Qixin Lan¹, Binqiang Chen^1,*, Bin Yao¹, Wangpeng He²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2825-2844, 2024, DOI:10.32604/cmes.2023.030378

Abstract The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the tool will generate significant noise and vibration, negatively impacting the accuracy of the forming and the surface integrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wear state and promptly replace any heavily worn tools to guarantee the quality of the cutting. The conventional tool wear monitoring models, which are based on machine learning, are specifically built for the intended cutting conditions. However, these models require retraining when the cutting conditions undergo any… More >

M. Farid Yahya¹, Reduan Mat Dan^2,3,*, M. Fadzlee Samsubaha¹, Zaini Rashid¹, Azma Putra^2,3,*

Sound & Vibration, Vol.57, pp. 119-131, 2023, DOI:10.32604/sv.2023.045029

Abstract A case study of excessive vibration on a motor-compressor system is presented in this paper. After barely two months of operation, the reciprocating compressor motor’s routine monitoring revealed excessive axial vibration amplitude. For this reason, the Operational Modal Analysis (OMA) was carried out in order to identify the primary cause. According to the investigation, one of the harmonic components which was 18 times the motor’s running speed matched with a resonance frequency of 112 Hz. According to OMA study, the motor was vibrating in torsional motion because the compressor’s load had stimulated the entire motor-compressor unit at this resonance frequency.… More >

Yuanjun Dai^1,2,*, Xin Wei¹, Baohua Li¹, Cong Wang¹, Kunju Shi¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.2, pp. 337-348, 2024, DOI:10.32604/fdmp.2023.042434

Abstract To study the impact of the trailing-edge wear on the vibrational behavior of wind-turbine blades, unworn blades and trailing-edge worn blades have been assessed through relevant modal tests. According to these experiments, the natural frequencies of trailing-edge worn blades −1, −2, and −3 increase the most in the second to fourth order, the fifth order increases in the middle, and the first order increases the least. The damping ratio data indicate that, in general, the first five-order damping ratios of trailing-edge worn blades −1 and trailing-edge worn blades −2 are reduced, and the first five-order damping ratios of trailing-edge worn… More >

Kanhua Su, Jian Wei^*, Meng Li, Hao Li, Wenghao Da, Lang Zhang

Sound & Vibration, Vol.57, pp. 97-117, 2023, DOI:10.32604/sv.2023.043734

Abstract During the drilling process, stick-slip vibration of the drill string is mainly caused by the nonlinear friction generated by the contact between the drill bit and the rock. To eliminate the fatigue wear of downhole drilling tools caused by stick-slip vibrations, the Fractional-Order Proportional-Integral-Derivative (FOPID) controller is used to suppress stick-slip vibrations in the drill string. Although the FOPID controller can effectively suppress the drill string stick-slip vibration, its structure is flexible and parameter setting is complicated, so it needs to use the corresponding machine learning algorithm for parameter optimization. Based on the principle of torsional vibration, a simplified model… More > Graphic Abstract

Zhicheng Liu¹, Long Zhao^1,*, Guanru Wen¹, Peng Yuan², Qiu Jin¹

Structural Durability & Health Monitoring, Vol.17, No.6, pp. 541-555, 2023, DOI:10.32604/sdhm.2023.029760

Abstract The displacement of transmission tower feet can seriously affect the safe operation of the tower, and the accuracy of structural health monitoring methods is limited at the present stage. The application of deep learning method provides new ideas for structural health monitoring of towers, but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning (DL). In this paper, we propose a DT-DL based tower foot displacement monitoring method, which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method. Then the vibration signal… More > Graphic Abstract

Peter Watson¹, Sebastien Vincent Bonnieu², Marcello Lappa^1,*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.1, pp. 1-29, 2024, DOI:10.32604/fdmp.2023.029280

Abstract We present a short retrospective review of the existing literature about the dynamics of (dry) granular matter under the effect of vibrations. The main objective is the development of an integrated resource where vital information about past findings and recent discoveries is provided in a single treatment. Special attention is paid to those works where successful synthetic routes to as-yet unknown phenomena were identified. Such landmark results are analyzed, while smoothly blending them with a history of the field and introducing possible categorizations of the prevalent dynamics. Although no classification is perfect, and it is hard to distillate general properties… More >

Baohua Li¹, Yuanjun Dai^1,2,*, Jingan Cui¹, Cong Wang¹, Kunju Shi¹

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.12, pp. 3033-3043, 2023, DOI:10.32604/fdmp.2023.029583

Abstract To reduce the vibration and aerodynamic noise of wind turbines, a new design is proposed relying on a blade with a bifurcated apex or tip. The performances of this wind turbine wheel are tested at the entrance of a DC (directaction) wind tunnel for different blade tip angles and varying centrifugal force and aerodynamic loads. The test results indicate that the bifurcated apex can reduce the vibration acceleration amplitude and the vibration frequency of the wind wheel. At the same time, the bifurcated apex can lower the maximum sound pressure level corresponding to the rotating fundamental frequency of the wind… More >

Youhao Wang¹, Chuntian Zhe¹, Chang Guo², Jinpeng Li³, Jinheng Li³, Shen Cheng², Zitian Wu¹, Suoying He¹, Ming Gao^1,*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.12, pp. 2965-2980, 2023, DOI:10.32604/fdmp.2023.029292

Abstract Flow channels with a variable cross-section are important components of piping system and are widely used in various fields of engineering. Using a finite element method and modal analysis theory, flow-induced noise, mode shapes, and structure-borne noise in such systems are investigated in this study. The results demonstrate that the maximum displacement and equivalent stress are located in the part with variable cross-sectional area. The average excitation force on the flow channel wall increases with the flow velocity. The maximum excitation force occurs in the range of 0–20 Hz, and then it decreases gradually in the range of 20–1000 Hz.… More >

Xiaofang Kang^1,*, Jian Wu¹, Xinqi Wang¹, Shancheng Lei²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 551-593, 2024, DOI:10.32604/cmes.2023.029044

Abstract In order to improve the seismic performance of adjacent buildings, two types of tuned inerter damper (TID) damping systems for adjacent buildings are proposed, which are composed of springs, inerter devices and dampers in serial or in parallel. The dynamic equations of TID adjacent building damping systems were derived, and the H₂ norm criterion was used to optimize and adjust them, so that the system had the optimum damping performance under white noise random excitation. Taking TID frequency ratio and damping ratio as optimization parameters, the optimum analytical solutions of the displacement frequency response of the undamped structure under white… More > Graphic Abstract

Chun Sha^1,*, Chaohui Yue², Wenchen Wang³

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 369-381, 2023, DOI:10.32604/sdhm.2023.027948

Abstract Convolution neural networks in deep learning can solve the problem of damage identification based on vibration acceleration. By combining multiple 1D DenseNet submodels, a new ensemble learning method is proposed to improve identification accuracy. 1D DenseNet is built using standard 1D CNN and DenseNet basic blocks, and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset sampling. When using submodels for damage identification, the voting method ideas in ensemble learning are used to vote on the results of each submodel, and then vote centrally. Finally, the cantilever damage problem… More >