Journal of Vibroengineering: Table of Contents

A CLSTM-CNN-Attention hybrid model and its application in fault diagnosis

Liang, Yuanhui — 2026-04-21T00:00:00Z

Journal of Vibroengineering, Vol. 28, Issue 3, 2026, p. 495-513.
Yuanhui Liang, Yuyu Zhu, Lei Wang
Autonomous underwater vehicles (AUVs) are indispensable equipments in underwater detection, surveying, and investigation. As the main power source of AUV, the accurate and timely fault diagnosis of thruster plays key role in ensuring its safe navigation. However, this task is full of challenges due to the complication, vagueness, and randomness of underwater settings. To address the issues, a hybrid diagnosis model named CLSTM-CNN-Attention is proposed, which combines convolutional long short term memory (CLSTM), convolutional neural network (CNN) and attention mechanism. Specifically, it combines an improved hybrid network to realize the connection of long-term and short-term memory (LSTM) with CNN in parallel, which can capture the time-related and space-related fault information of input signal simultaneously. At the same time, a new linear rectification function is also introduced into the hybrid model to enhance its anti-interference capability. Finally, the diagnostic performance of the hybrid model is further improved by adding attention mechanisms, which could better focus on the fused information. Experimental and comparison results indicate that the suggested approach has remarkable interference suppression capacity and surpasses other relevant methods, demonstrating good performance in fault diagnosis of AUV thruster.

Design and research of mechanical waveguide filters based on transmission line impedance matching

Gao, Xiaolin — 2026-04-21T00:00:00Z

Journal of Vibroengineering, Vol. 28, Issue 4, 2026, p. 940-955.
Xiaolin Gao, Xi Chen
In response to the growing demand for advanced vibration and noise suppression in high-end equipment within the manufacturing sector, this paper proposes a novel design method for mechanical waveguide filters based on transmission line impedance matching theory. By establishing a precise force-electric analogy model, this approach effectively bridges mature circuit theory with mechanical dynamics, enabling the accurate prediction and control of elastic wave propagation. To address the prevalent issue of non-stationary vibration signals in industrial applications the Fractional Fourier Transform is innovatively employed for superior time-frequency analysis. The designed dual-joint matching filter validates this methodology, exhibiting outstanding performance at a center frequency of 2500 Hz with an insertion loss of 0.8 dB and a return loss of 19.1 dB. The filter also demonstrates sharp frequency selectivity, effective stopband suppression, and stable phase response, which are critical for maintaining signal integrity in precision systems. This research, which integrates theoretical modeling, equivalent circuit analysis, and advanced signal processing, provides a robust and efficient design framework. The proposed technique offers significant practical value for the mechanical industry, presenting a viable solution to enhance the dynamic performance, operational reliability, and noise control in modern mechanical systems, with direct applicability in fields such as aerospace, precision manufacturing, and automotive engineering.

Uncertain RUL prediction for aircraft engines: an attention-based ensemble method with partial-transfer Bayesian deep learning

Zeng, Jiyan — 2026-04-21T00:00:00Z

Journal of Vibroengineering, Vol. 28, Issue 4, 2026, p. 922-939.
Jiyan Zeng, Yaohua Tong, Yujie Cheng, Chen Lu
Accurate prediction of the remaining useful life (RUL) of aircraft engines is crucial for ensuring flight safety and optimizing maintenance strategies. However, traditional data-driven methods typically yield point estimation, failing to quantify uncertainties arising from data noise and model limitations. This study proposes an attention-based ensemble method with partial-transfer Bayesian deep learning (Att-ensembled PT-BDL) for uncertainty quantification in aircraft engine RUL prediction. The proposed method transfers weights and biases from existing point estimation deep learning models as prior knowledge to the mean values of weights and biases in Bayesian deep learning models, freezing these parameters during training to reduce trainable parameters and enhance computational efficiency. An ensemble framework, enhanced by an attention mechanism, integrates multiple models to improve prediction accuracy and uncertainty quantification performance. A case study is conducted to demonstrate the effectiveness of the proposed method with a dataset of the PHM data challenge. The experiment results show that the proposed Att-ensembled PT-BDL method can achieve a better prediction accuracy and uncertainty quantification performance in terms of root mean square error (RMSE), prediction interval coverage probability (PICP) and prediction interval normalized average width (PINAW).

Flow characteristics of large-scale Francis turbine during transient processes

He, Xiuru — 2026-04-28T00:00:00Z

Journal of Vibroengineering, Vol. 28, Issue 3, 2026, p. 705-719.
Xiuru He, Xiangbo Liao, Qianlin Luo, Wengui Zhao, Xueli An
With the continuous establishment and improvement of the new power system, higher requirements have been imposed on the operation of large-scale hydroelectric generating units, which makes the flow inside the turbine more complex. This study employed the computational fluid dynamics (CFD) method based on the SST k-ω turbulence model to numerically simulate the internal flow field of a large Francis turbine during the load reduction transition process, and coupled an acoustic model to analyze its flow-induced noise. The results show that under a constant head, as the guide vane opening decreases (load reduces), the low-pressure and low-speed zone inside the runner expands from the blade outlet to the inlet, while the blade passage vortex and draft tube vortex in the runner gradually develop, and the flow instability increases. The noise sound pressure level shows a decreasing trend with the reduction of load, but there are still significant peaks in specific frequency bands, among which 22 Hz and 100 Hz are respectively related to blade vibration and the operational characteristics of the runner. This study reveals the correlation characteristics between the internal flow and noise of the turbine during the transition process, providing a reference basis for the safe and stable operation of the unit, vibration and noise control, and the optimization of the transition process.

