Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion

Yang, Dong; Li, Hui; Hu, Yang; Zhao, Jie; Xiao, Hong‐Wei; Lan, Yongsen

doi:10.1016/j.renene.2016.01.099

Cited by 55 publications

(22 citation statements)

References 11 publications

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“…The fitness of each particle is determined by the objective function. The performance of stochastic resonance system is mainly reflected in the signal to noise ratio (SNR) of the output signal [37]. Hence, the fitness function of QPSO algorithm is shown as follows: …”

Section: Fault Diagnosis Processmentioning

confidence: 99%

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

Chen

2017

International Journal of Rotating Machinery

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Aiming at the difficulty of early fault vibration signal extraction of rolling bearing, a method of fault weak signal extraction based on variational mode decomposition (VMD) and quantum particle swarm optimization adaptive stochastic resonance (QPSO-SR) for denoising is proposed. Firstly, stochastic resonance parameters are optimized adaptively by using quantum particle swarm optimization algorithm according to the characteristics of the original fault vibration signal. The best stochastic resonance system parameters are output when the signal to noise ratio reaches the maximum value. Secondly, the original signal is processed by optimal stochastic resonance system for denoising. The influence of the noise interference and the impact component on the results is weakened. The amplitude of the fault signal is enhanced. Then the VMD method is used to decompose the denoised signal to realize the extraction of fault weak signals. The proposed method was applied in simulated fault signals and actual fault signals. The results show that the proposed method can reduce the effect of noise and improve the computational accuracy of VMD in noise background. It makes VMD more effective in the field of fault diagnosis. The proposed method is helpful to realize the accurate diagnosis of rolling bearing early fault.

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Section: Fault Diagnosis Processmentioning

confidence: 99%

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

Chen

2017

International Journal of Rotating Machinery

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“…Sifting procedure is repeated k times for each of the resulting difference h k (t), often referred to prototype mode or shortly: proto-IMF [9,10].…”

Section: Emd Algorithmmentioning

confidence: 99%

Identification of the operating parameters for the mechanical systeIdentification system using EMD algorithm

et al. 2016

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Abstract. The subject of paper was a research on mechanical system with pneumatic coupling, consisting of a compressor turbine (T I ) and of so-called free driving turbine (T II ).The purpose of the tests is to determine the applicability of Empirical Mode Decomposition (EMD) algorithm to identify operating parameters of the mechanical system, which is necessary to build a system for structure condition monitoring with use of mechanical system, which is necessary to build a system for structure condition monitoring with use of vibroacoustic method. There has been assumed possibility of easy separation of working frequencies of these components on order to estimate amplitudes generated by them.

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“…Alternatively, the S-transform is superior in the time-frequency analyses of non-stationary signals, which eliminates the limitations of STFT and WT [14]. Moreover, the empirical mode decomposition (EMD) method shows excellent performance in non-stationary signal processing due to its local adaptive feature [15,16]. Nevertheless, the EMD also has some limits in application due to its recursive calculation and mode-mixing problems [17,18].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

Zhu

et al. 2019

Energies

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Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.

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Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion

Cited by 55 publications

References 11 publications

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

Identification of the operating parameters for the mechanical systeIdentification system using EMD algorithm

Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

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