Article
Computer Science, Artificial Intelligence
Anderson Ara, Mateus Maia, Francisco Louzada, Samuel Macedo
Summary: This paper proposes a new ensemble method called regression random machines for support vector regression. By using a random mixture of kernel functions and a properly bagging ensemble, this method eliminates the need to choose the best kernel function during the tuning process and achieves good predictive performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Industrial
Kai Cheng, Zhenzhou Lu
Summary: This paper proposes a structural reliability analysis method based on Bayesian support vector regression (SVR) model, which features point-wise probabilistic prediction while keeping the structural risk minimization principle. The method determines the optimal hyperparameters by maximizing Bayesian model evidence and presents two active learning algorithms based on the SVR model to estimate large and small failure probabilities of complex structures. Four benchmark examples are used to validate the performance of the proposed method.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Review
Engineering, Industrial
Atin Roy, Subrata Chakraborty
Summary: Support vector machine (SVM) is a powerful machine learning technique widely used in structural reliability analysis (SRA). This article provides a comprehensive review of various SVM approaches in SRA applications, including classification and regression algorithms. The article also discusses advanced variants of SVM and hyperparameter tuning algorithms. The review highlights the excellent capability of SVM in handling high-dimensional problems with relatively fewer training data in SRA applications.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Automation & Control Systems
Jun Ma, Zhaosheng Teng, Qiu Tang, Wei Qiu, Yingying Yang, Junfeng Duan
Summary: This paper presents an improved local outlier factor (ILOF) method to detect potential outliers, and an optimized distance function and adaptive threshold constraint method are used to improve the outlier detection performance of ILOF. Additionally, a kernel support vector regression (KSVR) method is proposed to fuse measurement error and multiple extreme environmental stresses. The evaluation framework combining ILOF and KSVR demonstrates higher assessment performance compared to other methods.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Mechanics
Steffen Funk, Ammar Airoud Basmaji, Udo Nackenhorst
Summary: This work presents a global surrogate modelling method for mechanical systems with elasto-plastic material behavior based on support vector regression (SVR). The study demonstrates the ability of SVR to handle discontinuous and high non-smooth outputs, and compares the performance of different kernel functions through one-dimensional and two-dimensional elasto-plastic cases. The computational cost of SVR is reduced by using anisotropic training grid, and the accuracy is improved by smoothing the response surface based on linear regression.
ARCHIVE OF APPLIED MECHANICS
(2023)
Article
Engineering, Mechanical
Yongbo Peng, Tong Zhou, Jie Li
Summary: An improved scheme of probability density evolution method (PDEM) is presented to tackle high-dimensional structural reliability analysis challenges, using the KPCA-GPR model and an active learning-based sampling strategy, achieving significant computational cost savings and accuracy enhancement in numerical examples.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Multidisciplinary
Jinsheng Wang, Chenfeng Li, Guoji Xu, Yongle Li, Ahsan Kareem
Summary: An adaptive algorithm based on the Bayesian SVR model (ABSVR) is proposed in this study, which combines new learning functions, distance constraint terms, and adaptive sampling region schemes to improve efficiency and reliability.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Geosciences, Multidisciplinary
Mohammad Najafzadeh, Saeid Niazmardi
Summary: This paper introduces a novel Multiple-Kernel Support Vector Regression (MKSVR) algorithm for estimating hard-to-measure water quality parameters, utilizing the Particle Swarm Optimization (PSO) algorithm to solve the optimization problem. Results show that MKSVR provides a more accurate prediction compared to SVR and RFR algorithms.
NATURAL RESOURCES RESEARCH
(2021)
Article
Engineering, Multidisciplinary
Behrooz Keshtegar, Mohamed El Amine Ben Seghier, Enrico Zio, Jose A. F. O. Correia, Shun-Peng Zhu, Nguyen-Thoi Trung
Summary: The paper presents a novel hybrid framework SVR-CFORM for reliability analysis of complex systems, which combines CFORM and SVR techniques to improve efficiency and robustness.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Mahdi Roozbeh, Arta Rouhi, Nur Anisah Mohamed, Fatemeh Jahadi
Summary: Classical regression approaches are not suitable for analyzing high-dimensional datasets with more explanatory variables than observations, as the results can be misleading. In this study, we propose using modern techniques like support vector regression, symmetry functional regression, ridge, and lasso regression methods to analyze such data. We introduce a generalized support vector regression approach that improves the performance of support vector regression by accurately estimating the penalty parameter using cross-validation. We evaluate the efficiency of the proposed estimators based on three criteria and apply them to real and simulated high-dimensional datasets.
