A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation.

Jiantao Lu, Wei Cheng, Yanyang Zi
Author Information
  1. Jiantao Lu: State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China. lujiantao1990@stu.xjtu.edu.cn. ORCID
  2. Wei Cheng: State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China. chengw@xjtu.edu.cn.
  3. Yanyang Zi: State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China. ziyy@xjtu.edu.cn.

Abstract

To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method.

Keywords

References

  1. IEEE Trans Neural Netw. 2005 May;16(3):645-78 [PMID: 15940994]
  2. IEEE Trans Neural Netw Learn Syst. 2012 Feb;23(2):306-16 [PMID: 24808509]
  3. IEEE J Biomed Health Inform. 2017 Jan;21(1):94-104 [PMID: 26625438]
  4. IEEE Trans Neural Netw Learn Syst. 2017 Dec;28(12):3102-3108 [PMID: 28113526]

Grants

  1. 51775407/National Natural Science Foundation of China
  2. 61633001/Key Project supported by National Natural Science Foundation of China
  3. 2017YFC0805701/The National Key Research and Development Program of China
  4. 2014T70911/The China Postdoctoral Science Foundation

Word Cloud

Created with Highcharts 10.0.0sourcesignalscanmethodestimationproposedmixedcontributionbasedunderdeterminedblindseparationsingleSSPsmixingmatrixrecoveredcoefficientsenergyviaquantitativelycontributionsSourceidentifymajorvibrationradiationnoisequantitativeFirstpointsidentifieddirectlysearchingidenticalnormalizedtime-frequencyvectorsimproveefficiencyaccuracyidentifyingobtainedhierarchicalclusteringalsoleastsquareSecondoptimalcombinationcalculatedminimumredundanterrorThereforeoptimallylinearlycombinedThirdeliminationusedestimateFinallyeffectivenessverifiednumericalcasestudiesexperimentscylindricalstructureresultsshoweffectivelyestimatedNovelUnderdeterminedBlindSeparationMethodApplicationContributionQuantitativeEstimationpoint

Similar Articles

Cited By