Adaptive vibration-based bearing fault diagnosis for mud pumps using MSESSA-optimized variational mode decomposition

Zhang, Chao — 2026-05-08T00:00:00Z

Journal of Vibroengineering, Vol. 28, Issue 4, 2026, p. 887-921.
Chao Zhang, Bing Wang, Zhenzhong Zhang
Mud pumps operate under harsh conditions, where strong background noise, load fluctuations, and complex excitations result in highly non-stationary vibration signals, posing significant challenges for reliable bearing fault diagnosis. To address these challenges, this study proposes an adaptive vibration-based fault diagnosis framework for mud pump bearings, utilizing variational mode decomposition optimized by a multi-strategy enhanced sparrow search algorithm (MSESSA-VMD). The proposed MSESSA incorporates sine–cosine perturbation, Cauchy mutation, and adaptive weight updating mechanisms to enhance global exploration and convergence stability. In contrast to conventional SSA-based optimization approaches, this strategy enables automatic and robust optimization of key VMD parameters, including the number of decomposition modes and the penalty factor, thereby improving decomposition quality under complex operating conditions. Fault-relevant intrinsic mode functions (IMFs) are subsequently selected based on energy-based criteria for multi-dimensional feature extraction. Intelligent fault classification is then performed using a Light Gradient Boosting Machine (LightGBM) classifier. The effectiveness of the proposed framework is first verified using the benchmark Case Western Reserve University (CWRU) bearing dataset and further validated on simulated mud pump bearing vibration signals to assess robustness under industrial-like operating conditions. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy of 96.14 %, outperforming conventional VMD-based and SSA-VMD approaches in terms of accuracy and robustness against noise and signal non-stationarity. Overall, this study presents a novel framework that integrates a multi-strategy enhanced sparrow search mechanism with adaptive VMD parameter optimization for mud pump bearing fault diagnosis, providing a robust and generalizable solution for vibration-based machinery health monitoring in complex industrial environments.

Motor bearing fault early warning model for TCN integrating multi-attention mechanisms

Liu, Taohuang — 2026-05-08T00:00:00Z

Journal of Vibroengineering, (in Press).
Taohuang Liu, Zhihua Hu, Gulizhati Hailati, Lili Tao, Yiheng Hu, Feng Ding
Aiming at the problem of limited accuracy in motor bearing fault prediction under low-frequency, low-dimensional, and imbalanced data scenarios, this paper proposes a motor bearing fault early warning model based on a temporal convolutional network that integrates multi-attention mechanisms. The model leverages these mechanisms to enhance the network's ability to model long-term dependencies and focus on critical fault features from sparse data. To validate its performance, ablation and comparative experiments were conducted on a dataset of 10,000 samples collected from a self-built test bench. The experiment results demonstrate the superiority of the proposed model. Specifically, the Multi-Attention Temporal Convolutional Network achieved a coefficient of determination of 0.9629 and a Mean Absolute Error of 0.0244, significantly outperforming the standard Temporal Convolutional Network baseline which only achieved a Mean Absolute Error of 0.1349. These results indicate the excellent performance of the proposed model in this specific data prediction scenario, providing an effective solution to practical engineering problems where high-precision sensing equipment is unavailable.

Fault diagnosis of automotive transmission bearings based on improved CNN and transformer

Huang, Zhaoming — 2026-05-15T00:00:00Z

Journal of Vibroengineering, Vol. 28, Issue 3, 2026, p. 560-580.
Zhaoming Huang, Xuhui Yang, Can Guo
To address the challenges of fault feature extraction and the weak adaptability of diagnostic models for automotive gearbox bearings under complex operating conditions, this study proposes an improved intelligent diagnostic model that integrates Convolutional Neural Networks (CNN) and Transformers with a dual-stage dynamic sparse activation and three-dimensional attention mechanism. First, to overcome the limitations of traditional CNN with fixed architectures and limited perception of multi-domain fault features, a dual-stage dynamic sparse activation mechanism is designed. It enables adaptive computation path selection based on the complexity of input features. Then, to enhance the perception of multidimensional time-frequency-phase fault information, the Hilbert transform is applied to construct a three-dimensional feature tensor containing instantaneous amplitude, frequency, and phase. A 3D self-attention module is embedded to achieve multi-domain feature fusion. Finally, the proposed method is validated using experimental data collected under various gearbox bearing fault states and operating conditions. The results show that the model achieves an accuracy of 99.73 %, with precision, recall, and F1-score of 99.64 %, 99.63 %, and 99.68 %, respectively – all outperforming state-of-the-art methods such as GDS-YOLOv5s. Moreover, the model maintains stable recognition performance under noise and variable load conditions. These findings demonstrate that the proposed approach effectively captures subtle multi-domain fault features and exhibits strong adaptability and robustness, providing a reliable solution for intelligent operation and maintenance of gearbox bearings.

Structural and process parameter optimization for rotary riveting of wheel hub bearing face teeth

Xiong, Wei — 2026-05-16T00:00:00Z

Journal of Vibroengineering, (in Press).
Wei Xiong, Yong-Kun Guo, Zhong-Di Deng, Hai-Bo Zhang, Shuo Lv
The face teeth of wheel hub bearing are manufactured using a two-step rotary riveting forming process. Controlling the forming force and improving the tooth profile fullness are critical, as these factors directly affect bearing performance. This study employs DEFORM finite element simulation combined with experimental verification to analyze the influence of structural and process parameters – including blank dimensions, pre-riveting state, feed rate, spindle rotation speed, and riveting inclination angle – on the forming force and tooth profile fullness. Optimal structural and process parameters are thereby determined. The results indicate that among blank structural parameters, wall thickness has the greatest impact on tooth profile fullness, followed by inner corner radius, with outer corner radius having the least effect. Optimized blank dimensions improve profile fullness and reduce forming force. The pre-riveting state significantly influences the tooth forming process, a fully riveted state reduces axial forming force by 24.9 % compared to a non-riveted state and markedly improves profile fullness. A smaller feed rate and higher spindle speed reduce forming force and improve profile fullness, but may cause flash at the tooth outer edge. Conversely, excessive feed rate and low spindle speed reduce profile fullness. Experimental verification shows that optimized process parameters increase tooth profile fullness by 4.6 % to 96.2 % and reduce forming force by 17.8 % compared to pre-optimization conditions, confirming the effectiveness of the parameter optimization.

