Abstract
It is known that the vibration impulses occurred from a bearing defect are non-periodic but cyclostationary due to the slippage of rollers. The vibration status is often perceived to be synonymous with quality and thus used for predictive maintenance before breakdown. As a result, the analysis of vibration has been used as a key condition tool for fault detection, diagnosis, and prognosis. Any defect in a bearing causes some vibration that consists of certain frequencies depending on the nature and location of the defect. Although many techniques for time–frequency analysis are reported to measure vibration signals, they were found less efficient in practical applications. For this reason, this article develops an on-line bearing vibration detection and analysis using enhanced fast Fourier transform algorithm. The relation between major vibration frequency and dispersed leakage caused from fast Fourier transform can be induced, and it is then used to establish a mathematical model to find major frequencies of vibration signal. Also, the dispersed energy can be collected to retrieve its original gravitational acceleration. The proposed model is developed using a simple arithmetic operation based on fast Fourier transform so that it is feasible for more efficient calculation in impulse signal analysis. Both measurement calibration and practical results verify that the proposed scheme can achieve accurate, rapid, and reliable outcomes.
Highlights
The rolling bearings are being applied almost in every type of rotating machinery
The Fourier transform (FT) analysis is used to reconstruct a periodical waveform by series harmonic components, where harmonic frequency is defined as a multiple of fundamental
For the purpose of measurement calibration, the proposed e-fast Fourier transform (FFT) model is first performed using known quantity of hardware signals generated from the function generator
Summary
The rolling bearings are being applied almost in every type of rotating machinery. With improvement in modern manufacturing technology and materials, the bearing fatigue life is not the limiting factor of failures in service. A number of techniques for time–frequency analysis such as short-time Fourier transform (STFT), Wigner– Ville distribution (WVD), and continuous wavelet transform (CWT) are available to decompose complicated signals. The effectiveness in identification of transient elements in the dynamic signal depends on the type of the wavelet function.[22,23,24,25,26] In view of such constraints, the Hilbert–Huang transform (HHT) approach provides multi-resolution in the instantaneous frequencies resulting from the intrinsic mode functions (IMFs) of the signal. The vibration signal can be analyzed using IMFs that is extracted from the process of empirical mode decomposition (EMD) It is, that HHT may lead to errors in characteristic defect frequencies of the rolling-element bearings. The time resolution affects its corresponding frequency of the signal significantly.[27,28,29]
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