Surface characteristic profile extraction based on Hilbert–Huang transform

Xia, Chunlin; Wu, Yicheng; Lu, Qianqian; Ju, Bing‐Feng

doi:10.1016/j.measurement.2013.08.066

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…After the vibration signals were processed with EMD method, the IMF components ranked from high to low frequency. In particular, the specific physical meanings of each IMF component are as follows: IMF5 (frequency: 0.05859 HZ) is harmonic component caused by the kiln crank; IMF4 (frequency: 0.1758 HZ) is the vibration component caused by the supporting roller shaft deflection; according to [17,18], IMF3 is the ripple deformation component of the supporting roller's surface; IMF2 is the harmonic component of the supporting roller's surface roughness; IMF1 is the harmonic component of microscopic deformation. ;;…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…According to the numerical analysis for the supporting rollers, it can be found that the vibration signal mainly includes the harmonic component caused by the kiln crank and the harmonic component caused by the deflection of the roller shaft. In [17], authors pointed out that it also contains the harmonic component of the surface characteristic, such as the harmonic component of waviness, the roughness and micro-irregularities. In fact, the vibration signal are nonlinear, non-stationary signal which are not suitable processed by the traditional signal processing method such as fast Fourier transform (FFT) or Wavelet transform (WT) [12][13][14].…”

Section: Feature Extraction Using Empirical Mode Decomposition Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Fault Diagnosis for Supporting Rollers of the Rotary Kiln Using the Dynamic Model and Empirical Mode Decomposition

Zheng¹,

Zhang²,

Zhao³

et al. 2016

mech

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Feature Extraction Using Empirical Mode Decomposition Methodsmentioning

confidence: 99%

Fault Diagnosis for Supporting Rollers of the Rotary Kiln Using the Dynamic Model and Empirical Mode Decomposition

Zheng¹,

Zhang²,

Zhao³

et al. 2016

mech

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“…The main features of the HHT are its adaptive signal decomposition and filtering in the temporal /spatial domains [16]. Xia et al [17] used the HHT to decompose and filter these surface measurement data before extracting engineering surface features. Guo et al [18] applied the time-frequency energy matrix of the distribution network fault signal to the sampled fault signal through the HHT method.…”

Section: Literature Reviewsmentioning

confidence: 99%

Incorporating Visual Defect Identification and Determination of Occurrence Side in Touch Panel Quality Inspection

2022

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This study explores a visual inspection system incorporating defect detection and the judgment of the defect occurrence side in touch panel fabrication. The surface of a touch panel is transparent glass with conductive lines inside to achieve the purpose of touch control. When touch panels are manufactured, it is the front sides of the products that are primarily processed and are treated with stricter standards. Therefore, it is important to correctly distinguish the defect locations occurring on the front or back side to ensure the quality of the touch panels. We first apply the Hilbert-Huang transform to enhance the contrast between the defects and the background. Second, statistical interval estimation is used to segment background and defects to achieve defect detection. Third, the detected defects on both sides of the substrates are combined and numbered for feature extraction. Fourth, the random forest model is applied to judge where the defects occur on the front or back of the transparent substrates. Experimental results show that the defect detection rate achieves 85.75%, and the false alarm rate is lower to 0.33%. The correct location judgment rate of the defect occurrence is 98.62% on the front and back sides of the touch panels.INDEX TERMS Visual inspection, defect identification, occurrence side judgment, touch panels, Hilbert-Huang transform, random forest model.

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“…Considering the convenience of reading, this subsection provides a brief review of the EMD, EEMD used in this study; further details can be found in [12][13][14]. For a given time series, through a shifting process, it can be written as…”

Section: Hht and Complementary Thickness Measurement Principlementioning

confidence: 99%

Application of the HHT Method to the Non-contact Thickness Measurement of an Axially Moving Thin Plate

Xia

et al. 2016

J. Inst. Eng. India Ser. C

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Non-contact thickness measuring systems can be found in a wide spectrum of technologies. In this paper, Hilbert-Huang transform method is used to analyze the real time signals of a measuring system which includes two round conveyor strings carrying a thin plate, a solar wafer as a sample under test. The vibrations of moving strings and the plate, which are sensitive to moving speed and initial tension in the string, are introduced briefly; the relevant analyses should be helpful for the system design. Using EMD-based time-domain filtering and complementary method, thickness variations and error bands are estimated for different cases. The results show that HHT method as an adaptive time-frequency method, should be potential in measurement engineering applications.

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Surface characteristic profile extraction based on Hilbert–Huang transform

Cited by 7 publications

References 16 publications

Fault Diagnosis for Supporting Rollers of the Rotary Kiln Using the Dynamic Model and Empirical Mode Decomposition

Fault Diagnosis for Supporting Rollers of the Rotary Kiln Using the Dynamic Model and Empirical Mode Decomposition

Incorporating Visual Defect Identification and Determination of Occurrence Side in Touch Panel Quality Inspection

Application of the HHT Method to the Non-contact Thickness Measurement of an Axially Moving Thin Plate

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