Near-field seismic wave attenuation and local magnitude calibration based on controlled blasting experiments

Wang, Yu — 2026-05-16T00:00:00Z

Journal of Vibroengineering, (in Press).
Yu Wang, Junhao Qu, Ruifeng Liu, Zibo Wang, Qinying Wang, Kunpeng Shi, Qijie Zhou, Shiwen Xie
With the increasing depth and intensity of coal mining in China, non-natural seismic events occur more frequently, posing higher demands on mine safety and seismic monitoring. As a core parameter in earthquake monitoring and hazard assessment, precise determination of magnitude is essential for predicting and mitigating dynamic disasters. To address this, we conducted 22 controlled blasting experiments in the Weihai Port area of Shandong Province, using a combination of fixed and mobile seismic stations to systematically investigate the attenuation characteristics of near-field seismic waves and to establish a regional calibration function for local magnitude (ML). The calibration functions for both horizontal and vertical components were derived through least-squares fitting, from which the corresponding local magnitude determination formula was developed. Results from the 22 blasting events indicate good consistency of single-station magnitudes, with small deviations compared to the magnitudes determined by the Shandong Seismic Network, satisfying the required accuracy for magnitude estimation. Overall, this study establishes a calibration function applicable within 5 km of the Weihai blasting area, enhancing the consistency between magnitudes of blasting and natural earthquakes. The results provide a valuable reference for improving regional seismic monitoring systems and strengthening early warning capabilities for mine-related hazards.

Dynamic response and collapse mechanisms of transmission lines under downburst-induced wind–rain loads

Shao, Guodong — 2026-05-16T00:00:00Z

Journal of Vibroengineering, (in Press).
Guodong Shao, Chongyang Zhang, Yuanchao Jia, Mingxuan Zhu, Syed Hassan Farooq, Oryngozhin Yernaz, Zhengyu Ren, Siyao Zhang, Juncai Liu
As a localized high-intensity downdraft disaster, downbursts are a significant cause of wind-rain-induced damage to transmission lines. Their unique wind field characteristics make it difficult for existing design methods to comprehensively evaluate the resistance capacity of transmission lines. Current collapse analyses of transmission lines often fail to adequately consider the wind-rain field conditions during downbursts. Therefore, this study investigates the dynamic response and collapse mechanisms of transmission lines under downburst wind-rain conditions. First, a numerical simulation model is established to explore the distribution characteristics of wind and rain. A full-scale three-dimensional computational domain model is employed to simulate the wind-rain field, which is subsequently modified. The wind-rain velocity ratio is analyzed, and a fitting formula is proposed. Subsequently, combined distributed loads are applied to the transmission line to conduct parametric analyses of the dynamic response and investigate the collapse mechanisms. The results demonstrate that the computational domain model for simulating the wind-rain field is validated using relevant models, and the Vicroy model is modified for generating wind-rain loads. The horizontal velocity of raindrops does not synchronize with wind speed variations, and the proposed fitting formula for the wind-rain velocity ratio exhibits high accuracy. Downbursts significantly influence the dynamic response of the transmission tower-line system, with the most unfavorable wind attack angles, heights, rainfall intensities, and combined conditions identified. The collapse failure mode of the transmission line is characterized by initial damage and failure of diagonal members, leading to extensive structural collapse. The critical segments vary under different wind attack angles and reference heights, while rainfall has a minor impact on the collapse process. This study provides important technical insights for the wind-resistant design and safe operation of transmission lines.

Sensor placement method for crane structural health monitoring based on multi-level damage identification

Liu, Guansi — 2026-05-16T00:00:00Z

Journal of Vibroengineering, (in Press).
Guansi Liu, Hui Jin, Keqin Ding
Local damages such as microcracks and corrosion in crane steel structures often exhibit strong localization and weak mode shape perturbation characteristics. Especially when the damage scale is smaller than the modal wavelength, it cannot be simply treated as an overall damage issue for identification. In response to the diverse damage modes of crane structures and the need for dense sensor placement for local identification, a sensor optimization placement method based on multi-level damage feature fusion is proposed. Firstly, structural sub-regions are divided according to the main beam diaphragms, and sensors are arranged with boundary points as key measurement points. The displacement frequency response amplitude changes before and after damage are utilized to identify damage and eliminate insensitive measurement points to complete preliminary optimization. Secondly, a displacement frequency response amplitude change matrix is constructed, and the damage signal is enhanced through cross-frequency weighted superposition to form a damage identification vector, accurately locating the damage occurrence area. Furthermore, node-level correction is performed in candidate areas based on displacement flexibility difference values, and precise localization of damage points is achieved through priority sorting of flexibility differences. Simulation results show that under the condition where corrosion damage is set in units 20739, 20762, and 20785, the maximum point of the flexibility difference damage index is located in unit 20762, which coincides with the preset damage location, verifying the effectiveness of the hierarchical placement strategy from initial damage screening to precise localization.

