Article
Statistics & Probability
Xiaotian Zheng, Athanasios Kottas, Bruno Sanso
Summary: The study introduces a framework for constructing stationary MTD models that extend beyond linear, Gaussian dynamics. Conditions for first-order strict stationarity are explored, with inference and prediction developed under the Bayesian framework with structured priors for mixture weights. Model properties are investigated analytically and via synthetic data examples, with real data applications illustrating Poisson and Lomax examples.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2022)
Article
Biochemical Research Methods
Zachary R. McCaw, Hugues Aschard, Hanna Julienne
Summary: This study introduces a Gaussian mixture model for dealing with missing data and develops an R package for handling such data. The results indicate that this model is more effective in recovering true cluster assignments and provides accurate assessment of cluster assignment uncertainty.
BMC BIOINFORMATICS
(2022)
Article
Engineering, Mechanical
Xiao-Wei Liu, Da-Gang Lu
Summary: There are two main challenges in fatigue load probability modeling. Firstly, it is difficult to measure the estimation errors, especially in the tails with low-probability and high-stress levels. Secondly, the component number of the mixture model cannot be observed. To address these challenges, this research introduces the hierarchical Bayesian mixture model and the Dirichlet process prior. A relative error measure is proposed to reveal the errors of the density tails. The results of an illustrative example show significant relative errors in the tails and a discrete distribution of the mixture number.
INTERNATIONAL JOURNAL OF FATIGUE
(2023)
Review
Engineering, Aerospace
Ronghui Zheng, Guoping Chen, Huaihai Chen
Summary: This paper reviews various methods for controlling stationary non-Gaussian random vibration, discussing how to control time and frequency domain characteristics in non-Gaussian random vibration tests and generate a one frame stationary non-Gaussian random signal.
CHINESE JOURNAL OF AERONAUTICS
(2021)
Article
Engineering, Chemical
Hanwen Zhang, Weiwei Fan, Houze Guo, Chunjie Yang
Summary: This paper proposes a dynamic stationary subspace analysis method for monitoring blast furnace ironmaking processes. The method analyzes the dynamic relationship of the data by introducing a sliding time window and uses a Gaussian mixture model to handle the non-Gaussian characteristics of the data. Experimental results demonstrate the effectiveness of this method in dealing with non-stationary and non-Gaussian dynamic processes in blast furnace ironmaking.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2023)
Article
Agronomy
Yanchun Yao, Xiaoke Li, Zihan Yang, Liang Li, Duanyang Geng, Peng Huang, Yongsheng Li, Zhenghe Song
Summary: This paper studies the non-stationary random vibration characteristics of harvesters in field harvesting conditions and analyzes the correlation between vibration frequency and modal frequency. Different time-frequency transformation methods are used to calculate the vibration frequency distribution characteristics of the harvester. The results show that the vibration signals under harvesting conditions conform to the characteristics of non-stationary randomness, and the FFT algorithm provides denser vibration frequencies.
Article
Computer Science, Interdisciplinary Applications
Tessa Maurer, Francesco Avanzi, Carlos A. Oroza, Steven D. Glaser, Martha Conklin, Roger C. Bales
Summary: The Gaussian Mixture Models (GMMs) provide a more robust, objective, and repeatable method for spatial distribution of hydrologic models, better representing the distribution of watershed features relevant to the hydrologic cycle. It demonstrates superior or equivalent performance compared to traditional distribution models while significantly reducing time costs.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Acoustics
Zhen Shan, Zhongqiu Wang, Jianhua Yang, Dengji Zhou, Houguang Liu
Summary: In this paper, a novel general time-varying scale transformation aperiodic stochastic resonance method is proposed to extract and enhance weak non-stationary signals under strong noise background. The method has stronger noise robustness and can provide output with higher signal-to-noise ratio and cross-correlation coefficient.
JOURNAL OF VIBRATION AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Tony Bonnaire, Aurelien Decelle, Nabila Aghanim
Summary: The paper proposes a regularized version of mixture models to learn a principal graph from a distribution of D-dimensional datapoints. The proposed method utilizes an Expectation-Maximization procedure to iteratively estimate the parameters of the model and guarantee convergence for any graph prior. It also incorporates a natural way to handle outliers and heteroscedasticity, and extends the graph prior using random sub-samplings to consider cycles in the spatial distribution.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Imil Hamda Imran, Rustam Stolkin, Allahyar Montazeri
Summary: This paper studies the real-time parameter estimation and adaptive tracking control problem for a 6-DOF quadrotor UAV as an under-actuated system. It proposes a virtual PD method for position dynamics and compares two adaptive control schemes for attitude dynamics with unknown parameters. The first scheme extends the classical adaptive scheme using the certainty equivalence principle, while the second scheme introduces an additional continuous function. Both schemes incorporate a robust term to handle perturbations caused by unknown time-varying parameters. Rigorous analytical proof and numerical simulation analysis support the efficacy of the proposed controller.
