Comparison of Advanced Signal-Processing Methods for Roller Bearing Faults Detection

Urbanek, Jacek; Barszcz, Tomasz; Uhl, Tadeusz

doi:10.2478/v10178-012-0063-2

Cited by 13 publications

(6 citation statements)

References 12 publications

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“…The propagation of fault is shown in the frequency band. In their literature, Sun and Tang 22 and Urbanek et al 23 proposed an improvement of sensitivity of envelope spectra. The modulating signal may contain several parts as it is difficult to diagnose the fault.…”

Section: Technical Backgroundmentioning

confidence: 99%

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

Nistane

Harsha

2019

Noise & Vibration Worldwide

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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.

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Section: Technical Backgroundmentioning

confidence: 99%

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

Nistane

Harsha

2019

Noise & Vibration Worldwide

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“…Short-time Fourier transform (STFT), wavelet transform (WT) and Wigner-Ville distribution (WVD) are commonly used to convert the non-stationary signals into a time-frequency domain [2,3]. Urbanek et al [4] compared the effectiveness of various frequency domain-based signal processing techniques for detecting the faults in a roller bearing. They have concluded that the method using spectral coherence for narrowband envelope analysis was found to be effective in detecting the faults.…”

Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Rex,

Andrews,

Krishnakumari

et al. 2020

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Achieving a reliable fault diagnosis for gears under variable operating conditions is a pressing need of industries to ensure productivity by averting unwanted breakdowns. In the present work, a hybrid approach is proposed by integrating Hu invariant moments and an artificial neural network for explicit extraction and classification of gear faults using time-frequency transforms. The Zhao-Atlas-Marks transform is used to convert the raw vibrations signals from the gears into time-frequency distributions. The proposed method is applied to a single-stage spur gearbox with faults created using electric discharge machining in laboratory conditions. The results show the effectiveness of the proposed methodology in classifying the faults in gears with high accuracy.

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“…However, bearings inevitably degenerate and break down due to an overload of repetitive work. According to statistics, 30 percent of faults in rolling machinery originate from bearing faults; therefore, bearing fault prognostics is an important area of research [2][3][4][5]. Recently, studies on bearing faults prognostics have focused on fault signal feature extraction and fault classification.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning

Yao

Peng

et al. 2017

Applied Sciences

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Abstract:In order to improve the accuracy of the fault diagnosis of roller bearings, this paper proposes a kind of fault diagnosis algorithm based on manifold learning combined with a wavelet neural network. First, a high-dimensional feature signal set is obtained using a conventional feature extraction algorithm; second, an improved Laplacian characteristic mapping algorithm is proposed to reduce the dimensions of the characteristics and obtain an effective characteristic signal. Finally, the processed characteristic signal is inputted into the constructed wavelet neural network whose output is the types of fault. In the actual experiment of recognizing data sets on roller bearing failures, the validity and accuracy of the method for diagnosing faults was verified.

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Comparison of Advanced Signal-Processing Methods for Roller Bearing Faults Detection

Cited by 13 publications

References 12 publications

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

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

Metrology and Measurement Systems

Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning

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