Structural capacity and impact behavior of circular bridge piers strengthened by concrete jacketing

Cong, Xiaohui — 2026-05-16T00:00:00Z

Journal of Vibroengineering, (in Press).
Xiaohui Cong, Yunlong Zhang, Haixia Zhao
In response to the lack of clear calculation methods for the normal section bearing capacity of circular piers strengthened by concrete jacketing in current design codes, this paper derives a calculation formula applicable to the normal section bearing capacity of strengthened circular piers under unloading conditions. The derivation is based on the relevant provisions of the Specifications for Design of Highway Reinforced Concrete and Prestressed Concrete Bridges and Culverts, incorporating the plane-section assumption and ultimate state theory. Finite element verification shows that the theoretical values agree well with the simulation results, with a maximum error of –1.55 %, and the results are conservative, meeting engineering safety requirements. In terms of impact resistance, increasing the thickness of the strengthening layer significantly enhances the pier's stiffness, reduces the displacement peak, and shortens the dynamic response time, shifting the damage mode from global damage to locally controllable damage. Parameter analysis indicates that the diameter of the main reinforcement and the spacing of the stirrups have a limited effect on the displacement response but play a key role in energy dissipation capacity: increasing the main reinforcement diameter effectively improves the total energy dissipation, while reducing the stirrup spacing enhances the confinement effect on the core concrete and improves energy dissipation efficiency. Considering both economy and construction feasibility, it is recommended to prioritize larger diameter main reinforcement and control the stirrup spacing within the range of 10-15 cm in impact-resistant design to achieve an optimal balance between performance and cost. Combined with a bridge strengthening project in Changchun, this paper summarizes key technical points, forming a theoretically complete and practically verified technical system for pier strengthening.

Dynamics and vibration analysis of a vehicle with inner axle box based on flexible wheelsets

Ma, Yufeng — 2026-05-16T00:00:00Z

Journal of Vibroengineering, Vol. 28, Issue 4, 2026, p. 970-989.
Yufeng Ma, Yunhua Huang, Xu Hu
This study systematically investigates the dynamic performance and vibration characteristics of a metro vehicle equipped with an inner axle box bogie, with a focus on the effects of wheelset structural flexibility. A rigid-flexible coupled dynamics model is constructed. For computational efficiency, the flexible wheelset within it was developed using the finite element method and subsequently condensed via substructuring techniques. The model is integrated into multi-body dynamics software SIMPACK, incorporating non-linear suspension characteristics. Parametric analysis is conducted to evaluate vehicle dynamics under varying primary vertical stiffness and operating speeds, comparing rigid and flexible wheelset configurations in terms of straight-line ride comfort, non-linear critical speed, and curve negotiation safety. The influence of wheelset flexibility, evaluated through time- and frequency-domain analysis of axle-box vibration, is found to be subtle yet statistically relevant: it slightly reduces critical speed and amplifies lateral vibrations at high speeds without inducing resonance or exceeding safety thresholds. The rigid wheelset model is deemed sufficient for basic curve negotiation and vibration analysis, whereas the flexible model is recommended for critical speed and high-speed dynamics. These findings provide theoretical support for the design and optimisation of inner axle box bogies.

Nonsingular sliding mode control method for vibration of driving motor of ship rim propulsion device

Han, Lijun — 2026-05-16T00:00:00Z

Journal of Vibroengineering, (in Press).
Lijun Han, Qian Jiang
Owing to its unique structure, the driving motor of a ship’s rim propulsion device is subject to coupling effects from multiple physical fields. This makes it difficult for conventional control methods to effectively suppress vibrations caused by high-amplitude, complex harmonics, leading to poor speed and torque control performance. Therefore, a vibration suppression method based on disturbance observer combined with non singular sliding mode control is proposed. First, a disturbance observer is constructed to monitor motor torque in real time and accurately capture torque fluctuations induced by vibration. Secondly, design a non singular sliding mode controller to adaptively and quickly adjust the motor speed when vibration is detected. Finally, the quantum particle swarm algorithm, enhanced by the artificial bee colony algorithm, is used to optimize the controller parameters, thereby improving robustness and accuracy under multi-physics field coupling. The experimental results show that this method can accurately observe the motor torque and quickly stabilize the speed between 500 r/min-800 r/min under vibration state, with the smallest torque fluctuation amplitude. This result holds important scientific significance: it validates the effectiveness of nonsingular sliding mode control combined with intelligent optimization algorithms in decoupling multi-physics field interactions and suppressing complex electromagnetic excitation vibrations, offering a new control perspective for understanding motor dynamics under extreme operating conditions. In terms of application value, this method significantly enhances the dynamic response speed and steady-state accuracy of the driving motor, directly improving propulsion efficiency and maneuverability. It also effectively reduces fatigue wear on mechanical components, extends equipment life, and lowers operation and maintenance costs throughout the ship’s life cycle. In the future, we will explore integrating this control strategy with energy efficiency optimization for propulsion devices and investigate predictive vibration suppression methods based on digital twins to achieve smarter, more efficient health management of ship propulsion systems.

Hybrid smith predictor-neuroendocrine control for semi-active suspension of high-clearance agricultural sprayers

Chen, Congcong — 2026-05-16T00:00:00Z

Journal of Vibroengineering, (in Press).
Congcong Chen, Weiwei Song, Gang Li, Yuling Ye, Junfeng An
. In order to attain time-lag dynamic compensation control and decrease time-lag influence of high-clearance sprayer semi-active suspensions, a novel suspension intelligent controller based on improved Smith prediction neuroendocrine algorithm is proposed. Firstly, the time-lag semi-active suspension dynamic model of high-clearance sprayers is established, the neuroendocrine intelligent controller is designed combined with the hormone regulation mechanism in the organism. Then, by combining enhanced Smith prediction compensation controller with neuroendocrine intelligent controller, a kind of high-clearance sprayer semi-active suspension intelligent controller based on the improved Smith prediction neuroendocrine algorithm is developed. The research results show the proposed algorithm can significantly reduce body vertical acceleration (BVA) and tire dynamic load (TDL) indicators of high-clearance sprayer semi-active suspensions, effectively improve smoothness and road friendliness of sprayers, significantly enhance comprehensive performance, show strong adaptability and robustness under working conditions, and is very suitable for vibration control of the high-clearance sprayer semi-active suspension with high order time-varying, complex nonlinear and strong coupling.

