Vol. 59 No. 6 (2025)

Open Access

Article

Article ID: 3284

Dual-idler gear-rack transmission mechanism: Analysis and optimization of vibration excited by defects

by Wenhe Han, Pengfei Wang, Shuming Guo, Shuyan Wang, Chenglin Ruan, Jiahao Yuan

Sound & Vibration, Vol.59, No.6, 2025;

In long-distance heavy-load transmission systems, the unique high load-bearing capacity and trajectory constraint characteristics of the double-idler rack-and-pinion mechanism can significantly improve the reliability of the transmission system. However, engineering practice has revealed that factors such as tooth profile distortion caused by residual stress from rack rolling, flatness errors of mounting surfaces, and linear expansion due to environmental temperature changes can markedly alter the dynamic meshing characteristics of the rack and pinion. During the meshing process of the double-idler rack-and-pinion, the deformation of the rack and pinion and installation errors can lead to meshing impacts, thereby generating significant impact noise. This paper analyzes abnormal meshing states influenced by changes in center distance and pitch, constructs a defective meshing model for the rack and pinion, and further identifies factors more sensitive to defective meshing impacts through dynamic simulations. Finally, the paper proposes a flexible floating idler shaft structure that effectively reduces meshing impacts. The results demonstrate that the proposed structure yields significant improvements, particularly in the direction of pitch variation, which is more sensitive to vibrations. Specifically, the effective vibration values for random pitch micro-variations and sudden rack pitch changes are reduced by 60.5% and 23.4%, respectively, while those for sudden changes in rack center distance are reduced by 57.01%. This research provides new methodological support for optimizing the dynamic characteristics of precision rack-and-pinion transmission systems.

Open Access

Article

Article ID: 3217

Acoustical simulation and optimization in Mosques

by Wasim Orfali

Sound & Vibration, Vol.59, No.6, 2025;

This article discusses the assessment and optimization of speech intelligibility in the Grand Mosque in Makkah. It is the most iconic Islamic site in the world. Most of the surface material used is marble, which is considered to be a highly sound reflective material. Acoustic measurement of the current conditions (with a surface area of 85,000 m²) was conducted. A virtual model was used to obtain an optimum acoustical solution. The measurement and inspection of ambient acoustical data were conducted using the EASERA measuring tool, and as a result, late energy arrival (after 100 ms) was noticed; this forms echoes and decreases intelligibility. It was also noticed that the measured background noise levels were as high as 90 dB (A). High temperature and humidity degrade the quality of the ambient acoustical environment. A virtual model for the targeted areas (Massa roof and adjacent perimeter) was created using EASE simulation software. Mapping results showed a high level of intelligibility (0.6) and homogenous distribution of the sound pressure level (SPL) (max. variation 9 dB). Loudspeaker columns minimized late energy arrival. The “active” approach improved the ambient acoustical environment in the targeted area and eliminated the necessity of an expensive “passive treatment”.

Open Access

Article

Article ID: 3458

A framework based on deep learning and the intelligent sensors for pavement assessment condition

by Wael A. Altabey

Sound & Vibration, Vol.59, No.6, 2025;

Long-term pavement performance is a key topic in highway engineering. By diving deep into research on pavement systems, we can bring together past, fragmented knowledge and experiences into a solid, comprehensive engineering theory. This essentially helps guide practical work like pavement design, construction, maintenance, and management. In this research, we look at using a mentoring system for automatic monitoring of pavement performance. By placing various sensors in different positions like the road surface, base, and slopes, a sensor network powered by Internet of Things technology is created. This setup allows for accurate and ongoing observation of factors like weather, physical condition, mechanical responses, and structural changes. Given the large volume of data and the need for real-time analysis, a data from sensors measuring temperature, humidity, pressure, asphalt strain, and displacement are used to train a deep learning model based on a Convolutional Neural Network (CNN) algorithm. This model helps predict multi-point displacement in the pavement, which allows us to detect issues like pavement damage. Impressively, the CNN model achieved accuracy, regression rates, and F-score of 93.51%, 91.63%, and 90.64% respectively. To improve the experimental section of a deep learning study, we compared the performance of the proposed model against several established or simpler algorithms (baselines) in the literature such as K-Nearest Neighbors (K-NN), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM). This contextualizes the model's efficacy and demonstrates its advantage over existing methods. This study showcases how different sensors can support deep learning algorithms in the assessment of pavement performance over the long term.

Open Access

Article

Article ID: 3820

Integral reinforcement learning based adaptive control of a RTG crane in twisting motion

by Jialu Lv, Yongli Zhang, Bingdong Jiang, Changli Zhang, Aihua Jiang

Sound & Vibration, Vol.59, No.6, 2025;

The rubber-tyred gantry (RTG) crane is employed as an essential piece of equipment for container handling in port operations. The RTG crane owns time-varying characteristics and parametric uncertainties. Meanwhile, the twisting of the container during its operation has a detrimental effect on the operation efficiency. In view of this, an improved adaptive control method based on integral reinforcement learning (IRL) is proposed in this paper. The mechanism model of the RTG system is developed, and the dynamic characteristics are analysed. Then, an IRL-based adaptive controller is designed and the involved positive definite Lyapunov matrix P is optimised to improve the control performance. In contrast to classical adaptive control methods, the proposed method calculates P based on real-time state variables, thereby eliminating model reliance and guaranteeing adaptive capacity. Finally, the effectiveness of the proposed method in enhancing anti-twisting performance is verified by digital and physical experiments. In the digital experiments, compared with the classical adaptive method, the load twisting settling time is reduced by 1 s, and the maximum twisting angle is decreased by approximately 0.7 degrees. In the physical experiments, despite the influence of practical friction and disturbances, the settling time is still reduced by about 1 s. These results show that the proposed scheme possesses both theoretical effectiveness and engineering practicality.

