Rolling Bearing Fault Diagnosis Based on Optimal Notch Filter and Enhanced Singular Value Decomposition

Pang, Bin; He, Yu‐Ling; Tang, Guiji; Zhou, Chong; Tian, Tian

doi:10.3390/e20070482

Cited by 15 publications

(14 citation statements)

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“…And the optimal parameter a o = 115.7 Hz can be automatically confirmed in terms of MFWEE minimum value. Then the filter length parameter is further optimized on the basis of a = a o , and the search scope is set as [20,200] with search step size of 1. The FWEE indicators of deconvolution signals, which are obtained by CYCBD with different filter lengths and optimal cyclic frequency a o = 115.7, are shown in 9c.…”

Section: Experimental Signal Analysis and Results Comparsionmentioning

confidence: 99%

“…Figure 17b shows the calculated MFWEE values corresponding to different cyclic frequencies, and the optimal cyclic frequency a o = 79.4 Hz is selected based on the MFWEE minimum value. Within the scope of [20,200], the optimal filter length parameter is further searched with step size of 1. Figure 17c display s the calculated FWEE indicators corresponding to different filter lengths, and we can determinate the optimal filter length L o = 157 according to the FWEE minimum value.…”

Section: Engineering Signal Analysis and Results Comparsionmentioning

confidence: 99%

“…Within the scope of [20,200], the optimal filter length parameter is further searched with step size of 1. Figure 17c displays the calculated FWEE indicators corresponding to different filter lengths, and we can determinate the optimal filter length o L = 157 according to the FWEE minimum value.…”

Section: Engineering Signal Analysis and Results Comparsionmentioning

confidence: 99%

“…For L = L I + g (g = 0,1,2,· · · , (L E − L I ) is the number of step size movement), the FWEE indicator corresponding to each filter length (L I + g) is in turn calculated based on the similar process. And all FWEE indicators corresponding to the whole search scope [20,200] can be obtained. Then confirm the optimal filter length parameter L o when FWEE indicator is the smallest.…”

Section: Optimal Parameter Selection Stragegy Guided By Fwee Indicatormentioning

confidence: 99%

“…And the signal denoising operation can be implemented by simply reconstructing the matrix with a number of larger singular values. This admirable trait of SVD is a benefit to enhancing fault feature during bearing injury judgment [20]. Inspired by the respective advantages of the frequency weighted energy operator and…”

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Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

Wang

Yan

2019

Applied Sciences

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The fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward to extract and enhance the fault feature effectively. In this diagnosis method, the fast spectral coherence is fused with the equal step size search strategy for the cyclic frequency parameter and the filter length parameter optimization, and a new frequency weighted energy entropy (FWEE) indicator which combining the advantages of the frequency weighted energy operator (FWEO) and the Shannon entropy, is developed for deconvolution signal evaluation during parameter optimization process. In addition, a novel singular value order determination approach based on fitting error minimum principle is utilized by SVDD to enhance the fault feature. During the process of defect identification, OCYCBD with the optimal parameters is firstly used to recover the informative source from the collected vibration signal. FWEO is further utilized to highlight the potential impulsive characteristics, and the instantaneous energy signal of deconvolution result can be acquired. The whole interferences contained in the instantaneous energy signal can’t be removed due to the weak fault signature and the severe background noise. Then, SVDD is applied to purify the instantaneous energy signal of deconvolution signal, by which the residual interference component is eliminated and the fault feature is strengthened immensely. Finally, frequency domain analysis is performed on the denoised instantaneous energy signal, and the defect location identification of wind turbine bearing can be achieved through analyzing the obvious spectral lines in the obtained enhanced energy spectrum. The collected signals from the experimental platform and the engineering field are both utilized to verify the feasibility of proposed method, and its superiority is further demonstrated through comparing with several well known diagnosis methods. The results indicate this novel method has distinct advantage on bearing weak feature extraction and enhancement.

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Section: Experimental Signal Analysis and Results Comparsionmentioning

confidence: 99%

Section: Engineering Signal Analysis and Results Comparsionmentioning

confidence: 99%

Section: Engineering Signal Analysis and Results Comparsionmentioning

confidence: 99%

Section: Optimal Parameter Selection Stragegy Guided By Fwee Indicatormentioning

confidence: 99%

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confidence: 99%

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