A review of bridge resilience: assessment frameworks, intelligent algorithms, and future directions

Zhou, Jiuqing — 2026-05-16T00:00:00Z

Journal of Vibroengineering, (in Press).
Jiuqing Zhou, Mingyou Chen, Leifa Li, Guanghui Zhang, Daming Lin
Bridge resilience assessment enables bridges to maintain their structural integrity and functionality in the face of loads and disasters, improves their reliability and recovery capability, and ensures the smooth flow of transportation. This study focuses on the assessment methods and indicator selection for the seismic resilience of bridges, summarizing the latest research progress in this field. It reviews the development history of commonly used bridge resilience assessment methods and frequently used assessment methods, and comparatively analyzes the advantages and disadvantages of experimental research and numerical simulation. Several bridge resilience assessment frameworks and evaluation models are introduced, including those for single bridges as well as for entire bridge networks, to assist researchers in selecting appropriate models and frameworks based on actual conditions. The key indicators for the seismic resilience assessment of bridges are reviewed, including structural strength, deformation capacity, durability, and reparability. Finally, future directions for research are proposed. The research indicates that assessment methods for bridge resilience under the coupled effects of multiple hazards are not yet mature and require further investigation. The close integration of intelligent algorithms with bridge resilience assessment is an important future research direction. This review aims to further promote the research and application of seismic resilience assessment for bridges.

Operating status prediction method for gas storage compressors based on BiLSTM

Chen, Hua — 2026-05-16T00:00:00Z

Journal of Vibroengineering, (in Press).
Hua Chen, Liang Wang, Shuang Li, Xiaohan Wei
Compressors serve as fundamental power units in underground natural gas storage facilities and frequently need to accommodate wide-range operational condition adjustments. However, their inherent nonlinear characteristics, coupled with high-noise and uncertain operating environments, pose significant challenges to precise control and safe operation. To accurately predict compressor operational status and provide reliable decision-making information for operation and maintenance, this study proposes an operating-state prediction method for gas storage compressors based on vibration-sequence analysis and a Bidirectional Long Short-Term Memory (BiLSTM) network. Singular Spectrum Analysis (SSA) is first applied to decompose and reconstruct monitoring signals, extracting dominant trend components while suppressing background noise. A Convolutional Neural Network (CNN) is subsequently employed to capture multi-scale local features, after which the BiLSTM network learns temporal evolution patterns from the extracted features. The Adam optimizer is utilized to enhance training stability and prediction accuracy. Experimental validation using real monitoring data from gas storage compressors indicates that the proposed method achieves precise and reliable prediction of compressor operating states.

Erratum: Rolling bearing fault diagnosis under varying operating conditions using a convolutional-transformer multi-alignment approach

Wang, Yiying — 2026-05-19T00:00:00Z

Journal of Vibroengineering, (in Press).
Yiying Wang, Fulu Sui, Xiaoling Li, Xiaoxin Zhang, Mingxian Liu, Chen Liu, Jie Wu

Gearbox compound fault diagnosis using CEEMDAN feature extraction and a dual-attention multi-scale BiLSTM model

Wu, Lianxin — 2026-05-25T00:00:00Z

Journal of Vibroengineering, Vol. 28, Issue 4, 2026, p. 869-886.
Lianxin Wu, Xiaojie Sun
As a core component of mechanical transmission systems, the gearbox's operating state directly determines equipment reliability and industrial production safety. In actual working conditions, a single fault can easily evolve into a complex fault mode with multiple coupled faults. Traditional diagnostic methods face challenges such as insufficient feature extraction and low fault mode discrimination. To address this issue, an intelligent diagnostic model is proposed that integrates adaptive noise complete set empirical mode decomposition (CEEMDAN) feature extraction, multi-scale convolution, and a dual attention mechanism. First, CEEMDAN is used to decompose the vibration signal at multiple scales. After effective IMF filtering, time-domain, frequency-domain, fault-specific, and coupled interactive features are extracted to form a multi-dimensional feature set. Then, adaptive principal component analysis (PCA) is used to reduce the dimensionality to obtain a low-redundancy feature set. Subsequently, a diagnostic model containing multi-scale convolution, a bidirectional long short-term memory network (BiLSTM), and dual attention branches is constructed, and an improved loss function is combined to enhance the ability to distinguish complex fault features. Experimental results based on the Beijing Jiaotong University bogie gearbox bench dataset verify the effectiveness and robustness of the proposed method under complex fault modes, providing a reliable technical solution for gearbox fault diagnosis in industrial scenarios.

Structure reconstruction of anchor winch drum based on parameterized surrogate model with lightweight design

Zhou, Guangqian — 2026-05-25T00:00:00Z

Journal of Vibroengineering, Vol. 28, Issue 4, 2026, p. 956-969.
Guangqian Zhou, Lihong Guo, Xuehong Huang
To improve the operational stability of the anchor winch, parametric reconstruction of the winch drum structure was conducted based on surrogate model technology. With the stress peak value and natural frequency as constraint conditions, lightweight design of the overall structure was implemented, effectively achieving energy conservation and consumption reduction. A parametric model coupling strength and modal characteristics of the winch drum was established using the finite element method. Taking the stress peak value, first-order natural frequency, and mass as objective functions, a response surface was fitted according to the mapping relationship with discretized parameter dimensions, and error verification was conducted. An optimization mathematical model of the winch drum was constructed, and the sequential quadratic programming (SQP) algorithm was adopted to solve the extremum of the model under different constraint conditions. Research indicates that the proxy model exhibits high accuracy. Under conditions of 1 time and 1.5 times the peak stress, the weight of the anchor drum can be reduced by 17.4 % and 27.6 %, respectively, with the natural frequencies remaining no lower than the initial values. The research method can significantly improve the cost-effectiveness of mechanical design and achieve good energy-saving and consumption reducing effects.

