposition (EMD) and the Hilbert transform (HT). The proposed approach extracts the temporal components of oscillating vibration signals called intrinsic mode functions (IMFs). These components are classified locally from the highest frequencies to the lowest frequencies. By selecting the appropriate components, it is possible to construct a bank of self-adaptive and automatic filters. Combined with the HT, the EMD allows an estimate of the instantaneous frequency of each IMF. A health indicator called the Hilbert marginal spectrum density is then extracted in order to detect and diagnose the degradation of bearings. This approach was validated on two test benches with variable speeds and loads. The obtained results demonstrated the effectiveness of this approach for the monitoring of ball and roller bearings.
Highlights
Introduction to BearingsBearings are mechanical components used to provide a mobile connection between Bearings are mechanical components used to provide a mobile connection between two rotating elements
Studies conducted on induction motors have classified the most common fault mechStudies conducted on induction motors have classified the most common fault mechaanisms accordingtotobearing bearing faults, stator faults, rotor faults, and other faults
The empirical mode decomposition (EMD) is a local, iterative, adaptive approach that describes the signal as local oscillatory components gosubtraction of intrinsic mode functions (IMFs), which makes the reconstruction fast and visual because it consists ing from the high to low frequencies while the wavelet method is a parametric approach of a simple summation
Summary
Bearings are mechanical components used to provide a mobile connection between Bearings are mechanical components used to provide a mobile connection between two rotating elements. Indicators are precise about the the health of bearings These These indicators are mainly mainly based on the calculation of the rms the value, thefactor, crest and factor, and kurtosis. Authors in [11] listed the main challenges reported by past researchers of bearing condition monitoring, such as the signal background noise, the interference of the bearing signal with other components of the studied system (gears, shafts, etc.), the speed and load fluctuations, etc To overcome these issues, several approaches were proposed by later works, including wavelet transform-based techniques [12], the Hilbert–Huang transform technique (HHT) [13], empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD) [14,15]. We will show how the EMD is associated with the Hilbert transform (HT) to detect the envelope of vibration signals for the determination of their instantaneous frequencies and will apply it for the denoising of vibration signals in order to extract our health indicator
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