Article
Computer Science, Information Systems
Xiaojin Xie, Kangyang Luo, Guoqiang Wang
Summary: This paper proposes a new multi-kernel learning ensemble algorithm called Ada-L1MKL-WSVR, which extends the concepts of multi-kernel learning and weighted support vector regression. The algorithm adaptively selects optimal base models and their parameters by adding the L-1 norm of the weights of the combined kernel function to the objective function of WSVR. Furthermore, an integrated learning framework based on AdaBoost is proposed, which incorporates the FISTA algorithm into AdaBoost and outputs an ensemble regression function. Experimental results show that Ada-L1MKL-WSVR significantly improves the prediction performance on multiple datasets.
Article
Biochemical Research Methods
Bingxing An, Mang Liang, Tianpeng Chang, Xinghai Duan, Lili Du, Lingyang Xu, Lupei Zhang, Xue Gao, Junya Li, Huijiang Gao
Summary: The study introduced a novel cosine kernel-based KRR model, KCRR, for genomic prediction (GP) in breeding programs. KCRR showed stable performance across multiple species, suggesting its potential for diverse genetic architectures. Additionally, a modified genomic similarity matrix called Cosine similarity matrix (CS matrix) was defined, which significantly improved computing efficiency without compromising prediction accuracy when compared to traditional methods like GBLUP. This research presents a promising strategy for future genomic prediction.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Environmental Sciences
Mir Jafar Sadegh Safari, Shervin Rahimzadeh Arashloo, Babak Vaheddoost
Summary: Multiple kernel fusion (MKF) combines multiple sources of information to improve performance. This study applies MKF in hydrological modeling to simulate lake water depth and demonstrates improved predictive performance compared to other models.
ENVIRONMENTAL RESEARCH
(2023)
Article
Materials Science, Multidisciplinary
Xiaoke Li, Qianlong Jiang, Yu Long, Zhenzhong Chen, Wenbo Zhao, Wuyi Ming, Yang Cao, Jun Ma
Summary: In this paper, a structure parameter optimization model was established to reduce the wear and damage of the chute caused by long-term impact of coke. The ensemble of support vector regression (E-SVR) with different kernel functions was developed to replace the implicit relationship between the conveying speed, impact force, and structure parameters. Numerical examples were used to verify the effectiveness of the E-SVR model. After optimization, the maximum impact force was reduced by 17.07% and the maximum conveying speed was reduced by 6.59%, which still falls within the specified range. Therefore, the feasibility of the optimization results and the effectiveness of the E-SVR surrogate model were verified.
Article
Computer Science, Information Systems
Zichen Zhang, Shifei Ding, Yuting Sun
Summary: This paper introduces a new method called multiple birth support vector regression (MBSVR), which constructs the regressor from multiple hyperplanes obtained by solving small quadratic programming problems, aiming for faster computation and better fitting precision.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
Chengxin Feng, Matthias Faes, Matteo Broggi, Chao Dang, Jiashu Yang, Zhibao Zheng, Michael Beer
Summary: This paper proposes a new method, named the interval field limit equilibrium method (IFLEM), for assessing the stability of slope in the presence of the interval field. The method involves using a modified exponential function to characterize the spatial uncertainty of the interval field and employing Karhunen-Loeve-like decomposition to generate the interval field. The proposed method is verified using three numerical examples and demonstrates reasonable accuracy and efficiency, making it potentially applicable to a number of geotechnical systems.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Engineering, Multidisciplinary
Masaru Kitahara, Chao Dang, Michael Beer
Summary: This paper proposes a Bayesian updating approach called parallel Bayesian optimization and quadrature (PBOQ). It applies Gaussian process priors and explores a constant c in BUS through parallel infill sampling strategy. The proposed approach effectively reduces computational burden of model updating by leveraging prior knowledge and parallel computing. Numerical examples are used to demonstrate its potential benefits and advocate a coherent Bayesian fashion for BUS analysis.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Mechanical
Chen Ding, Chao Dang, Marcos A. Valdebenito, Matthias G. R. Faes, Matteo Broggi, Michael Beer
Summary: This paper proposes a novel approach to estimate the first-passage probability of high-dimensional nonlinear stochastic dynamic systems. The approach captures the extreme value distribution of the system response using the concepts of fractional moment and mixture distribution, and efficiently computes the fractional moments using a parallel adaptive sampling scheme. By fitting a set of fractional moments, the desired extreme value distribution can be recovered, and the first-passage probabilities under different thresholds can be obtained directly.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
P. Ni, V. C. Fragkoulis, F. Kong, I. P. Mitseas, M. Beer