Dynamic modeling and screening performance of a double-layer forced synchronous circular vibrating screen

Li, Hongxi — 2026-05-28T00:00:00Z

Journal of Vibroengineering, (in Press).
Hongxi Li, Enhui Zhou, Haishen Jiang, Ling Shen, Zixin Yin
The double-layer forced synchronous circular vibrating screen (DLFSCVS) is one of the most effective solutions for material screening. In this paper, a dynamic model was established to control the vibration of the screen, and the vibration characteristics of DLFSCVS are obtained by vibration experiment and parameter analysis. The classification performance of DLFSCVS was studied by EDEM, and the screening mechanism of DLFSCVS was revealed. The results show that the established dynamic model can describe the vibration of DLFSCVS well, and the maximum deviation between the experimental results and the theoretical results was within 4.78 %. The trajectory of the screen box is approximately circular. When the vibration frequency is 14 Hz, the acceleration amplitude of the screen box in the X and Y axis directions is 34.7 and 35.2 m/s2, respectively. With the increase of vibration frequency, the displacement amplitude of the screen box is basically unchanged, and the velocity and acceleration amplitude increase gradually. The results showed that when f= 14 Hz, the screening efficiency of the upper and lower screen plates up to 0.87 and 0.93, respectively.

Dynamic modeling and signal mapping of rotor displacement and velocity under rub-impact faults

Xu, Haishan — 2026-06-04T00:00:00Z

Journal of Vibroengineering, (in Press).
Haishan Xu, Hongchao Wang
Shaft vibration (displacement signal) and bearing vibration (velocity signal) are key indicators for evaluating the dynamic characteristics of the rotor and supporting bearing system, and they play a crucial role in the operational performance and safety of equipment. However, in practical applications, collecting shaft vibration or bearing vibration signals often encounters multiple challenges, primarily attributed to limitations in measurement technology, interference from faults, and variations in operating environments. In-depth investigation into the intrinsic correlation between shaft vibration and bearing vibration not only enables data supplementation to improve information completeness, but also offers more precise references for fault diagnosis and condition monitoring. Therefore, this study proposes a method based on homologous information fusion, aiming to explore the intrinsic correlation between shaft vibration and bearing vibration under rub-impact faults. The study first constructs a dynamic model under rub-impact fault condition, and then fuses homologous information using full vector spectrum technology to improve the accuracy of determining the relationship between shaft vibration and bearing vibration at different rotational speeds. Finally, the reliability of simulation results is validated through the establishment of a rotor experimental rig. Experimental results reveal that by mastering this complementary relationship, the operating health status of equipment can be inferred based on the variation tendencies of other critical parameters – even when a specific measured signal is unavailable – and corresponding maintenance and management strategies can thus be formulated.

A feature lightweight image coding method for fault diagnosis of hydraulic motor bearings: PVIE

Teng, Xiaomin — 2026-06-04T00:00:00Z

Journal of Vibroengineering, (in Press).
Xiaomin Teng, Huiying Xing, Wansheng Wang, Jing Li, Yunlin Ma
To address the critical challenge of low diagnostic accuracy in multistate bearing fault diagnosis caused by inefficient discriminative feature extraction under varying operating conditions, this paper proposes a novel Parameter-weighted Viridis Image Encoding (PVIE) method. Unlike conventional image encoding techniques (e.g., GADF, GASF, MTF, RP) that often suffer from high computational complexity and limited feature separability in complex scenarios, PVIE integrates Variational Mode Decomposition (VMD) with a newly designed Parameter-weighted Euler Difference Feature Extraction (PWEDFE) module. This module explicitly enhances the nonlinearity and periodicity of fault signatures, mapping them into lightweight 2D feature images via Viridis Feature Value Mapping (VFVM). Extensive experiments on two benchmark datasets demonstrate that PVIE achieves exceptional diagnostic accuracies of 99.92 % and 99.74 %, respectively. Compared to state-of-the-art encoding methods, PVIE improves average accuracy by 21.06 % to 39.78 % while reducing diagnostic time by 53.3 %, significantly outperforming existing approaches in both accuracy and efficiency. Furthermore, the method exhibits robust performance under strong noise interference and small-sample scenarios. These results confirm that PVIE offers a substantial advancement over current research by providing a more discriminative, lightweight, and robust solution for real-time industrial fault diagnosis.

Prediction and experimental validation of radial displacement in combined bearings for borehole trajectory control tools

Guo, Peng Gao — 2026-06-04T00:00:00Z

Journal of Vibroengineering, (in Press).
Peng Gao Guo, Lei Shi, Yan Fei Yu, Zhan Zhou, Dian Ren Mao
With the decreasing of easy-to-exploit oil and gas resources, complex structure wells put forward higher requirements for wellbore trajectory control accuracy. As the core supporting part of the guiding tool, the radial displacement of the combined bearing directly affects the deflection accuracy of the spindle. In order to reveal the internal correlation mechanism between the radial displacement of the combined bearing and the accuracy of the spindle deflection control, this paper combines the statically indeterminate beam theory and the parametric finite element method to establish a spindle deflection prediction model considering multi-factor coupling. The influence of radial load, rotational speed and inner and outer ring eccentricity on the radial displacement of the combined bearing is systematically studied. The results show that the radial displacement of the combined bearing increases significantly with the increase of radial load, rotational speed and eccentricity. Especially under heavy load conditions, the change of the contact logarithm of the rollers inside the bearing leads to a nonlinear turning of the stiffness characteristics, showing obvious adaptive bearing characteristics. The prototype experiment shows that the variation trend of the simulation and the measured data is highly consistent under the condition of 30 r/min, and the error is controlled within 10 %-15 %, which verifies the accuracy of the model. This study not only quantifies the influence of key parameters on bearing displacement, but also provides a theoretical basis and parameter selection criteria for structural optimization design and accuracy improvement of rotary steering drilling tool combined bearings.

