Speckle noise reduction for structural vibration measurement with laser Doppler vibrometer on moving platform. (1st October 2022)

• Record Type: Journal Article
• Authors: Zeng, Yuanchen; Núñez, Alfredo; Li, Zili
• Is Part Of: Mechanical systems and signal processing. Volume 178(2022)
• Publication Date: 2022-10-01
• Subjects: Laser Doppler vibrometer -- Laser Doppler vibrometer on moving platform -- Speckle noise -- Spike detection -- Imputation -- Railway track; LDV laser Doppler vibrometer -- LDVom laser Doppler vibrometer on moving platform -- CWT continuous wavelet transform -- DWT discrete wavelet decomposition -- IDWT inverse discrete wavelet transform -- ARIMA autoregressive integrated moving average -- ACF autocorrelation function -- SVM support vector machine -- ANN artificial neural network; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109196
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Characteristics of speckle noise from laser Doppler vibrometer on moving platform. A despeckle framework with three steps: spike detection, imputation and smoothing. Validation based on track vibration measurements on V-Track test rig at 0.5∼20 km/h. Good agreement with trackside vibration measurements. Abstract: Speckle noise is a major problem for structural vibration measurements with Laser Doppler vibrometer on moving platform (LDVom) due to its highly random, frequent, and broadband nature, especially at high speeds. This paper develops a new post-processing framework to reduce speckle noise based on a case study of LDVom measurements on railway tracks. First, the characteristics of the speckle noise are studied. As the speed increases, the speckle noise occurs more frequently, with shorter intervals, shorter durations, greater amplitudes, and broader frequency bands. Then, a three-step despeckle framework is proposed, consisting of spike detection, imputation, and smoothing. This framework works by detecting and replacing spikes, recovering false positives, and smoothing false negatives and residual noise. To showcase this framework, we use a wavelet-based method for Step 1, an ARIMA-based method for Step 2, and a Butterworth filter for Step 3. Besides, the parameter selection strategies and the alternative methods are discussed. Next, the methods are validated through qualitative comparison and quantitative evaluation using a Monte Carlo-based strategy. We demonstrate that the proposed methods effectively reduce the speckle noise at speeds of at least 20 km/h while avoiding the pseudo vibrations. Finally, we show that the LDVom successfully captures the track vibrations at dominant frequencies of 500 ∼ 700 Hz with good repeatability between different laps and good agreement with trackside measurements.