Summary: This paper proposes a new technique for determining the response of multi-degree-of-freedom nonlinear systems with singular parameter matrices subject to combined deterministic and non-stationary stochastic excitation. The system response is decomposed into deterministic and stochastic components, corresponding to the two components of the excitation. Two sets of differential equations are formulated and solved simultaneously to compute the system response. The efficiency of the proposed technique is demonstrated by numerical examples involving a vibration energy harvesting device and a structural nonlinear system.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Yongchao Zhang, Zhaohui Ren, Ke Feng, Kun Yu, Michael Beer, Zheng Liu
Summary: Cross-domain machinery fault diagnosis aims to transfer enriched diagnosis knowledge from a labeled source domain to a new unlabeled target domain. Existing methods often assume prior knowledge of fault modes in the target domain, which is rare in engineering practice. This study proposes a source-free domain adaptation method that can handle cross-domain fault diagnosis scenarios without source data and explicit assumptions about target fault modes.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Marco Behrendt, Matthias G. R. Faes, Marcos A. Valdebenito, Michael Beer
Summary: In engineering, the modelling of environmental processes is essential for designing structures safely and determining the reliability of existing structures. This work focuses on situations where data is limited and it is not feasible to derive reliable statistics. The proposed approach uses a radial basis function network to generate basis functions that enclose the data, resulting in an interval-based power spectral density (PSD) function. The applicability of this imprecise PSD model is demonstrated with recorded earthquake ground motions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Industrial
Yang Zhang, Jun Xu, Michael Beer
Summary: This paper proposes a single-loop approach for time-variant reliability evaluation based on a decoupling strategy and probability distribution reconstruction. The proposed method allows capturing the reliability at a specified time instant by performing time-invariant reliability analysis only once. The method employs the expansion optimal linear estimation, decoupling strategy, Box-Cox transformation, and maximum entropy method to derive the probability distribution of the equivalent extreme value limit state function and compute the time-variant failure probability efficiently.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Civil
Diqi Zeng, Hao Zhang, Hongzhe Dai, Michael Beer
Summary: Risk assessment of spatially distributed infrastructure systems under natural hazards should consider the performance of individual components as stochastically correlated due to similarities in engineering practices. This study addresses the scalability issue of risk analysis by developing an interpolation technique that accurately evaluates damages of sampled components and interpolates damages of remaining components. The method is applicable to both linear systems and systems with complex connectivity. Two examples demonstrate its effectiveness in cyclone loss assessment and connectivity analysis of infrastructure systems.
Article
Engineering, Multidisciplinary
Chao Dang, Marcos A. Valdebenito, Jingwen Song, Pengfei Wei, Michael Beer
Summary: This paper introduces an innovative method called partially Bayesian active learning line sampling (PBAL-LS) for assessing small failure probabilities. The problem of evaluating the failure probability integral in the line sampling method is treated as a Bayesian inference problem, allowing for the incorporation of prior knowledge and modeling of discretization error. The paper proposes a learning function and a stopping criterion based on the posterior statistics of the failure probability, and an efficient algorithm is designed to implement the PBAL-LS method.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Industrial
Kun Zhang, Ning Chen, Jian Liu, Shaohui Yin, Michael Beer
Summary: This paper proposes an efficient meta-model-based method for uncertainty propagation problems involving non-parameterized probability-boxes. The method utilizes the Kriging meta-model to establish the mapping relationship between the non-parameterized P-box variables and the system response. Interval analysis is then performed using the constructed Kriging model, and the cumulative distribution function of the response function is obtained using interval Monte Carlo. The proposed method demonstrates high accuracy and efficiency in handling nonlinearity, high-dimensional, and complex engineering problems.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Civil
Yan Shi, Hong-Zhong Huang, Yu Liu, Michael Beer
Summary: This study presents an adaptive decoupled robust design optimization method based on the Kriging surrogate model, which transforms the nested double-loop estimation process into a traditional deterministic optimization procedure, reducing computational costs. A novel estimation expression for the performance standard deviation is established to reflect uncertainties in both prediction and performance mean simultaneously.
