Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

Chen, Binqiang; Lan, Qixin; Li, Yang; Zhuang, Shiqiang; Xiao, Cao

doi:10.3390/en12183536

Cited by 7 publications

(6 citation statements)

References 32 publications

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“…The above Equation is also named as the inverse fast Fourier transform (IFFT). For a sinusoidal wave whose sampling parameters satisfy the full period sampling condition, it is mapped to a single spectral line (11). Otherwise the phenomenon of energy leakage occurs and causes dense representation of the sinusoidal wave in the frequency domain.…”

Section: Notch Filters Based On Fourier Transform Theorymentioning

confidence: 99%

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Adaptive Sparse Detector for Suppressing Powerline Component in EEG Measurements

Chen

Zheng

Wang

et al. 2021

Front. Public Health

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Powerline interference (PLI) is a major source of interference in the acquisition of electroencephalogram (EEG) signal. Digital notch filters (DNFs) have been widely used to remove the PLI such that actual features, which are weak in energy and strongly connected to brain states, can be extracted explicitly. However, DNFs are mathematically implemented via discrete Fourier analysis, the problem of overlapping between spectral counterparts of PLI and those of EEG features is inevitable. In spite of their effectiveness, DNFs usually cause distortions on the extracted EEG features, which may lead to incorrect diagnostic results. To address this problem, we investigate an adaptive sparse detector for reducing PLI. This novel approach is proposed based on sparse representation inspired by self-adaptive machine learning. In the coding phase, an overcomplete dictionary, which consists of redundant harmonic waves with equally spaced frequencies, is employed to represent the corrupted EEG signal. A strategy based on the split augmented Lagrangian shrinkage algorithm is employed to optimize the associated representation coefficients. It is verified that spectral components related to PLI are compressed into a narrow area in the frequency domain, thus reducing overlapping with features of interest. In the decoding phase, eliminating of coefficients within the narrow band area can remove the PLI from the reconstructed signal. The sparsity of the signal in the dictionary domain is determined by the redundancy factor. A selection criteria of the redundancy factor is suggested via numerical simulations. Experiments have shown the proposed approach can ensure less distortions on actual EEG features.

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Section: Notch Filters Based On Fourier Transform Theorymentioning

confidence: 99%

“…Secondly, the sampling parameters also affect the filtering results. A phenomenon of energy leakage is likely to be produced by sinusoidal waves whose sampling does not satisfy the full period sampling condition (11). Thirdly, the actual EEG features cover a comparatively broad range in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sparse Detector for Suppressing Powerline Component in EEG Measurements

Chen

Zheng

Wang

et al. 2021

Front. Public Health

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“…. , N − 1 }, the problem of energy leakage and PFE occur (Chen et al, 2019). In such circumstance, the FFT spectrum of {pla[n]} is dense in frequency domain (Figure 1B).…”

Section: Mechanism Of Signal Distortion In Electroencephalogram Pre-processingmentioning

confidence: 99%

Suppressing of Power Line Artifact From Electroencephalogram Measurements Using Sparsity in Frequency Domain

et al. 2021

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Electroencephalogram (EEG) plays an important role in brain disease diagnosis and research of brain-computer interface (BCI). However, the measurements of EEG are often exposed to strong interference of power line artifact (PLA). Digital notch filters (DNFs) can be applied to remove the PLA effectively, but it also results in severe signal distortions in the time domain. To address this problem, spectrum correction (SC) based methods can be utilized. These methods estimate harmonic parameters of the PLA such that compensation signals are produced to remove the noise. In order to ensure high accuracy during harmonic parameter estimations, a novel approach is proposed in this paper. This novel approach is based on the combination of sparse representation (SR) and SC. It can deeply mine the information of PLA in the frequency domain. Firstly, a ratio-based spectrum correction (RBSC) using rectangular window is employed to make rough estimation of the harmonic parameters of PLA. Secondly, the two spectral line closest to the estimated frequency are calculated. Thirdly, the two spectral lines with high amplitudes can be utilized as input of RBSC to make finer estimations of the harmonic parameters. Finally, a compensation signal, based on the extracted harmonic parameters, is generated to suppress PLA. Numerical simulations and actual EEG signals with PLA were used to evaluate the effectiveness of the improved approach. It is verified that this approach can effectively suppress the PLA without distorting the time-domain waveform of the EEG signal.

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“…Grądzki [9] proposed a method of blades condition evaluation based on differences in phase shifts of measurement signals. For transient and nonstationary signals of blade rubbing, time-frequency analysis methods are often used [10] such as wavelet analysis and its improving method [11][12],…”

Section: Introductionmentioning

confidence: 99%

Rub-impact fault identification of a bladed rotor based on chaotic features

Kou

Yue

Lee

et al. 2023

Preprint

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The bladed rotor is an important part in turbine machines. Timely detection of its blade rubbing fault may avoid serious accidents. This paper developed a rub-impact fault identification approach for the bladed rotor based on chaotic features such as average period, time delay, embedded dimension, and largest Lyapunov exponent. A dynamic analytical model of a rotating variable thickness blade with the rub-impact fault is established. It is verified that the blade rubbing model could obtain the relationship between the chaotic behaviors and the rub-impact fault for a rotating blade. Through the nonlinear characteristic analysis, we could establish that different blade rubbing states have a certain corresponding relationship with the chaotic characteristics, which provides the theoretical basis for the blade rubbing identification using the chaotic features. In particular, a rub-impact fault identification approach based on chaotic features is further studied. And the blade rubbing fault can be easily identified by combining four chaotic features, which is proven by experiments. Comparing with the time domain and frequency domain analysis methods, the proposed approach provides a new way to identify such blade rubbing fault.

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Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

Cited by 7 publications

References 32 publications

Adaptive Sparse Detector for Suppressing Powerline Component in EEG Measurements

Adaptive Sparse Detector for Suppressing Powerline Component in EEG Measurements

Suppressing of Power Line Artifact From Electroencephalogram Measurements Using Sparsity in Frequency Domain

Rub-impact fault identification of a bladed rotor based on chaotic features

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