Fast computation of the spectral correlation

Antoni, Jérôme; Xin, Ge; Hamzaoui, Nacer

doi:10.1016/j.ymssp.2017.01.011

Cited by 293 publications

(214 citation statements)

References 31 publications

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“…Spectral coherence provides a new interpretation of periodic flows of energy across the analysis frequency band and the cyclic frequency band [27]. With the help of its excellent ability in revealing the presence of modulation and describing the cyclostationarity, the fast spectral coherence is applied to accurately estimate the search center, and the relevance theory of fast spectral coherence can refer to literature [28]. The cyclic frequency location corresponding to the maximum energy distribution in the fast spectral coherence is regarded as the search center a M .…”

Section: Optimal Parameter Selection Stragegy Guided By Fwee Indicatormentioning

confidence: 99%

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: Optimal Parameter Selection Stragegy Guided By Fwee Indicatormentioning

confidence: 99%

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

Wang

Yan

2019

Applied Sciences

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“…Let x[n], n=0, 1,…,N be the data vector and P is the number of neighboring samples. The 1D LBP of x[n] is formulated as: (5) An example of this concept is presented in Fig. 3 for 1D LBP calculated over a time series for P=8.…”

Section: B 1d Local Binary Patternmentioning

confidence: 99%

“…The data in this work was obtained from sites that contain operating rotating machines. It was reported in [5] that mechanical or electrical faults in rotating machines generate a cyclic signature that could be present in the captured data signals. This signature determines periodic statistical characteristics within the cyclostationary signal.…”

Section: Introductionmentioning

confidence: 99%

Classification of Multiple Electromagnetic Interference Events in High-Voltage Power Plant

Mitiche

Morison

Nesbitt

et al. 2018

2018 53rd International Universities Power Engineering Conference (UPEC)

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This paper addresses condition assessment of electrical assets contained in High-Voltage (HV) electrical power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the expert's knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events.

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“…However, traditional SC techniques have low computational efficiency. Accordingly, Antoni proposed the fast spectral correlation (Fast-SC) method [28], which is a novel spectral correlation estimation method. The Fast-SC method not only has the advantages of spectral correlation, but also overcomes the shortcomings of high computational cost.…”

Section: Introductionmentioning

confidence: 99%

“…In order to reduce the computational cost and improve the efficiency of spectral correlation, a fast spectral correlation method based on short time Fourier transform was proposed in Reference [28].…”

Section: Brief Introduction Of Fast Spectral Correlationmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest

et al. 2018

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Fault diagnosis of rolling bearings is important for ensuring the safe operation of industrial machinery. How to effectively extract the fault features and select a classifier with high precision is the key to realizing the fault recognition of bearings. Accordingly, a new fault diagnosis method of rolling bearings based on improved fast spectral correlation and optimized random forest (i.e., particle swarm optimization-random forest (PSO-RF)) is proposed in this paper. The main contributions of this study are made from two aspects. One is that an improved fast spectral correlation approach was developed to extract the fault features of bearings and form the feature vector more effectively. The other is that an optimized random forest classifier was developed to achieve highly accurate identification by exploiting particle swarm optimization to select the best parameters of random forest (RF). In the presented method, improved fast spectral correlation was first utilized to analyze the raw vibration signal caused by a faulty bearing to obtain the enhanced envelope spectrum. Then, the amplitudes of the four characteristic cyclic frequencies (i.e., the rotating frequency, the characteristic frequency of outer-race fault, the characteristic frequency of inner-race fault, and the characteristic frequency of rolling element fault) exhibited in the enhanced envelope spectrum were selected to form the feature vector. Finally, the PSO-RF method was introduced for identifying and classifying bearing faults. The experimental investigations demonstrate the proposed method can accurately identify bearing faults and outperform other state-of-art techniques considered.

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Fast computation of the spectral correlation

Cited by 293 publications

References 31 publications

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

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

Classification of Multiple Electromagnetic Interference Events in High-Voltage Power Plant

Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest

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