Evaluation and optimization of sound quality in high-speed trains

Qian, Kun; Hou, Zhichao; Sun, Qiang; Gao, Yang; Sun, Dengke; Liu, Ruixue

doi:10.1016/j.apacoust.2020.107830

Cited by 16 publications

(8 citation statements)

References 27 publications

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“…For each windowk, calculate the ratio of the SAWP at 400 $ 700 Hz using Equation ( 2). In the step, the ratio of the SAWP is employed instead of the SAWP itself, to reduce the influence of train speed variation on the energy concentration [34] and further on the anomaly identification.…”

Section: Inspection Algorithmmentioning

confidence: 99%

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Carriage interior noise-based inspection for rail corrugation on high-speed railway track

et al. 2022

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Section: Inspection Algorithmmentioning

confidence: 99%

“…The speed-dependent parameter can be extracted by either interior noise measurements (e.g., Fig. 4 in Qian et al [34]) or numerical simulations (e.g., Fig. 6 in Molodova et al [36]).…”

Section: Speed Dependency Of Rail Corrugation Indexmentioning

confidence: 99%

Carriage interior noise-based inspection for rail corrugation on high-speed railway track

et al. 2022

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“…where N min (z) and N max (z) are the minimum and maximum values of the specific loudness (in z-th Bark), respectively [26].…”

Section: Psychoacoustic Parameters Of Sqmentioning

confidence: 99%

Interior Sound Quality Prediction of Pure Electric Vehicles Based on Transfer Path Synthesis

et al. 2021

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The interior sound quality (SQ) of pure electric vehicles (PEVs) has become an important consideration for users purchasing vehicles. At present, it is insufficient to take the sound pressure level as the interior acoustics design index of PEVs. Transfer path analysis (TPA) and transfer path synthesis (TPS) that take the SQ of interior noise as the improvement target remains in the preliminary exploration stage. In this paper, objective psychoacoustic parameters of SQ were taken as evaluation indexes of interior PEV noise. A virtual interior SQ synthesis model was designed on the basis of TPA and TPS, which combines experimentation and simulation. The SQ synthesis model demonstrates each noise component contribution in a PEV by new SQ separation technology. First, the interior noise transfer path and noise source of the PEV were determined in a synthesis analysis method of the interior PEV noise. Second, on the basis of the composition mechanism of interior noise and the basic principle of TPA, the excitation signal and transfer function of each interior noise path in the PEV were tested. On the basis of TPS, the interior SQ synthesis model of PEV was then established. Finally, the accuracy of the prediction model was verified in simulation and experimental comparison studies on the psychoacoustic objective parameters of SQ. The SQ objective parameter value of each transfer path was quantified by using contribution analysis. The results are expected to improve the comfort of the interior acoustic environment and enhance the competitiveness of vehicle products. They also provide an effective reference and new ideas for the development of interior SQ in PEVs.

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“…e safe and reliable operation of equipment is highly dependent on healthy performance; however, equipment will gradually age during operation, which in turn will lead to reduced performance or even failure, further causing system downtime or even catastrophic accidents. Modern equipment such as high-speed trains [1], missile weapons [2,3], industrial robots [4,5], petrochemical equipment, and other modern equipment are gradually becoming larger, more diversi ed, and integrated at the same time as their functions continue to improve.…”

Section: Introductionmentioning

confidence: 99%

“…The safe and reliable operation of equipment is highly dependent on healthy performance; however, equipment will gradually age during operation, which in turn will lead to reduced performance or even failure, further causing system downtime or even catastrophic accidents. Modern equipment such as high-speed trains [ 1 ], missile weapons [ 2 , 3 ], industrial robots [ 4 , 5 ], petrochemical equipment, and other modern equipment are gradually becoming larger, more diversified, and integrated at the same time as their functions continue to improve. The service process is affected by variable environment, variable load, variable working conditions, large disturbances, and strong impact; the overall and key components performance will inevitably degrade; once the equipment performance degradation is caused by the final failure, it will cause huge casualties and property damage.…”

Section: Introductionmentioning

confidence: 99%

Aging Time Prediction Model Analysis and Numerical Simulation of Random Degradation Equipment Based on Big Data Linkage Technology

Peng

2022

Computational Intelligence and Neuroscience

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In this study, we focus on the relevance of remaining life prediction of randomly degraded equipment in the context of big data monitoring and the core issue of quantifying uncertainty in remaining life prediction. We analyze the limitations and common problems of current research. To address the limitations and common problems, a solution for predicting the remaining life of randomly degraded devices with multisource sensing monitoring in the context of big data is proposed, and the feasibility and effectiveness of the idea are verified using battery data. Finally, multiple machine learning methods, such as support vector machines, random forests, recurrent neural networks, and convolutional neural networks, are combined to predict the remaining life of batteries, and these four machine learning methods perform well in the work of battery remaining life prediction and solve the key scientific problems.

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Evaluation and optimization of sound quality in high-speed trains

Cited by 16 publications

References 27 publications

Carriage interior noise-based inspection for rail corrugation on high-speed railway track

Carriage interior noise-based inspection for rail corrugation on high-speed railway track

Interior Sound Quality Prediction of Pure Electric Vehicles Based on Transfer Path Synthesis

Aging Time Prediction Model Analysis and Numerical Simulation of Random Degradation Equipment Based on Big Data Linkage Technology

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