Blind deconvolution based on cyclostationarity maximization and its application to fault identification

Buzzoni, Marco; Antoni, Jérôme; D’Elia, Gianluca

doi:10.1016/j.jsv.2018.06.055

Cited by 203 publications

(118 citation statements)

References 23 publications

(68 reference statements)

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“…Considering the cyclic stationary trait of fault signal of rotating machinery, a novel blind deconvolution method called CYCBD based on second-order cyclostationarity indicator is investigated by Buzzoni et al, and its specific principles can be found in Reference [18]. A larger indicator value means the periodic impact behavior of deconvolution signal is more prominent.…”

Section: Complexitymentioning

confidence: 99%

“…In addition, the resample operation needs to be carried out if the deconvolution period is not an integer, which is easy to cause misdiagnosis [17]. Recently, a novel cyclostationary blind deconvolution (CYCBD) approach is investigated by defining the higher-order cyclostationarity maximization indicator [18]. Nevertheless, its performance depends mostly on the cyclic frequency and the filter length parameters, which need to be artificially set in advance through prior knowledge and the operator's experiences, and the processing result can be entirely different due to the imprecise selection of these two parameters.…”

Section: Introductionmentioning

confidence: 99%

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Application of RSSD-OCYCBD Strategy in Enhanced Fault Detection of Rolling Bearing

Wang

Tang

2020

Complexity

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The defect characteristics of rolling bearing are difficult to excavate at the incipient injury phase; in order to effectively solve this issue, an original strategy fusing recursive singular spectrum decomposition (RSSD) with optimized cyclostationary blind deconvolution (OCYCBD) is put forward to achieve fault characteristic enhanced detection. In this diagnosis strategy, the data-driven RSSD method without predetermined component number is proposed. In addition, a new morphological difference operation entropy (MDOE) indicator, which takes advantage of morphological transformation and Shannon entropy, is developed for confirming the influencing parameters of cyclostationary blind deconvolution (CYCBD). During the process of fault detection, RSSD is firstly adopted to preprocess the original signal, and the most sensitive singular spectrum component (SSC) is selected by the envelope spectrum peak (ESP) indicator. Then, the grid search algorithm is adopted to precisely confirm the optimal parameters and OCYCBD is further performed as a postprocessing technology on the most sensitive component to suppress the residual interferences and amplify the fault signatures. Finally, the enhanced fault detection of rolling bearing is able to achieve by analyzing the envelope spectrum of deconvolution signal. The feasibility of the proposed strategy is verified by the simulated and the measured signals, respectively, and its superiority is also demonstrated through several comparison methods. The results manifest this novel strategy has praisable advantages on weak characteristic extraction and intensification.

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Section: Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of RSSD-OCYCBD Strategy in Enhanced Fault Detection of Rolling Bearing

Wang

Tang

2020

Complexity

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“…where N and L are the length of s and h, respectively. A new deconvolution algorithm based on cyclostationarity maximization is proposed in reference [15]. In this algorithm, the cyclic frequency is defined as:…”

Section: Theoretical Background Of Cyclostationary Blind Deconvolutionmentioning

confidence: 99%

“…In additional, the resample operation needs to be carried out when the deconvolution period isn't integer, which is easy to cause misdiagnosis. Recently, a novel cyclostationary blind deconvolution (CYCBD) approach, which is able to eliminate the influence of vibration transmission path and separate the fault source effectively, is investigated by defining a simple and effective indicator of higher-order cyclostationarity maximization [15]. Nevertheless, the performance of CYCBD is mostly depended on the cyclic frequency and the filter length parameters, which also need to be artificially selected in advance.…”

mentioning

confidence: 99%

“…The search scope of cyclic frequency parameter is initialized as [a As for the filter length L, the deconvolution signal will be distorted if it is too large, while the treatment efficiency will be inconspicuous if this parameter is too small. Referring to literature[15], the search scope of filter length is set as [L I , L E ] =[20,200] with search step size of 1, where L E and L I denote the upper boundary and the lower boundary of search scope. Step 3 Determinate the optimal cyclic frequency parameter.…”

mentioning

confidence: 99%

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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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Identifiability Considerations for Rotating Machine Fault Diagnosis and Prognosis

Schmidt

Heyns

Wilke

2021

Applied Condition Monitoring

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Blind deconvolution based on cyclostationarity maximization and its application to fault identification

Cited by 203 publications

References 23 publications

Application of RSSD-OCYCBD Strategy in Enhanced Fault Detection of Rolling Bearing

Application of RSSD-OCYCBD Strategy in Enhanced Fault Detection of Rolling Bearing

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

Identifiability Considerations for Rotating Machine Fault Diagnosis and Prognosis

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