Sound quality evaluation and prediction for the emitted noise of axial piston pumps

Zhang, Junhui; Xia, Shiqi; Ye, Shaogan; Xu, Bing; Zhu, Shiqiang; Xiang, Jiawei; Tang, Hesheng

doi:10.1016/j.apacoust.2018.09.015

Cited by 30 publications

(14 citation statements)

References 43 publications

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“…9 Existing instruments do not directly measure the subjective sound perception in the human auditory system, but relevant studies have shown that subjective evaluation was quantified by calculating objective parameters of psycho-acoustics. 10,11 Objective acoustic parameters can reflect psychological response mechanism and perceptual characteristics with the advantages of low cost and high efficiency; however, objective evaluation is not a substitute for the more intuitive subjective test. 12 Note that the whole subjective evaluation process requires a large 1 School of Mechanical and Automotive Engineering, Xiamen University of Technology, China number of personnel, equipment and time, and evaluation test is difficult to repeat.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, based on comprehensive consideration of subjective and objective evaluation results, the mathematical relationship between them is often established to predict acoustic comfort. 10,13,14 Therefore, after doing improvement tests for the same prototype vehicles in the future, it only needs to calculate the objective parameters of the new collected noise signals and bring them into the established mathematical model to obtain the predicted acoustic comfort instead of organizing the subjective evaluation test again.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear modeling and prediction of forklift acoustic annoyance based on the improved neural networks

et al. 2021

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Aiming at the characteristics of high decibels and multiple samples for forklift noise, a subjective evaluation method of rank score comparison (RSC) based on annoyance is presented. After pre-evaluation, comprehensive evaluation and data tests on collected 50 noise samples, the annoyance grades of all noise samples were obtained, and seven psycho-acoustic parameters including linear sound pressure level (LSPL), A-weighted sound pressure level (ASPL), loudness, sharpness, roughness, impulsiveness and articulation index (AI) were determined by correlation calculation. Considering the nonlinear characteristics of human ear subjective perception, objective parameters, and annoyance were used as input and output variables correspondingly and then three nonlinear mathematical models of forklift acoustic annoyance were established using traditional artificial neural network (ANN), genetic-algorithm neural network (GANN), and particle-swarm-optimization neural network (PSONN). Moreover, the prediction accuracy of the three models was tested and compared by sample data. The results indicate that the average relative error (ARE) between the experimental and predicted values of acoustic annoyance based on PSONN model is 3.893%, which provides an effective technical support for further optimization and subjective evaluation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear modeling and prediction of forklift acoustic annoyance based on the improved neural networks

et al. 2021

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“…The cyclic mechanism of pumps and motors generates periodic noise in the system (i.e. narrowband), and instability and cavitation in valves introduce broadband noise [2]. New energy-efficient hydraulic devices, such as electrohydrostatic actuators and switched inertance hydraulic converters (SIHC) exhibit particularly significant noise problems due to the nature of their control and design [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Novel Integrated Active and Passive Control of Fluid-Borne Noise in Hydraulic Systems

Pan

Yuan

Ding

et al. 2021

Journal of Dynamic Systems, Measurement, and Control

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Fluid-borne noise (FBN) is a major contributor to structure-borne noise (SBN) and air-borne noise (ABN) in hydraulic fluid power systems and could lead to increased fatigue in system components. FBN is caused by the unsteady flow generated by pumps and motors and propagates through the system resulting in SBN and ABN. New hydraulic technologies such as digital switched hydraulic converters also generate unavoidable FBN. This article reports on a novel integrated FBN attenuation approach, which employs a hybrid control system by integrating an active feed forward noise attenuator with passive tuned flexible hoses. The active system which consists of adaptive notch filters using a variable step-size filtered-X Least Mean Squares algorithm is used to control a newly designed high-force high-bandwidth piezoelectric actuator in order to attenuate the dominant narrowband pressure ripples. The passive hose is tuned in the frequency domain and used to cancel the high-frequency pressure ripples. A time-domain hose model considering coupling of longitudinal wall and fluid waves was used to model the flexible hose in the integrated control system. Very good FBN cancellation was achieved by using the integrated control approach in simulation and experiments. It is an effective, cost-efficient and practical solution for FBN attenuation. The problem of high noise levels generated by hydraulically powered machines has risen significantly in awareness within industry and amongst the general public, and this work constitutes an important contribution to the sustainable development of low noise hydraulic fluid power machines.

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“…The sharpness calculation is based on the specific loudness [19]. To date, only the sharpness generated from the Zwicker specific loudness has been standardized (as DIN 45692:2009 [22]) and applied in numerous SQ studies [23][24][25][26][27][28][29]. To calculate the sharpness using the Moore-Glasberg specific loudness, the method proposed by Swift and Gee [30,31] was applied in this study.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Improvement of the Sound Quality of a Diesel Engine Based on Tests and Simulations

Yang

Feng

et al. 2020

Energies

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Traditional acoustic evaluation of a diesel engine generally uses the A-weighted sound pressure level (AWSPL) and radiated sound power to assess the noise of an engine prototype present in an experiment. However, this cannot accurately and comprehensively reflect the auditory senses of human subjects during the simulation stage. To overcome such shortage, the Moore–Glasberg loudness and sharpness approach is applied to evaluate and improve the sound quality (SQ) of a 16 V-type marine diesel engine, and synthesizing noise audio files. Through finite element (FE) simulations, the modes of the engine’s block and the average vibrational velocity of the entire engine surface were calculated and compared with the test results. By further applying an automatically matched layer (AML) approach, the engine-radiated sound pressure level (SPL) and sound power contributions of all engine parts were obtained. By analyzing the Moore–Glasberg loudness and sharpness characteristics of three critical sound field points, an improvement strategy of the oil sump was then proposed. After improvement, both the loudness and sharpness decreased significantly. To verify the objective SQ evaluation results, ten noise audio clips of the diesel engine were then synthesized and tested. The subjective evaluation results were in accordance with the simulated analysis. Therefore, the proposed approach to analyze and improve the SQ of a diesel engine is reliable and effective.

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Sound quality evaluation and prediction for the emitted noise of axial piston pumps

Cited by 30 publications

References 43 publications

Nonlinear modeling and prediction of forklift acoustic annoyance based on the improved neural networks

Nonlinear modeling and prediction of forklift acoustic annoyance based on the improved neural networks

Novel Integrated Active and Passive Control of Fluid-Borne Noise in Hydraulic Systems

Evaluation and Improvement of the Sound Quality of a Diesel Engine Based on Tests and Simulations

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