Open Access

Article

Article ID: 3617

An incremental intelligent fault diagnosis method for marine diesel engines based on CNN-Transformer and cosine similarity

by Yingying Wu, Yongjian Wang, Hangxi Cai, Guoqiang Li, Xin Wei

Sound & Vibration, Vol.59, No.6, 2025;

This paper proposes an incremental intelligent fault diagnosis method for marine diesel engines based on a Convolutional Neural Network (CNN)-Transformer architecture and cosine similarity. The method is designed to address critical limitations of conventional supervised diagnostic frameworks, including heavy reliance on labeled data, weak cross-condition generalization, and the inability to identify new or evolving fault types. The model first employs CNN to extract local temporal features from vibration signals and then uses a Transformer to learn high-level semantic representations of fault attributes. During the incremental learning phase, known fault classes—such as exhaust valve failures—are used to train the model. In the testing phase, the model calculates the cosine similarity between feature embeddings of unseen samples and the prototypes of known classes in the attribute space to determine their classification or novelty. This mechanism enables effective identification of both known and novel faults, including those in cylinder liners and piston rings, without requiring prior labeled data for the latter. Experimental results demonstrate that the proposed approach achieves superior classification accuracy, robustness, and adaptability compared to traditional supervised methods, offering a scalable and generalizable solution for intelligent marine diesel engine fault diagnostics.

Open Access

Article

Article ID: 3620

Nonlinear free vibration of conical beams using He's frequency formula: Educational implications

by Lingxing Song, Xin Wei, Cheligeer Bai, Jiahui Yu

Sound & Vibration, Vol.59, No.6, 2025;

This study presents an analytical solution for the nonlinear free vibration of conical beams using He's frequency formula. This solution aims to efficiently address the challenges posed by their axially varying cross-sectional dimensions and associated nonlinear mechanical behaviors. Conical beams, with their customized stiffness and mass distribution, find wide application in aerospace, civil engineering, and micro-electromechanical systems (MEMS), where precise vibration analysis is imperative for ensuring structural stability and performance. The frequency formula, rooted in residual minimization, is employed to derive the frequency-amplitude relationship of conical beams. This method avoids complex iterative procedures and reduces computational complexity compared to traditional methods like Aboodh Transform-based variational iteration method (ATVIM) or homotopy perturbation. The validation of the method against ATVIM and numerical solutions (4th-order Runge–Kutta method) confirms its accuracy, with close agreement across moderate to large amplitudes and a frequency relative error of less than 5%. Beyond its practical utility in engineering design—enabling rapid parametric analysis for resonance avoidance—the study also highlights educational implications, as the conical beam case study bridges abstract nonlinear dynamics theory with real-world applications, aiding students in understanding frequency-amplitude coupling and method selection. This work demonstrates that He's frequency formula offers a robust, accessible framework for analyzing conical beam vibrations, linking theoretical nonlinear dynamics, engineering practice, and educational value.

Open Access

Article

Article ID: 3799

Vibrational behaviour of MWCNT-reinforced single and cross overlap adhesive joints: Experimental and FEA analysis

by Vikram Hanmantrao Londhe, Madan Mohanrao Jagtap

Sound & Vibration, Vol.59, No.6, 2025;

Adhesively bonded joints are widely used in lightweight aerospace and automotive structures, but their dynamic performance under vibrational conditions remains a critical design concern. Recent studies have shown that nanofiller-modified adhesives can enhance static strength, but limited experimental evidence is available on their influence on the vibration behaviour of different joint configurations. In this work, the vibrational characteristics of single lap joints (SLJs) and cross lap joints (CLJs) bonded with epoxy adhesive reinforced with 1 wt% multiwall carbon nanotubes (MWCNTs) are investigated through combined experimental and numerical approaches. Modal testing was conducted using an impact hammer technique and fast fourier transform (FFT) based frequency response analysis to determine natural frequencies and damping characteristics. Finite element modal harmonic analyses were performed using a three-dimensional viscoelastic model to simulate the dynamic response of the joints. The results indicate that the incorporation of MWCNTs leads to a consistent increase of the joints. The results indicate that the incorporation of MWCNTs leads to a consistent increase in natural frequencies for both joint types, reflecting enhanced joint stiffness due to nanoscale reinforcement of the adhesive layer. The effect is more pronounced in cross-lap joints, highlighting the strong sensitivity of adhesive-dominated joint geometries to stiffness modification. Numerical predictions show reasonable agreement with experimental measurements, validating the proposed modelling approach despite unavoidable experimental uncertainties. The findings demonstrate the potential of MWCNT-reinforced epoxy adhesives to improve vibration resistance and dynamic stability in bonded joints, providing useful insight for the design and optimization of lightweight structures subjected to dynamic loading.