A multi-scale CNN-transformer hybrid network with parallel attention mechanism and local linear unit for cross-condition fault diagnosis

Cai, E — 2026-06-08T00:00:00Z

Journal of Vibroengineering, Vol. 28, Issue 4, 2026, p. 836-852.
E Cai, Yangyang Li
Domain shift caused by variations in rotational speed and load under cross-condition scenarios may distort the statistical distribution and feature representation of vibration signals, posing a challenge to the reliable deployment of intelligent fault diagnosis systems for rotating machinery. Existing multi-scale CNN–Transformer hybrid architectures generally do not explicitly consider the influence of such condition-induced domain shifts. To address this issue, a multi-scale CNN-Transformer hybrid method integrating a Parallel Attention Mechanism (PAM) and a Local Linear Unit (LLU) is proposed to enhance the stability of feature representations under varying operating conditions. In the proposed method, raw vibration signals are directly used as inputs, and a multi-scale CNN module is employed to capture transient impact features across multiple temporal scales. The PAM then adjusts feature responses in both channel and temporal dimensions through parallel attention branches, enabling adaptive feature reweighting to alleviate condition-induced statistical variations. Furthermore, a Transformer encoder embedded with LLU is adopted as the backbone to incorporate local structural modeling through depthwise separable convolution while preserving the global dependency modeling capability of self-attention. Experiments on three benchmark datasets (PU, PHM09, and CWRU) show that the proposed method achieves average accuracies of 80.15 %, 93.96 %, and 98.17 %, respectively, under unseen operating conditions, consistently outperforming several representative comparative methods. Additional ablation studies further verify the contribution of PAM and LLU to robust feature representation learning. Moreover, noise robustness experiments under multiple signal-to-noise ratio (SNR) levels demonstrate that the proposed method maintains stable performance under moderate noise conditions, highlighting its practical reliability in realistic industrial environments. Our code is available at https://github.com/caie8201/Multi-Scale-CNN-Transformer-Hybrid-Network.

Vibration resistance analysis of steel wire reinforced hoses in automotive fuel delivery systems

Wang, Yonggang — 2026-06-14T00:00:00Z

Journal of Vibroengineering, (in Press).
Yonggang Wang
Mechanical deformation and fatigue fracture of the steel wire layer are the main damage modes of wire reinforced hoses in automotive fuel delivery systems. To investigate the anti-vibration performance of steel wire reinforced hoses, a finite element model covering prestressed modal calculation, harmonic response analysis and random vibration analysis was constructed based on the modal superposition method. Parametric comparative analyses were carried out respectively under varied conditions of hose length (200-320 mm), wall thickness (0.75-0.9 mm) and fuel delivery pressure (0-12 MPa), and the influence laws of each parameter on the anti-vibration performance of steel wire reinforced hoses were obtained. The study revealed that the first six natural frequencies of the model ranged from 66.311 Hz to 108.877 Hz, which were highly overlapped with the energy-concentrated frequency band of 0-100 Hz in the power spectral density (PSD) of road surface excitation. Parametric analyses showed that low-frequency resonance stress could be reduced by lengthening the hose, while stress concentration at the end would be intensified. The vibration characteristics and fatigue damage mechanism of steel wire reinforced hoses under actual service conditions were clarified, which could provide reliable theoretical basis and technical support for the anti-vibration design and parameter optimization of flexible pipelines in automotive fuel systems.

Non-stationary noise suppression in low voltage power line carrier channel based on CNN-LSTM hybrid model impedance matching algorithm

Zhu, Chunshan — 2026-06-20T00:00:00Z

Journal of Vibroengineering, (in Press).
Chunshan Zhu, Qinglong Wang, Yunga A, Xueqi Shi, Wenjiao Lu
Due to non-stationary noise, the low-voltage power line communication (LPCC) encounters significant challenges in smart grid applications. Conventional denoising techniques, such as wavelet thresholding and adaptive filtering, exhibit limited performance in complex industrial environments, while emerging deep learning models often suffer from insufficient real-time capability. In response to the noise characteristics of low-voltage power line channels and the limitations of traditional impedance matching algorithms, we propose a hybrid CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory Network) architecture. A dual-branch feature fusion mechanism is introduced, which employs parallel processing of time-frequency features via STFT+WVD (Short-Time Fourier Transform+Wigner-Ville Distribution) to enhance noise identification accuracy. A dynamic impedance matching module, optimized in real time using a deep reinforcement learning (DRL)-based gradient descent algorithm, is developed to overcome the poor adaptability of conventional fixed-parameter approaches. Furthermore, a joint noise suppression and signal reconstruction framework is designed to effectively preserve useful signal components while suppressing noise. The experimental results verify that the proposed model achieves an SNR (Signal-to-Noise Ratio) improvement of up to 18.2 dB, outperforming the conventional DnCNN (Denoising Convolutional Neural Network) method by 16.7 %. Under harmonic interference conditions (THD = 15 %), the waveform distortion rate is only 2.1 %, with latency optimized to 9.2 ms, meeting real-time requirements. By incorporating multi-scale feature fusion and dynamic gating mechanisms, the model effectively mitigates mixed interference composed of switching impulse noise and additive white Gaussian noise, which will offer a viable solution for enhancing the reliability of LPCC systems.