Article
Engineering, Industrial
Danko J. Jerez, M. Chwala, Hector A. Jensen, Michael Beer
Summary: This paper proposes a framework for designing optimal borehole configurations for shallow foundation systems under undrained soil conditions. It minimizes the standard deviations of the bearing capacity to ensure performance. The method adopts a random failure mechanism for evaluating random bearing capacity and provides sensitivity information of the selected performance measure.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Mechanical
Xu-Yang Cao, De-Cheng Feng, Michael Beer
Summary: With the development of performance-based earthquake engineering, the risk-informed assessment framework has gained recognition worldwide, particularly the probability seismic fragility analysis. Researchers are exploring non-parametric approaches to express intrinsic fragility without distribution assumptions, while also considering calculation efficiency and non-stationary stochastic responses. This paper proposes a kernel density estimation-based non-parametric cloud approach for efficient seismic fragility estimation and demonstrates its effectiveness through an application example. The findings provide insights for the development of non-parametric seismic fragility approaches.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Jun Lai, Kai Wang, Jingmang Xu, Ping Wang, Rong Chen, Shuguo Wang, Michael Beer
Summary: This paper addresses the issue of derailment risk in classification yards when trains pass through railway turnouts. It proposes a failure probability assessment approach that integrates intuitionistic fuzzy fault tree analysis and Noisy or gate Bayesian network to quantify the derailment risk. The method can handle imprecise, incomplete, and vague information on the train-turnout system components. Testing at a classification yard in China demonstrates that the method can efficiently evaluate the risk and identify key factors. Measures such as optimizing turnout alignment, controlling impact between wagons, lubricating the rails, and regularly inspecting the turnout geometries can be implemented to reduce the risk and prevent secondary damage and injuries.
ENGINEERING FAILURE ANALYSIS
(2023)
Article
Engineering, Multidisciplinary
Fangqi Hong, Pengfei Wei, Jingwen Song, Marcos A. Valdebenito, Matthias G. R. Faes, Michael Beer
Summary: Uncertainty quantification is crucial for reliability-oriented analysis and design of engineering structures. Three groups of mathematical models have been developed for different forms of uncertainties: probability models, imprecise probability models, and non-probabilistic models. Propagating these models through expensive simulators to quantify output uncertainties is a challenging task. Collaborative and Adaptive Bayesian Optimization (CABO) has been improved to handle all three categories of uncertainty models and to bound various probabilistic measures of the output.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Mechanical
Xuanen Kan, Yanjun Lu, Fan Zhang, Weipeng Hu
Summary: A blade disk system is crucial for the energy conversion efficiency of turbomachinery, but differences between blades can result in localized vibration. This study develops an approximate symplectic method to simulate vibration localization in a mistuned bladed disk system and reveals the influences of initial positive pressure, contact angle, and surface roughness on the strength of vibration localization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zimeng Liu, Cheng Chang, Haodong Hu, Hui Ma, Kaigang Yuan, Xin Li, Xiaojian Zhao, Zhike Peng
Summary: Considering the calculation efficiency and accuracy of meshing characteristics of gear pair with tooth root crack fault, a parametric model of cracked spur gear is established by simplifying the crack propagation path. The LTCA method is used to calculate the time-varying meshing stiffness and transmission error, and the results are verified by finite element method. The study also proposes a crack area share index to measure the degree of crack fault and determines the application range of simplified crack propagation path.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Rongjian Sun, Conggan Ma, Nic Zhang, Chuyo Kaku, Yu Zhang, Qirui Hou
Summary: This paper proposes a novel forward calculation method (FCM) for calculating anisotropic material parameters (AMPs) of the motor stator assembly, considering structural discontinuities and composite material properties. The method is based on multi-scale theory and decouples the multi-scale equations to describe the equivalence and equivalence preconditions of AMPs of two scale models. The effectiveness of this method is verified by modal experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Zhang, Jiangcen Ke
Summary: This research introduces an intelligent scheduling system framework to optimize the ship lock schedule of the Three Gorges Hub. By analyzing navigational rules, operational characteristics, and existing problems, a mixed-integer nonlinear programming model is formulated with multiple objectives and constraints, and a hybrid intelligent algorithm is constructed for optimization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Jingjing He, Xizhong Wu, Xuefei Guan
