• Title/Summary/Keyword: Multi-component signals

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Prediction of Cutting Force Using Independent Component Analysis (독립성분 해석을 이용한 절삭력 예측)

  • Lee, Young-Moon;Jang, Sung-Il;Lee, Dong-Sik;Jun, Jung-Woon
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.2 no.2
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    • pp.22-30
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    • 2003
  • Cutting force signals are very useful to evaluate the cutting state, but many disturbing factors are occurring during cutting. For the reliability of the analysis, selecting pure cutting force signals from the original ones is needed. In the current study, using the ICA(Independent Component Analysis) effective cutting force components are seperated from the original signals. And using this, as input data of MLP(Multi-Layer Perception) cutting forces are predicted Experimental results are then compared with the predicted ones to verify the validation of the proposed model.

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Sensing method of multi-component forces and moments using a column structure (기둥을 이용한 다축 힘/모멘트 감지 방법에 관한 연구)

  • Shin, H.H.;Kang, D.I.;Park, Y.K.;Kim, J.H.;Joo, J.W.;Kim, O.H.
    • Proceedings of the KSME Conference
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    • 2001.06a
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    • pp.837-841
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    • 2001
  • The column-type sensing element in building and mechanical construction parts was designed as three forces and three moments sensor by attaching strain gages approximately. Compared to conventional multi-component sensor, the designed sensor can solve the problem about low stiffness and high cost. The radius of the column was designed analytically and compared with finite element analysis. The coupling errors between components were minimized by using addition and subtraction procedure of signals. The fabricated sensor was tested by using a deadweight force standard machine and a six-component force calibration machine in Korea Research Institute of Standards and Science(KRISS). The calibration showed that the multi-component force/moment sensor had coupling error less than 19.8 % between $F_x$ and $M_y$ components, and 9.0 % in case of other components.

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Frequency Bin Alignment Using Covariance of Power Ratio of Separated Signals in Multi-channel FD-ICA (다채널 주파수영역 독립성분분석에서 분리된 신호 전력비의 공분산을 이용한 주파수 빈 정렬)

  • Quan, Xingri;Bae, Keunsung
    • Phonetics and Speech Sciences
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    • v.6 no.3
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    • pp.149-153
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    • 2014
  • In frequency domain ICA, the frequency bin permutation problem falls off the quality of separated signals. In this paper, we propose a new algorithm to solve the frequency bin permutation problem using the covariance of power ratio of separated signals in multi-channel FD-ICA. It makes use of the continuity of the spectrum of speech signals to check if frequency bin permutation occurs in the separated signal using the power ratio of adjacent frequency bins. Experimental results have shown that the proposed method could fix the frequency bin permutation problem in the multi-channel FD-ICA.

Comparison of On-Line Diagnotic Methods on Multi-Channel Signals in Nuclear Plant (원자력발전소 다채널 신호의 온라인 진단방법 비교)

  • Lee, Kwang-Dae;Yang, Seung-Ok
    • Proceedings of the KIEE Conference
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    • 2003.11c
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    • pp.705-708
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    • 2003
  • In this paper, we have evaluated the methods to generate the reference signal for the diagnosis of multi-channel signals. The channel signal integrity can be known by the difference between the reference signal and each channel value. The generation method of reference signal is important in the diagnosis of multi-channel measurement system. The continuous weighting average method rejects the abnormal signal using weighting method and makes the reference signal using sumation of all channel values. This gives the simple and reasonable reference signal. The principle component analysis, one of the multivariate analysis methods, and the neural network method give the reliable reference signal by using signal models, and learning algorithm. Two methods can make the reliable reference if all signals are normal, but any signal having the drift have an effect on the reference.

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Reduced wavelet component energy-based approach for damage detection of jacket type offshore platform

  • Shahverdi, Sajad;Lotfollahi-Yaghin, Mohammad Ali;Asgarian, Behrouz
    • Smart Structures and Systems
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    • v.11 no.6
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    • pp.589-604
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    • 2013
  • Identification of damage has become an evolving area of research over the last few decades with increasing the need of online health monitoring of the large structures. The visual damage detection can be impractical, expensive and ineffective in case of large structures, e.g., offshore platforms, offshore pipelines, multi-storied buildings and bridges. Damage in a system causes a change in the dynamic properties of the system. The structural damage is typically a local phenomenon, which tends to be captured by higher frequency signals. Most of vibration-based damage detection methods require modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such good sensitive indication of structural damage. Identification of damaged jacket type offshore platform members, based on wavelet packet transform is presented in this paper. The jacket platform is excited by simple wave load. Response of actual jacket needs to be measured. Dynamic signals are measured by finite element analysis result. It is assumed that this is actual response of the platform measured in the field. The dynamic signals first decomposed into wavelet packet components. Then eliminating some of the component signals (eliminate approximation component of wavelet packet decomposition), component energies of remained signal (detail components) are calculated and used for damage assessment. This method is called Detail Signal Energy Rate Index (DSERI). The results show that reduced wavelet packet component energies are good candidate indices which are sensitive to structural damage. These component energies can be used for damage assessment including identifying damage occurrence and are applicable for finding damages' location.

