EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

In the internal combustion engine noise source separation process, the combustion noise and the piston slap noise are found to be seriously aliased in time-frequency domain. It is difficult to accurately separate them. Therefore, the noise source separation method which is based on Gammatone filter bank and robust independent component analysis (RobustICA) is proposed. The 6-cylinder internal combustion engine vibration and noise test are carried out in a semianechoic chamber. The lead covering method is adopted to isolate the interference noise from numbers 1 to 5 cylinder parts, with only the number 6 cylinder parts left bare. Firstly, many mode components of the measured near-field radiated noise signals are extracted through the designed Gammatone filter bank. Then, the RobustICA algorithm is utilised to extract the independent components. Finally, the spectrum analysis, the continuous wavelet time-frequency analysis, the correlation function method, and the drag test are employed to further identify the separation results. The research results show that the frequency of the combustion noise and the piston slap noise are, respectively, concentrated at 4025 Hz and 1725 Hz. Compared with the EWT-RobustICA method, the separation results obtained by the Gammatone-RobustICA method have very fewer interference components.

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“…However, EEMDbased single channel method has many defects [13][14][15]. The EMD algorithm lacks rigorous mathematical derivation.…”

Section: Introductionmentioning

confidence: 99%

Radiation Noise Separation of Internal Combustion Engine Based on Gammatone-RobustICA Method

Yao

Xiang

Qian

et al. 2017

Shock and Vibration

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“…The SK is a powerful analysis tool to detect transients feature from their noisy vibration signals and has been widely studied and applied in the rotating machine diagnosis, see refs [27] and [28]. First, we apply the EEMD method [29,30] to decompose the original vibration signal, thus the 12 IMF components are generated and displayed in Fig. 10, which can be helping to distinguish the periodic impulse from the mixed noisy signal.…”

Section: Bearing Incipient Fault Diagnosis Based Upon Maximal Spectramentioning

confidence: 99%

Bearing incipient fault diagnosis based upon maximal spectral kurtosis TQWT and group sparsity total variation denoising approach

Li¹,

Liang²

2018

J. vibroeng.

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Abstract. Localized faults in rolling bearing tend to result in periodic shocks and thus arouse periodic responses in the vibration signal. In this paper, a novel fault diagnosis method based on maximal spectral kurtosis tunable Q-factor wavelet transformation (TQWT) and group sparsity total variation denoising (GS-TVD) is proposed to address the issue of bearing incipient failure. Firstly, the range of Q-factor was pre-selected according to the spectral distribution of impulse component, and bearing vibration signal was transformed by the TQWT method. Then, the spectral kurtosis of each scale transform coefficients was calculated, and the optimal Q-factor and decomposition scale can be selected according to the kurtosis maximum principle. In order to remove the interference components and high-frequency noise from the reconstructed vibration signal generated by inverse TQWT, the GS-TVD approach is employed, thus the cyclic periodicity characteristic and transient impulses can be detected obviously. The two cases experimental results indicate that the proposed technique is more effective and applicable for bearing incipient fault diagnosis compared with traditional method.

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“…The additional white noise populates the time-frequency space uniformly. Details can be found in the literature, such as [20].…”

Section: Vibration Analysis Using Empirical Mode Decompositionmentioning

confidence: 99%

Indicative Fault Diagnosis of Wind Turbine Generator Bearings Using Tower Sound and Vibration

2017

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Abstract:The idea of indicative fault diagnosis based on measuring the wind turbine tower sound and vibration is presented. It had been reported by a wind farm operator that a major fault on the generator bearing causes shock and noise to be heard from the bottom of the wind turbine tower. The work in this paper was conceived to test whether tower top faults could be identified by taking simple measurements at the tower base. Two accelerometers were attached inside the wind turbine tower, and vibration data was collected while the wind turbine was in operation. Tower vibration signals were analyzed using Empirical Mode Decomposition and the outcomes were correlated with the vibration signals acquired directly from the generator bearings. It is shown that the generator bearing fault signatures were present in the vibrations from the tower. The results suggest that useful condition monitoring of nacelle components can be done even when there is no condition monitoring system installed on the generator bearings, as is often the case for older wind turbines. In the second part of the paper, acoustic measurements from a healthy and a faulty wind turbine are shown. The preliminary analysis suggests that the generator bearing fault increases the overall sound pressure level at the bottom of the tower, and is not buried in the background noise.

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EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

Cited by 161 publications

References 88 publications

Radiation Noise Separation of Internal Combustion Engine Based on Gammatone-RobustICA Method

Radiation Noise Separation of Internal Combustion Engine Based on Gammatone-RobustICA Method

Bearing incipient fault diagnosis based upon maximal spectral kurtosis TQWT and group sparsity total variation denoising approach

Indicative Fault Diagnosis of Wind Turbine Generator Bearings Using Tower Sound and Vibration

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