Article
Automation & Control Systems
Ting Wang, Xin Li, Jin Guo, Yanlong Zhao
Summary: This paper studies the system identification of ARMA models with binary-valued observations, which is more challenging due to limited accessible information compared to existing quantized identification methods. Unlike the identification of FIR models, ARMA models require estimating both the parameter and the prediction of the original system output. An online identification algorithm is proposed, consisting of parameter estimation and prediction of the original output, which are strongly coupled and mutually reinforcing. By jointly analyzing the two estimates, the paper proves that the parameter estimate can converge to the true parameter with a convergence rate of O(1/k) under certain conditions. Simulations are provided to demonstrate the theoretical results.
Article
Computer Science, Artificial Intelligence
Tomoharu Iwata
Summary: Appropriate representations are critical for better clustering performance. Existing neural network-based clustering methods do not directly train neural networks to improve clustering performance. We propose a method that meta-learns clustering knowledge from labeled data and applies it to cluster unseen unlabeled data. The method trains neural networks to obtain representations that improve clustering performance through variational Bayesian (VB) inference and infinite Gaussian mixture models.
Article
Mathematics
Xiang Peng, Xiaoqing Xu, Jiquan Li, Shaofei Jiang
Summary: A sampling-based sensitivity analysis methodology was proposed for engineering products with uncertain input variables and distribution parameters, reducing nonlinearity and calculating mean and variance using innovative methods. Compared to traditional Monte Carlo simulation, the proposed algorithm decreased loop and sampling numbers efficiently, showcasing accuracy and efficiency in numerical and engineering examples.
Article
Automation & Control Systems
Weiming Shao, Zhiqiang Ge, Zhihuan Song
Summary: A semisupervised Bayesian GMM (S(2)BGMM) method is proposed to learn from both labeled and unlabeled datasets, addressing the issue of limited labeled samples. Case studies demonstrate the effectiveness and reliability of the proposed approach in industrial processes.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Chemistry, Multidisciplinary
Tongjing Sun, Yabin Wen, Xuegang Zhang, Bing Jia, Mengwei Zhou
Summary: Ocean reverberations, a significant interference source in active sonar, are often described using the Rayleigh distribution. This study introduces the Gaussian mixture model to simulate the distribution of reverberation data, initializing parameters based on statistical attributes and estimating them using the expectation-maximization algorithm. The most suitable statistical model is selected based on evaluation results, highlighting the effectiveness of the Gaussian mixture model in accurately characterizing the distribution of reverberation data.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Multidisciplinary
Nikos A. Spanos, John S. Sakellariou, Spilios D. Fassois
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2020)
Article
Engineering, Civil
Henar Martin-Sanz, Konstantinos Tatsis, Vasilis K. Dertimanis, Luis David Avendano-Valencia, Eugen Brehwiler, Eleni Chatzi
STRUCTURE AND INFRASTRUCTURE ENGINEERING
(2020)
Article
Acoustics
T-C Aravanis, J. S. Sakellariou, S. D. Fassois
JOURNAL OF SOUND AND VIBRATION
(2020)
Article
Engineering, Mechanical
Luis David Avendano-Valencia, Eleni N. Chatzi
PROBABILISTIC ENGINEERING MECHANICS
(2020)
Article
Engineering, Mechanical
Luis David Avendano-Valencia, Eleni N. Chatzi, Dmitri Tcherniak
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2020)
Article
Computer Science, Artificial Intelligence
Luis David Avendano-Valencia, Knud B. Yderstraede, Esmaeil S. Nadimi, Victoria Blanes-Vidal
Summary: A video-based eye-tracking method is proposed as a low-cost alternative for detection of diabetic neuropathy, showing significant differences in eye movement precision between control subjects and diabetics. The methodology achieves a classification accuracy of 95% and encourages further research in this field despite limitations in sample size.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2021)
Article
Green & Sustainable Science & Technology
Luis David Avendano-Valencia, Imad Abdallah, Eleni Chatzi
Summary: A data-driven model using Gaussian Process Regression was proposed to predict short-term fatigue Damage Equivalent Loads (DEL) on wake-affected wind turbines. Sensitivity analysis was conducted to assess the impact of different inputs on DEL predictions, with qualitative recommendations provided to minimize errors. The study aims to promote the use of sparse structural monitoring for diagnostics on wake-affected turbines.
