Combination of envelope spectra and generative topographic mapping to diagnose bearing fault and evaluate degradation of bearing

Abstract

Functioning of rotary machines is based on the rolling element of bearings, as the rolling element of bearings is the crucial component of rotary machines. The entire operation of rotating machines may terminate after the failure of rolling element of bearings. Hence, the failure of rotating machine is mainly dependent on rolling element of bearings, as it may break down an entire process. Thus, to avoid the sudden breakdown of rotary machines, it is essential to develop the diagnostics and prognostics methodology of rolling element of bearings. In the rolling element of bearings, the assessment of bearing degradation and fault diagnostics is essential to establish the methodology. Therefore, this article proposes a methodology to diagnose the fault of bearing and to assess bearing degradation. Envelope spectra and generative topographic mapping are used together in the methodology proposed. Selection of frequency band based on encompasses one or more resonance of spectrum of signal. Envelope spectrum is determined to pick up the peak of the characteristic fault frequencies of the bearing. Extraction of features from time and frequency domains is used in the generative topographic mapping. Health degradation index, an index termed as evolution of bearing degradation, is obtained from the classifier of generative topographic mapping. Experiments were conducted on the bearings to verify the proposed methodology. On interpreting the obtained results, it was found that the proficiency of proposed methodology is most appropriate for detecting the fault in bearing and tracking the degradation evolution in bearings.

Fault feature extraction method of gear based on spectral amplitude modulation and autocorrelation analysis

“…4 When fault occurs, the rotation frequency of the faulty gear modulates the meshing frequencies and their multiple frequencies, which makes the gear fault signal show an impact modulation phenomenon. 5 In order to effectively extract the meaningful information from vibration signals for gear fault detection, many methods have been developed to extract the gear fault features, such as the spectrum, 6 envelope demodulation, 7 cepstrum analysis, 8 and fast kurtogram. 9…”