Summary: A sensitivity and reliability enhanced ultrasonic method has been developed in this study to monitor and predict stress loss in pre-stressed multi-layer structures. The method leverages the potential breathing effect of porous cushion materials in the structures to increase the sensitivity of the signal feature to stress loss. Experimental investigations show that the proposed method offers improved accuracy, reliability, and sensitivity to stress change.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Benyamin Hosseiny, Jalal Amini, Hossein Aghababaei
Summary: This paper presents a method for monitoring sub-second or sub-minute displacements using GBSAR signals, which employs spectral estimation to achieve multi-dimensional target detection. It improves the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xianze Li, Hao Su, Ling Xiang, Qingtao Yao, Aijun Hu
Summary: This paper proposes a novel method for bearing fault identification, which can accurately identify faults with few samples under complex working conditions. The method is based on a Transformer meta-learning model, and the final result is determined by the weighted voting of multiple models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaomeng Li, Yi Wang, Guangyao Zhang, Baoping Tang, Yi Qin
Summary: Inspired by chaos fractal theory and slowly varying damage dynamics theory, this paper proposes a new health monitoring indicator for vibration signals of rotating machinery, which can effectively monitor the mechanical condition under both cyclo-stationary and variable operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Wang, Songye Zhu
Summary: This paper extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed, and different latching control strategies are tested and evaluated. The latching control can optimize the phase lag between control force and structural response, and provide an innovative solution to improve damper effectiveness and develop adaptive semi-active dampers.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Menghao Ping, Xinyu Jia, Costas Papadimitriou, Xu Han, Chao Jiang, Wang-Ji Yan
Summary: Identification of non-Gaussian processes is a challenging task in engineering problems. This article presents an improved orthogonal series expansion method to convert the identification of non-Gaussian processes into a finite number of non-Gaussian coefficients. The uncertainty of these coefficients is quantified using polynomial chaos expansion. The proposed method is applicable to both stationary and nonstationary non-Gaussian processes and has been validated through simulated data and real-world applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Lei Li, Wei Yang, Dongfa Li, Jianxin Han, Wenming Zhang
Summary: The frequency locking phenomenon induced by modal coupling can effectively overcome the dependence of peak frequency on driving strength in nonlinear resonant systems and improve the stability of peak frequency. This study proposes the double frequencies locking phenomenon in a three degrees of freedom (3-DOF) magnetic coupled resonant system driven by piezoelectricity. Experimental and theoretical investigations confirm the occurrence of first frequency locking and the subsequent switching to second frequency locking with the increase of driving force. Furthermore, a mass sensing scheme for double analytes is proposed based on the double frequencies locking phenomenon.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Kai Ma, Jingtao Du, Yang Liu, Ximing Chen
Summary: This study explores the feasibility of using nonlinear energy sinks (NES) as replacements for traditional linear tuned mass dampers (TMD) in practical engineering applications, specifically in diesel engine crankshafts. The results show that NES provides better vibration attenuation for the crankshaft compared to TMD under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Wentao Xu, Li Cheng, Shuaihao Lei, Lei Yu, Weixuan Jiao
Summary: In this study, a high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were used to conduct research on cavitation signals of mixed-flow pumps. By analyzing the water pressure pulsation signal, it was found that the power spectrum density method is more sensitive and capable of extracting characteristics compared to traditional time-frequency domain analysis. This has significant implications for the identification and prevention of cavitation in mixed-flow pump machinery.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaodong Chen, Kang Tai, Huifeng Tan, Zhimin Xie
Summary: This paper addresses the issue of parasitic motion in microgripper jaws and its impact on clamping accuracy, and proposes a symmetrically stressed parallelogram mechanism as a solution. Through mechanical modeling and experimental validation, the effectiveness of this method is demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zhifeng Shi, Gang Zhang, Jing Liu, Xinbin Li, Yajun Xu, Changfeng Yan
Summary: This study provides useful guidance for early bearing fault detection and diagnosis by investigating the effects of crack inclination and propagation direction on the vibration characteristics of bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