A generalized adaptive variational mode decomposition method for nonstationary signals with mode overlapped components

  • Liu, Jing-Liang;Qiu, Fu-Lian;Lin, Zhi-Ping;Li, Yu-Zu;Liao, Fei-Yu
    • Smart Structures and Systems
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    • v.30 no.1
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    • pp.75-88
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    • 2022
  • Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

Application of Technique Discrete Wavelet Transform for Acoustic Emission Signals (음향방출신호에 대한 이산웨이블릿 변환기법의 적용)

  • 박재준;김면수;김민수;김진승;백관현;송영철;김성홍;권동진
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2000.07a
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    • pp.585-591
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    • 2000
  • The wavelet transform is the most recent technique for processing signals with time-varying spectra. In this paper, the wavelet transform is utilized to improved the assessment and multi-resolution analysis of acoustic emission signals generating in partial discharge. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals in case of applied voltage 20[kv]. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We applied FIR(Finite Impulse Response)digital filter algorithm in discrete to suppression for random noise. The white noise be included high frequency component denoised as decomposition of discrete wavelet transform level-3. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of acting(the early period, the last period) .

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Magnetocardiogram Topography with Automatic Artifact Correction using Principal Component Analysis and Artificial Neural Network

  • Ahn C.B.;Kim T.H.;Park H.C.;Oh S.J.
    • Journal of Biomedical Engineering Research
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    • v.27 no.2
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    • pp.59-63
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    • 2006
  • Magnetocardiogram (MCG) topography is a useful diagnostic technique that employs multi-channel magnetocardiograms. Measurement of artifact-free MCG signals is essenctial to obtain MCG topography or map for a diagnosis of human heart. Principal component analysis (PCA) combined with an artificial neural network (ANN) is proposed to remove a pulse-type artifact in the MCG signals. The algorithm is composed of a PCA module which decomposes the obtained signal into its principal components, followed by an ANN module for the classification of the components automatically. In the experiments with volunteer subjects, 97% of the decisions that were made by the ANN were identical to those by the human experts. Using the proposed technique, the MCG topography was successfully obtained without the artifact.

Multi-axial Vibration Test on MAST System with Field Data (국내도로 주행 시험을 통한 6축 진동시험 방법에 관한 연구)

  • Kim, Chan-Jung;Bae, Chul-Yong;Lee, Bang-Hyun;Kwon, Seong-Jin;Na, Byung-Chul
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.16 no.7 s.112
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    • pp.704-711
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    • 2006
  • Vibration test on MAST(multi axial simulation table) system has several advantage over one-axial vibration test that could simulate 6-DOF, 3-axial translation and 3-axial moment, at the same time. Since field vibration motion can be fully represented with 6-DOF, multi-axial vibration test on vehicle component is widely conducted in technical leading companies to make sure its fatigue performance in vibration environment. On the way to fulfill the process, editing technique of obtained field data is key issue to success a reliable vibration testing with MAST system. Since the original signals are not only too large to fulfill it directly, but all of the measured data is not guarantee its convergency on generating its driving files, editing technique of the original signals are highly required to make some events that should meet the equal fatigue damage on the target component In this paper, key technique on editing a field data feasible for MAST system is described based on energy method in vibration fatigue. To explain its technique explicitly, author first introduced a process on field data acquisition of two vehicle component and then, representing events are produced to keep up with the editing strategy about a energy method. In the final chapter, a time information regarding a vibration test on MAST system is derived from the energy data which is critical information to perform a vibration test.

MAST Vibration on MAST System with Field Data (국내도로 주행 시험을 통한 6축 진동시험 방법에 관한 연구)

  • Kim, Chan-Jung;Bae, Chul-Yong;Lee, Bong-Hyun;Kwon, Seong-Jin;Na, Byung-Chul
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2006.05a
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    • pp.764-769
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    • 2006
  • Vibration test on MAST(multi axial simulation table) system has several advantage over one-axial vibration test that could simulate 6-DOF, 3-axial translation and 3-axial moment, at the same time. Since field vibration motion can be fully represented with 6-DOF, multi-axial vibration test on vehicle component is widely conducted in technical leading companies to make sure its fatigue performance in vibration environment. On the way to fulfill the process, editing technique of obtained field data is key issue to success a reliable vibration testing with MAST system. Since the original signals are not only too large to fulfill it directly, but all of the measured data is not guarantee its convergency on generating its driving files, editing technique of the original signals are highly required to make some events that should meet the equal fatigue damage on the target component In this paper, key technique on editing a field data feasible for MAST system is described based on energy method in vibration fatigue. To explain its technique explicitly, author first introduced a process on field data acquisition of two vehicle component and then, representing events are produced to keep up with the editing strategy about a energy method. In the final chapter, a time information regarding a vibration test on MAST system is derived from the energy data which is critical information to perform a vibration test.

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