This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for systems has been a challenge encountered in wide range engineering fields, attracting decades research effort. To date, one the most promising and popular approaches is to view address problem from probabilistic perspective, which enables unknown state variables by tracking their distribution or statistics (e.g., mean covariance) conditioned on system's measurement data. offers...

We articulate the design imperatives for machine learning based digital twins nonlinear dynamical systems, which can be used to monitor "health" of system and anticipate future collapse. The fundamental requirement systems is evolution: twin must able evolve its state at present time next step without further input-a that reservoir computing naturally meets. conduct extensive tests using prototypical from optics, ecology, climate, where respective specific examples are a chaotic CO2 laser...

Nonlinear tracking control enabling a dynamical system to track desired trajectory is fundamental robotics, serving wide range of civil and defense applications. In engineering, designing requires complete knowledge the model equations. We develop model-free, machine-learning framework two-arm robotic manipulator using only partially observed states, where controller realized by reservoir computing. Stochastic input exploited for training, which consists partial state vector as first its...

Outliers can be caused by sensor errors, model uncertainties, changes in the ambient environment, data loss, or malicious cyberattacks to contaminate measurement process of many nonlinear dynamic systems. When extended Kalman filter (EKF) is applied such systems for state estimation, outliers seriously reduce estimation accuracy. This brief proposes an innovation saturation mechanism make EKF robust against outliers. applies a function that leverages correct estimation. As such, when occur,...

For anticipating critical transitions in complex dynamical systems, the recent approach of parameter-driven reservoir computing requires explicit knowledge bifurcation parameter. We articulate a framework combining variational autoencoder (VAE) and to address this challenge. In particular, driving factor is detected from time series using VAE an unsupervised-learning fashion extracted information then used as parameter input computer for transition. demonstrate power unsupervised learning...

Purpose The continual growth of additive manufacturing has increased tremendously because its versatility, flexibility and high customization geometric structures. However, design hurdles are presented in understanding the relationship between fabrication process materials microstructure as it relates to mechanical performance. purpose this paper is investigate role build architecture effects load direction on static response properties acrylonitrile butadiene styrene (ABS) specimens...

Fatigue crack growth propagation in critical rotorcraft structures is much more complex than fixed-wing aircrafts part due to the random and vibratory nature of flight load spectrum. Existing physical damage models, which are originally developed for uniform cyclic loads platforms, not accurate enough structures. Such inaccuracy often results increased risk operation higher cost maintenance. The goal this paper present an integrated diagnostic framework fatigue life estimation by combining...

Complex and nonlinear dynamical systems often involve parameters that change with time, accurate tracking of which is essential to tasks such as state estimation, prediction, control. Existing machine-learning methods require full observation the underlying system tacitly assume adiabatic changes in parameter. Formulating an inverse problem exploiting reservoir computing, we develop a model-free fully data-driven framework accurately track time-varying from partial real time. In particular,...

Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of underwater environment. In order improve UWA communications, modeling and understanding channel is indispensable. However, there exist many challenges high uncertainties environment lack real-world measurement data. this work, capability reservoir computing deep learning has explored for communication accurately using real data collected from a water tank with disturbance Lake...

Acoustic emission signals are information rich and can be used to estimate the size location of damage in structures. However, many existing algorithms may deceived by indirectly propagated acoustic waves which modulated reflection boundaries within We propose two deep learning models identify such that for detection localization used. The first approach uses long short-term memory recurrent neural networks learn distinct patterns directly from time-series data. In second approach, we...

Polymer composites subjected to cyclic loading would exhibit damage precursors, such as crazes and microcracks, during the first few load cycles. However, precursors are not readily detectable with existing sensing techniques, current service life prediction methods depend on macroscopic measures. For critical airframe structures, information does provide adequate warning time for corrective actions. This article explores feasibility of embedding particulate magnetostrictive particles early...

Abstract: The refraction‐induced image distortion introduces large errors in the deformation measurement of fluid submerged specimens using digital correlation (DIC). This study provides a review nature distortion, assesses experimental conditions that interact with refraction and proposes an elastic registration technique to correct underwater images. In technique, control points are selected on reference refracted images template object locally sensitive transformation functions overlay...

The vast majority of existing work on acoustic emission–based structural health monitoring is for geometrically simple structures with uninterrupted propagation path and constant wave speed. Realistic systems such as a full-scale fuselage, however, are built from interconnected pieces acoustically mismatched parts sandwich core panels, stringer stiffened skin, fastener holes. geometric complexity dynamic operating environment realistic mean that the emission undergoes multiple reflections,...

Parameters of the mathematical model describing many practical dynamical systems are prone to vary due aging or renewal, wear and tear, as well changes in environmental service conditions. These variabilities will adversely affect accuracy state estimation. In this paper, we introduce SSUE: Simultaneous State Uncertainty Estimation for quantifying parameter uncertainty while simultaneously estimating internal a system. Our approach involves development Bayesian framework that recursively...

Measurements made on a practical system can often be subject to outliers due sensor errors, changes in ambient environment, data loss or malicious cyber attacks. The seriously reduce the accuracy of Kalman filter (KF) when it is applied for state estimation. This paper proposes an innovation saturation mechanism robustify standard KF against outliers. basic notion saturate distorted by outlier, thus preventing from impairing estimation process. presents adaptive adjustment bound. design...

The vibration signals from sensors monitoring the activity of individual bearings in a power train unit may be linear instantaneous mixtures vibrations generated by various dynamic components. Generally, an exact physical model describing mixing process and contribution each component to received sensor signal is not available. Vibration source defective often overlap time frequency, and, as such, direct use time- frequency-domain methods result erroneous diagnostic information. This paper...

An experimental study is performed to investigate the electro-mechanical response of three-dimensionally conductive multi-functional glass fiber/epoxy laminated composites under quasi-static tensile loading. To generate a three-dimensional network within composites, multi-wall carbon nanotubes are embedded epoxy matrix and fibers reinforced between fiber laminates using an electro-flocking technique. A combination shear mixing ultrasonication employed disperse inside matrix. vacuum infusion...

The rapid growth of research in exploiting machine learning to predict chaotic systems has revived a recent interest Hamiltonian Neural Networks (HNNs) with physical constraints defined by the Hamilton's equations motion, which represent major class physics-enhanced neural networks. We introduce HNNs capable adaptable prediction nonlinear systems: training network based on time series from small number bifurcation-parameter values target system, HNN can dynamical states at other parameter...