Rolling bearing fault diagnosis based on improved adaptive parameterless empirical wavelet transform and sparse denoising

Li, Jimeng; Wang, Hui; Wang, Xiangdong; Zhang, Yungang

doi:10.1016/j.measurement.2019.107392

Cited by 56 publications

(24 citation statements)

References 30 publications

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“…Harmonic fault diagnosis and identification using a modified WT known as the harmonic WT is used in [14]. The denoising of the signal and detection of motor bearing faults using the WT has been proposed in [45]. The WT has also been used to detect induction motor faults such as stator open and short-circuiting and speed sensor faults [46].…”

Section: Comparison Of This Study With Existing Studiesmentioning

confidence: 99%

Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

et al. 2021

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In modern power systems, since most loads are inductive by nature, there is an ongoing power quality issue and researchers’ interest in improving the power factor is widespread, as inductive loads have a low power factor that depletes the system’s capacity and has an adverse effect on the voltage level. The measurement and acute analysis of voltage- and current-level waveforms is essential to tackle power quality issues. This article presents a detailed case study and analysis of real-time data measured from a frequency converter, which is used to operate the motor of a ventilation system. The output of the frequency converter is a highly distorted current wave. A hybrid Fourier transform (FT)- and wavelet transform-based solution has been proposed here to diagnose and identify the causes of motor failure in the ventilation system. The traditional FT did not give a detailed analysis of this type of signal, which is highly contaminated by noise. Therefore, first, the signal is preprocessed for data denoising using the wavelet transform. Second, the Fourier analysis is performed on the filtered signal for frequency analysis and segregation of fundamental frequency components, higher-order harmonics, and suppressed noise. The spectrum analysis reveals that the noise is generated due to the rapidly switching circuits in the frequency converter and this unfiltered signal at the output of the frequency converter causes motor failure.

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Section: Comparison Of This Study With Existing Studiesmentioning

confidence: 99%

Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

et al. 2021

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“…In the engineering field, the commonly used time-frequency analysis methods are used to extract fault features from noise interference [2][3][4][5][6][7][8][9]. It can be roughly summarized as ways based on Fourier transform and not based on Fourier transform [10,11].…”

Section: Introductionmentioning

confidence: 99%

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Guo

Yang

Cheng

et al. 2022

Shock and Vibration

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As the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (BMD) method previously proposed is to search in the association dictionary library containing broadband and narrowband signals. However, when it is applied to the broadband signals interfered by strong noise, the decomposition is easy to produce modal confusion, so the modulated broadband mode decomposition (MBMD) method is proposed. The fault signal just can be analyzed by MBMD, so it is applied to the fault diagnosis of low-speed hub bearing of the crane. To realize the fault identification of low-speed hub bearing of the crane, firstly, the original signal is decomposed by MBMD. Secondly, the eigenvalues of the first three-component signals are calculated, the eigenvalue matrix is constructed, and the marked features are selected by the distance evaluation technique (DET). Finally, the marked features are input into BP neural network for training and testing to identify the types of bearing fault. Compared with EEMD, VMD, and BMD, the MBMD method combined with BP neural network has good performance in feature extraction and fault identification.

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“…Ahmed et al [19] showed the proper application of the monitoring system in machining centers by using acoustic emission responses. Li et al [20] used empirical wavelet transform to conform denoising of vibration responses. They focused on machining components or tools and treated the response or FFT data with statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear Monitoring

et al. 2021

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Friction between metals is a physical phenomenon that occurs in manufacturing machine tools. This annoying noise implies unnecessary metal contact and deterioration of a mechanical system. In this study, for the monitoring of the friction between two metal surfaces, the acoustic signature was extracted by applying the wavelet transform method to the noise measured from the change in contact force for each state of adhesive and abrasive wear. Experiments were conducted with a constant relative speed between the contacting metal surfaces. For the adhesive wear, the peak signal-to-noise ratio (PSNR) calculated by the wavelet transformation increases with the increasing contact pressure. Opposite trends were observed for the abrasive wear. The proposed index formed a group within a specific range. This ratio exhibited a strong relationship with the wear characteristics and the surface condition. From the proposed index calculated by the wavelet coefficients, the continuous monitoring of the wear influence on the failure of the machine movement operations is achieved by the sound radiation from the contacting surfaces.

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Rolling bearing fault diagnosis based on improved adaptive parameterless empirical wavelet transform and sparse denoising

Cited by 56 publications

References 30 publications

Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear Monitoring

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