Early fault identification of rolling element using IESCFFOgram

Rabindra Kumar Sahoo *, M Sudhahar and P Vijay Kumar

Department of Mechanical Engineering, PRIST University, Vallam Thanjavur, India.
 
Research Article
International Journal of Science and Research Archive, 2023, 08(01), 367-382.
Article DOI: 10.30574/ijsra.2023.8.1.0022
Publication history: 
Received on 28 November 2022; revised on 11 January 2023; accepted on 14 January 2023
 
Abstract: 
Early fault identification of the rolling element bearings remains difficult because the repetitive transient signature generated via localized incipient damage is easily submerged by various interference components and strong noise. Spectral coherence (SCoh) is a break- through approach for revealing the second-order cyclostationary of bearing faults by displaying the energy flow of vibration signal jointly in a two-dimensional plane comprising the resonance frequency and bearing fault frequency. Considering the non-uniformity of fault in- formation distribution in the whole spectral frequency band, the enhanced envelope spectrum (EES) obtained by integrating over the full spectral frequency band is vulnerable to strong background noise. Thus, how to identify an informative spectral frequency band for constructing a diagnostic improved envelope spectrum (IES) is crucial to accurately identify bearing faults. To address this issue, a feature- adaptive method called IES via Candidate Fault Frequency Optimization-gram (IESCFFOgram) is proposed to determine the in- formative spectral frequency band from SCoh for bearing fault diagnosis. The innovation of this method is to fully excavate the fault information hidden in the SCoh and adaptively determine the informative spectral frequency band according to the identified candidate fault frequencies. The proposed method is tested and validated on simulated signals, vibration datasets obtained from artificial fault bearing experiments, and accelerated bearing degradation tests. In addition, comparisons with state-of-the art methods have been conducted to highlight the superiority of the proposed methodology.
 
Keywords: 
Spectral frequency; Fault Detection; Filtering Algorithm; Kurtosis; Cyclostationarity; Infogram
 
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