Print Email Facebook Twitter Removing speckle noise from the signals of a laser Doppler vibrometer on moving platforms (LDVom) by ensemble empirical mode decomposition Title Removing speckle noise from the signals of a laser Doppler vibrometer on moving platforms (LDVom) by ensemble empirical mode decomposition Author Jin, J. (TU Delft Railway Engineering) Dollevoet, R.P.B.J. (TU Delft Railway Engineering) Li, Z. (TU Delft Railway Engineering) Date 2022 Abstract With increasing requirements for structural stability and durability, effective monitoring strategies for existing and potential damage are necessary. A laser Doppler vibrometer on moving platforms (LDVom) can remotely capture large-scale structural vibrations, but speckle noise, a significant signal issue mainly when one-way continuously scanning from moving platforms, restricts its applications. A novel approach based on ensemble empirical mode decomposition (EEMD) is proposed to eliminate speckle noise. Moving root-mean-square thresholds are used to cut off signal drop-outs. With both numerically simulated and experimentally acquired signals, the proposed EEMD-based approach reveals the true vibrations despite the low initial signal-to-noise ratio. Other methods fail to eliminate the speckle noise. In physical experiments, the despeckled signal energy is concentrated at defect locations in the Hilbert-Huang spectrum. The identified damage locations agree well with the actual damage locations. Therefore, the developed approach demonstrates advantages and robustness of eliminating speckle noise in LDVom signals for damage inspection. Subject ensemble empirical mode decompositionlaser Doppler vibrometernoise removalspeckle noise To reference this document use: http://resolver.tudelft.nl/uuid:58759bbb-a2f2-4c7a-bec3-aaccb94eca19 DOI https://doi.org/10.1088/1361-6501/ac8daf ISSN 0957-0233 Source Measurement Science and Technology, 33 (12) Part of collection Institutional Repository Document type journal article Rights © 2022 J. Jin, R.P.B.J. Dollevoet, Z. Li Files PDF Jin_2022_Meas._Sci._Techn ... 125205.pdf 5.55 MB Close viewer /islandora/object/uuid:58759bbb-a2f2-4c7a-bec3-aaccb94eca19/datastream/OBJ/view