Multi-scale modeling of blasting-induced fracture in polycrystalline granite with grain boundary effects

Zhou, Shudong — 2026-06-24T00:00:00Z

Journal of Vibroengineering, (in Press).
Shudong Zhou, Wenxuan Zhang, Weijia Li, Jian Wang
This study presents a multi-scale finite-discrete element modeling approach for blasting-induced fracture in polycrystalline granite, with explicit consideration of grain boundary effects, to accurately reproduce the mesoscopic heterogeneity and dynamic fracture responses of granite under ultra-small diameter borehole blasting. A Voronoi-based polycrystalline geometric model is established via Neper software to characterize mineral distribution and microstructural anisotropy. Cohesive elements are simultaneously inserted into intragranular and grain boundary regions in Abaqus with differentiated mechanical parameters, and the Jones-Wilkins-Lee (JWL) equation of state is used to apply the dynamic blasting load of PETN explosive. Numerical results agree well with laboratory blasting tests, showing typical failure zones including a crushing zone, a radial fracture zone, and a circumferential tensile fracture zone. The polycrystalline model exhibits prominent non-uniformity and dynamic anisotropy in crack propagation, which is strongly governed by grain morphology and grain boundary properties. Grain boundary strength is identified as a key factor controlling the dynamic fracture mode: with decreasing grain boundary strength, the failure pattern gradually shifts from transgranular fracture to mixed fracture and then to intergranular fracture. Under moderate grain boundary strength, blasting energy is first transmitted inside grains and then released and dissipated at weak grain boundaries, forming a chain-type dynamic failure mechanism: intragranular energy transfer to grain boundary fracture. The proposed method reveals the micro-dynamic evolution mechanism of granite damage under ultra-small diameter blasting and provides a reliable theoretical basis for blasting parameter optimization, rock fragmentation control, and blast-induced vibration prediction in precision rock blasting engineering.

Study on mechanical response of X80 pipeline under strike-slip fault action

Huang, Shuai — 2026-06-29T00:00:00Z

Journal of Vibroengineering, (in Press).
Shuai Huang, Xinyue Zhang, Zhigang Tao, Zhonghao Xiong, Junbiao He, Pengcheng Pei, Tingting Liu, Jingwei Liu, Liwei Xiu
Strike-slip fault misalignment can lead to stress concentration, fissure development, and slip instability in overlying soil layers, triggering geologic hazards such as surface displacement, landslides, and rock formation deformation, causing severe damage to buried infrastructure. In cross-regional energy transmission projects, stress concentration and deformation failure induced by fault movement in cross-fault buried pipelines critically threaten the safe transmission of oil and gas. As the scale of oil and gas pipeline construction in China expands, pipelines inevitably cross fault zones, increasing rupture risk and necessitating investigation of fault misalignment mechanisms and pipeline mechanical response. This study employs three-dimensional nonlinear finite element analysis to investigate the mechanical response of X80 buried pipelines under strike-slip fault action. A comprehensive pipe-soil interaction model incorporating the Ramberg-Osgood constitutive relationship and Mohr-Coulomb soil plasticity is developed using ABAQUS software. The research systematically examines the effects of fault displacement (0.5-2.5 m), pipeline wall thickness (18.4-32.1 mm), internal pressure (0-12 MPa), and pipe-soil friction coefficient (0.3-0.6) on pipeline stress and strain responses. Key findings include: (1) a characteristic bimodal von Mises stress distribution occurs at approximately ±20 m from the fault plane, with secondary peaks at ±10 m; (2) stress and strain increase nonlinearly with fault displacement, with diminishing increments as the material enters the plastic regime; (3) increasing wall thickness from 18.4 mm to 32.1 mm reduces maximum tensile strain by approximately 50 %; (4) internal pressure and friction coefficient effects are significant only below the 2 m fault displacement threshold. The results provide quantitative guidelines for wall thickness selection and protective measure implementation for cross-fault pipeline design, ensuring safe operation during service life.

Dynamic characteristics of aircraft gear transmission systems under overload level-flight conditions

Zhou, Renhongyi — 2026-07-06T00:00:00Z

Journal of Vibroengineering, (in Press).
Renhongyi Zhou, Aiqiang Zhang, Pan Shen, Yichen Liu
As a crucial component in aircraft power transmission, gear transmission systems are subjected to time-varying additional inertial loads in a non-inertial environment during aircraft maneuvers. Current dynamic analyses of these systems mostly depend on the inertial coordinate system, which assumes the gearbox is fixed to the ground and ignores the extra effects of base motion. Notably, existing models often use a rigid-flexible coupling approach – treating only key components like shafts as flexible while assuming others such as casings and gear teeth as rigid – which may deviate from the actual dynamic behavior of gear transmissions under maneuvering conditions. To address this limitation, this study establishes a full-flexible coupled multibody dynamics model for gear transmissions under overload level-flight maneuvers. By varying maneuvering acceleration magnitudes, the mechanisms by which maneuvering acceleration affects internal excitation and force characteristics in the system were explored. Results show that maneuvering acceleration induces shaft deformation, causing time-varying fluctuations in the center distance between meshing gears. This further leads to changes in meshing stiffness, transmission error, and tooth backlash. Correspondingly, bearing support force, gear meshing force, and Hertzian contact dynamic stress vary with maneuvering overload-especially the bearing force aligned with the overload direction, which is significantly affected by acceleration. This finding provides a critical theoretical basis for the structural design and dynamic optimization of high-maneuverability aircraft gear transmissions.