Article
Chemistry, Analytical
Panayiotis Theodoropoulos, Christos C. Spandonidis, Fotis Giannopoulos, Spilios Fassois
Summary: The ability to use data for improvement in the shipping sector is crucial, with marine engineering considering data as an asset and focusing on system design. A methodology using a 1D Convolutional Neural Network is developed to identify early signs of defective behavior in vessel operation through data analysis. The study shows the applicability of 1D-CNN models in condition monitoring for ships, a topic not thoroughly explored in the maritime sector.
Article
Engineering, Multidisciplinary
Callum Roberts, David Garcia Cava, Luis D. Avendano-Valencia
Summary: The implementation of Vibration-Based Structural Health Monitoring (VSHM) systems is affected by Environmental and Operational Variations (EOVs), which cause observations to behave differently. This study addresses these challenges by using multivariate nonlinear regression and proposes a method to normalize new features. The research also investigates the selection of Damage Sensitive Features (DSFs) and how damage detection behaves under varying amounts of input information.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Mechanical
Jonas Gad Kjeld, Luis David Avendano-Valencia, Jonas Vestermark
Summary: Over time, the modal properties of Offshore Wind Turbines (OWT) undergo significant variations due to continuous changes in the environment and operation. These variations have a substantial impact on the turbine loads, leading to uncertainties in fatigue and useful life predictions. To understand how different environmental and operational parameters influence the modal properties of OWTs, a systematic analysis was conducted using data from a long-term monitoring campaign on an idling OWT in the DanTysk wind farm. By clustering the vibration response data into bins based on wind and wave characteristics, the study aims to isolate the effects of different sources of uncertainty on the OWT modal properties. The findings of the study help estimate the average and standard errors of structural damping in the first fore-aft and side-to-side tower bending modes for zero wind speed and wave excitation conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Jonas Gad Kjeld, Luis David Avendano-Valencia, Anders Brandt, Silas Sverre Christensen, Jacob Karottki Falk Andersen
Summary: This paper aims to experimentally determine the damping values and their uncertainty bounds for the first two modes of an idling offshore wind turbine. Field measurements from a 3.6 MW offshore wind turbine were used for this purpose. The proposed methodology confined the 90% confidence intervals of the damping ratio estimates within specific percentage points in different directions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Marine
Ioannis Asimakopoulos, Luis David Avendano-Valencia, Marie Luetzen, Niels Gorm Maly Rytter
Summary: This paper presents condition monitoring techniques for early detection of faults related to piston rings in remote cylinders of two-stroke marine diesel engines, which can ensure the safety of voyages and crew.
SHIPS AND OFFSHORE STRUCTURES
(2023)
Article
Engineering, Electrical & Electronic
Nikolaos Kaliorakis, John S. Sakellariou, Spilios D. Fassois
Summary: This study investigates the prompt detection of early-stage hollow worn wheels in railway vehicles using on-board random vibration measurements. Two unsupervised statistical time series methods were proposed and assessed through case studies. The results show that both methods exhibit remarkable performance in detecting wheel wear.
Article
Engineering, Electrical & Electronic
Luis Enrique Avendano, Luis David Avendano-Valencia, Edilson Delgado-Trejos, David Cuesta-Frau
Summary: This work focuses on the harmonic decomposition of pseudo-periodic non-stationary multivariate signals. The proposed method achieves this by establishing a procedure to transform the multivariate signal into a block-diagonal state-space representation. The harmonic components and instantaneous frequency are estimated using Kalman filtering, and an optimization framework is provided for the hyperparameters of the state space representation.
Proceedings Paper
Automation & Control Systems
Luis David Avendano-Valencia, Luis Enrique Avendano, Edilson Delgado-Trejos, David Cuesta-Frau
Summary: This work introduces a parametric modal decomposition method for multivariate non-stationary signals based on a block-diagonal time-dependent state space representation and Kalman filtering/smoothing. The methodology evaluates in a numerical example, concerning a multivariate signal with three modal components, featuring mode crossings and vanishing amplitudes. The identification of the state/parameter trajectories and the hyperparameters is accomplished with a tailored Expectation-Maximization algorithm.
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)