Research on the Control of Wheel-rail Noise by Wheel Sound-proof Skirt for High-speed Train

Zhang, Huanhuan; Shi, Guozhen; Zhang, Xiaoan; Huang, Zhidan; Zhang, Xiaoyun; Xu, Zhengxiao

doi:10.1109/icma.2019.8816428

Cited by 3 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2019, Zhang et al installed sound insulation skirts on the wheels of a high-speed train. They built a model and changed the thickness and shape of the sound insulation skirt to achieve the best noise reduction effect [13]. In 2020, Song et al used a high-speed machine tool as a vehicle to study noise reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on the noise reduction mechanism and application of the noise simulation analysis of care robots

Tang,

Chen

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Currently, robots on the market have varying operational requirements due to their different functions. The care robots are primarily used to serve people under care. Therefore, reducing the amount of noise they make while moving is important. This study measured their noise in an anechoic room. This study found that the most noise is produced when they move forward and backward at full speed. The robot’s vibration signal was measured and compared with the noise source to propose the most effective way to reduce the noise. Due to the robot's small internal space, methods for noise reduction are limited. The care robot’s development must adopt quick customizable 3D printing technologies, produce sound absorption/insulation materials independently, determine the best sound absorption/insulation material and structural configuration, use impedance tubes to measure the sound absorption coefficient and sound insulation performance, and select the appropriate materials and implement them into the robot for actual tests. These results indicate that the noise value of the main contributing frequency can be reduced by 15dB at most. Furthermore, this study also used an acoustic simulation analysis to calculate and simulate the vibration and noise of the care robot. The vibration and noise signals during the robot's operation were measured and compared to provide a reference for manufacturers in design. Finally, experimental materials were added to the simulation and compared with the noise reduction results. The results show similar trends. Therefore, the model developed in this study may provide a fast, low-cost, and feasible method for the noise reduction of care robots.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Study on the noise reduction mechanism and application of the noise simulation analysis of care robots

Tang,

Chen

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…During trains uncoupling on the hump platform, the sound from wheelsets will become different when occurring an unreleased brake, due to a conversion from rolling friction to sliding friction [8], [9]. Generally, the unreleased braking will cause wheel tread damages [10] together with the noise pollution [11], [12], and may lead to wagons dragging or derailment in different degrees [13], which makes it necessary to develop methods to recognize the unreleased braking proactively.…”

Section: Introductionmentioning

confidence: 99%

An Acoustic-Based Recognition Algorithm for the Unreleased Braking of Railway Wagons in Marshalling Yards

Zhang

Liang

2020

IEEE Access

View full text Add to dashboard Cite

The abnormal braking of wagons is a challenging safety problem for operators at railway marshalling yard. This paper develops an acoustic-based technology to detect the unreleased braking of wagons during the uncoupling operation. Experiments have been conducted to collect the acoustic waves of wagons abnormal braking, as well as the background sounds like train whistling and wheel vibration. Before data collection, a wayside recording system and an experimental train composed of 5 different wagons have been prepared in the marshalling yard. The recognition algorithm consists of the fast Fourier transform (FFT), feature extraction, template matching and support vector machine (SVM) classification. Based on the sample data of different acoustic waves, the FFT is firstly performed to obtain the frequency spectrum from original time-domain signals. Then the major spectrum features of different sounds are carefully extracted for SVM training through a newly-devised algorithm, where the features include the spectrum center, spectrum flux, energy peak and corresponding frequency. During the SVM training, classifiers are designed under oneagainst-one strategy to guarantee the recognition accuracy. Given a test data, at most 3 SVM classifiers will be activated according to the decision matrix of template matching. Meanwhile, rules have been made to regulate the classification result considering different activation cases. Finally, a case study of all 12 sound categories has been performed to illustrate the application of proposed algorithm. Results show that the acoustic-based recognition algorithm is indeed reliable to identify wagons unreleased braking, with the global warning accuracy over 98%.

show abstract

Railway Noise Pollution in Urban Environments

Kumar,

Chowdary

2023

Handbook of Vibroacoustics, Noise and Harshness

View full text Add to dashboard Cite

Research on the Control of Wheel-rail Noise by Wheel Sound-proof Skirt for High-speed Train

Cited by 3 publications

References 6 publications

Study on the noise reduction mechanism and application of the noise simulation analysis of care robots

Study on the noise reduction mechanism and application of the noise simulation analysis of care robots

An Acoustic-Based Recognition Algorithm for the Unreleased Braking of Railway Wagons in Marshalling Yards

Railway Noise Pollution in Urban Environments

Contact Info

Product

Resources

About