Search

Wayside acoustic detection is a promising technology for railway-vehicle bearing health monitoring due to its merits of non-conduct measurement, low cost, and early warning capacity. However, the diagnostic accuracy will be reduced by the problems of strong background noise and Doppler distortion. Considering the super spatial directivity ability of the microphone array, in this paper, a uniform rectangular array (URA) and an optimal spatial filter (OSF) based on the principle of minimum variance distortion-less response (MVDR) are designed to improve the diagnostic accuracy. Compared with the traditional single microphone and linear array, spatial directivity can be improved significantly so that the better anti-noise performance and higher diagnostic accuracy can be achieved. First, a URA consisting of 15 microphone elements arranged into five columns and three rows is designed to capture the wayside acoustic signal. Second, the direction angle of the target moving sound source with high accuracy is calculated at different times. Third, an OSF based on the principle of the MVDR is designed to extract the target sound source signal. Fourth, Doppler effect embedded in the filtered signal is eliminated using the MVDR spectrum estimation and resampling method. Finally, the diagnosis decision is made through an envelope spectrum analysis. The comparative simulation and experimental case studies are carried out to verify the effectiveness and improvement of the proposed method.

The development of image sensor and optics enables the application of vision-based techniques to the non-contact dynamic vibration analysis of large-scale structures. As an emerging technology, a vision-based approach allows for remote measuring and does not bring any additional mass to the measuring object compared with traditional contact measurements. In this study, a high-speed vision-based sensor system is developed to extract structure vibration signals in real time. A fast motion extraction algorithm is required for this system because the maximum sampling frequency of the charge-coupled device (CCD) sensor can reach up to 1000 Hz. Two efficient subpixel level motion extraction algorithms, namely the modified Taylor approximation refinement algorithm and the localization refinement algorithm, are integrated into the proposed vision sensor. Quantitative analysis shows that both of the two modified algorithms are at least five times faster than conventional upsampled cross-correlation approaches and achieve satisfactory error performance. The practicability of the developed sensor is evaluated by an experiment in a laboratory environment and a field test. Experimental results indicate that the developed high-speed vision-based sensor system can extract accurate dynamic structure vibration signals by tracking either artificial targets or natural features.

A novel, bio-inspired, single-phase driven piezoelectric linear motor (PLM) using an asymmetric stator was designed, fabricated, and tested to avoid mode degeneracy and to simplify the drive mechanism of a piezoelectric motor. A piezoelectric transducer composed of two piezoelectric stacks and a displacement amplifier was used as the driving element of the PLM. Two simple and specially designed claws performed elliptical motion. A numerical simulation was performed to design the stator and determine the feasibility of the design mechanism of the PLM. Moreover, an experimental setup was built to validate the working principles, as well as to evaluate the performance, of the PLM. The prototype motor outputs a no-load speed of 233.7 mm/s at a voltage of 180 Vp-p and a maximum thrust force of 2.3 N under a preload of 10 N. This study verified the feasibility of the proposed design and provided a method to simplify the driving harmonic signal and structure of PLMs. [ABSTRACT FROM AUTHOR]

This note reports an efficient singular value decomposition (SVD)-based vibration extraction approach that recovers sound information in silent high-speed video. A high-speed camera of which frame rates are in the range of 2 kHz-10 kHz is applied to film the vibrating objects. Sub-images cut from video frames are transformed into column vectors and then reconstructed to a new matrix. The SVD of the new matrix produces orthonormal image bases (OIBs) and image projections onto specific OIB can be recovered as understandable acoustical signals. Standard frequencies of 256 Hz and 512 Hz tuning forks are extracted offline from their vibrating surfaces and a 3.35 s speech signal is recovered online from a piece of paper that is stimulated by sound waves within 1 min. [ABSTRACT FROM AUTHOR]