Separation of Single Frequency Component Using Singular Value Decomposition

Zhao, Xuezhi; Bang-yan, YE

doi:10.1007/s00034-018-0852-2

Cited by 10 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Signals. Based on the Hankel-SVD technique, the frequencies and the amplitudes at modal peaks of the FRF signal can be decomposed to the component signals [28]. For the experimental FRF signal H(ω), the concerned number of modal peaks is set as k.…”

Section: Selection Of the Number Of The Accumulated Componentmentioning

confidence: 99%

“…e result shows the method well enhances the impulse feature. Recently, Zhao and Ye [28] proved that the SVD has a feature to recover certain frequency components, and Cheng et al [29] proposed a resonance enhancement SVD for feature extraction by adding the excitation sinusoidal signals with concerned frequencies to the original signal to form the enhanced signal. en, a rectangle Hankel matrix with a certain dimension is constructed from the enhanced signal.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method

Zhu

Zang

Zhang

2020

Shock and Vibration

View full text Add to dashboard Cite

The measured frequency response functions (FRFs) in the modal test are usually contaminated with noise that significantly affects the modal parameter identification. In this paper, a modal peak-based Hankel-SVD (MPHSVD) method is proposed to eliminate the noise contaminated in the measured FRFs in order to improve the accuracy of the identification of modal parameters. This method is divided into four steps. Firstly, the measured FRF signal is transferred to the impulse response function (IRF), and the Hankel-SVD method that works better in the time domain rather than in the frequency domain is further applied for the decomposition of component signals. Secondly, the iteration of the component signal accumulation is conducted to select the component signals that cover the concerned modal features, but some component signals of the residue noise may also be selected. Thirdly, another iteration considering the narrow frequency bands near the modal peak frequencies is conducted to further eliminate the residue noise and get the noise-reduced FRF signal. Finally, the modal identification method is conducted on the noise-reduced FRF to extract the modal parameters. A simulation of the FRF of a flat plate artificially contaminated with the random Gaussian noise and the random harmonic noise is implemented to verify the proposed method. Afterwards, a modal test of a flat plate under the high-temperature condition was undertaken using scanning laser Doppler vibrometry (SLDV). The noise reduction and modal parameter identification were exploited to the measured FRFs. Results show that the reconstructed FRFs retained all of the modal features we concerned about after the noise elimination, and the modal parameters are precisely identified. It demonstrates the superiority and effectiveness of the approach.

show abstract

Section: Selection Of the Number Of The Accumulated Componentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method

Zhu

Zang

Zhang

2020

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Then, S is replaced with S ′ in eq , and a new H x ′ and denoised signal X ′ are obtained. Zhao proved that an arbitrary frequency component in signals generates two nonzero singular values . Therefore, in SVD, smaller singular values are generated by noise, whereas larger singular values can reflect the frequency characteristics of the real signal and can be extracted as features.…”

Section: Multiacoustic Sensor Systemmentioning

confidence: 99%

“…Zhao proved that an arbitrary frequency component in signals generates two nonzero singular values. 19 Therefore, in SVD, smaller singular values are generated by noise, whereas larger singular values can reflect the frequency characteristics of the real signal and can be extracted as features. Accordingly, feature extraction and noise elimination can be achieved simultaneously using SVD.…”

Section: Industrial and Engineering Chemistry Researchmentioning

confidence: 99%

Agglomeration-Monitoring Method for a Fluidized Bed with Multiacoustic Sensors

Chen

Jing

et al. 2019

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

The acoustic method is sensitive to agglomerations in fluidized bed reactors; however, a single acoustic sensor is not sufficient for industrial-scale reactors with regard to size, environmental noise, and signal pollution. Therefore, a multiacoustic sensor-based monitoring approach is investigated in this study. Different agglomeration warning models are established on the basis of each separate sensor, and then information-fusion technology is introduced. In the modeling process, an early warning method for industrial applications is designed. First, singular value decomposition is introduced for feature extraction to simplify the signal preprocessing and reduce the number of preset parameters. Thereafter, as agglomeration samples are lacking in industrial data, support vector data description (SVDD), an unsupervised learning method, is employed to establish the agglomeration warning model according to samples under normal conditions. As the Boolean outputs of SVDD are not suitable for information fusion, the output probability estimation method for SVDD is studied. In the estimation method, additional sigmoid functions are incorporated into the SVDD models, and the maximum likelihood algorithm is combined with the expectation−maximization algorithm to optimize the parameters of the sigmoid functions. Finally, the Dempster−Shafer evidence theory is applied for information fusion. Experiments conducted on a pilot plant prove that the proposed early agglomeration detection method is more stable than the S statistics of acoustic signals. The multisensor-based warning method can provide better detection of agglomeration than the temperature−pressure method, and for industrial applications, it provides more reliable detection results than single-sensor systems.

show abstract

“…Some scholars have done further research into improving the selection method of the embedding dimension. According to the study of harmonic signals, Zhao and Ye [14] and Guo et al [15] proposed a method to determine the embedding dimension by constructing an approximate square matrix, which has been successfully applied to the fault diagnosis of a rotor system. Zhou et al [16] optimized the embedding dimension by an autocorrelation function and Cao's algorithm, and achieved satisfactory results in detecting a gear fault.…”

Section: Introductionmentioning

confidence: 99%

Adaptive singular value decomposition for bearing fault diagnosis under strong noise interference

Cui

Liu

Zhao

2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

Singular value decomposition (SVD) is an effective tool, which is a non-parametric signal analysis method free from phase shift and waveform distortion, for analyzing signals of mechanical systems and fault diagnosis. In the SVD, the embedding dimension of the Hankel matrix is an important parameter and directly influences the effectiveness of the SVD. However, the embedding dimension is usually determined by the experience which is quite subjective and limits the applicability of the SVD. As such, a novel SVD method, named adaptive SVD (ASVD), is proposed in this paper. In the ASVD, the novel criterions are defined to obtain the specific embedding dimensions for difference mechanical signals using the means of numerical simulation. Namely, the novel phenomenon, which is that the singular value pairs (SVP) changes periodically with the step size of half-cycle sampling points, is found and it can be used to calculate specific embedding dimension instead of selecting it from a range using experience. Meanwhile, the envelope spectral amplitude ratio (ESAR) index is developed for addressing the issue of the excessive decomposition in classic SVD. Lastly, the ASVD-based bearing fault diagnosis method is proposed for adaptively select useful sub-signals and detect fault. Both simulated signal and experiment signals, which collected from different bearing test rigs are used to verify the effectiveness of the proposed method. The results show that the proposed method has satisfactory ability to eliminate interference noise and detect bearing fault.

show abstract

Separation of Single Frequency Component Using Singular Value Decomposition

Cited by 10 publications

References 27 publications

Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method

Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method

Agglomeration-Monitoring Method for a Fluidized Bed with Multiacoustic Sensors

Adaptive singular value decomposition for bearing fault diagnosis under strong noise interference

Contact Info

Product

Resources

About