1,720,995 research outputs found
Fatigue Crack Growth in structures under random spectrum loading: Markov chain Monte Carlo methods for parameter identification
No full textSVILUPPO DI UN SISTEMA DI DIAGNOSTICA ON-BOARD BASATO SULLA MISURA DEL CAMPO DI DEFORMAZIONE PER L’IDENTIFICAZIONE DI CRICCHE A FATICA
No full textAdaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks
Full text linkLithium-Ion rechargeable batteries are widespread power sources with applications to consumer electronics, electrical vehicles, unmanned aerial and spatial vehicles, etc. The failure to supply the required power levels may lead to severe safety and economical consequences. Thus, in view of the implementation of adequate maintenance strategies, the development of diagnostic and prognostic tools for monitoring the state of health of the batteries and predicting their remaining useful life is becoming a crucial task. Here, we propose a method for predicting the end of discharge of Li-Ion batteries, which stems from the combination of particle filters with radial basis function neural networks. The major innovation lies in the fact that the radial basis function model is adaptively trained on-line, i.e., its parameters are identified in real time by the particle filter as new observations of the battery terminal voltage become available. By doing so, the prognostic algorithm achieves the flexibility needed to provide sound end-of-discharge time predictions as the charge-discharge cycles progress, even in presence of anomalous behaviors due to failures or unforeseen operating conditions. The method is demonstrated with reference to actual Li-Ion battery discharge data contained in the prognostics data repository of the NASA Ames Research Center database
An investigation of strain energy release rate models for real-time prognosis of fiber-reinforced laminates
No full textTechnological advancements in real-time distributed sensing and processing for structural health monitoring systems have enabled exploration of the next frontier in structural health monitoring for in situ condition-based prediction of remaining life of damaged or aging structures. In that context, model-based prognostics methods have shown considerable promising results. These methods require that suitable damage progression models are available or be developed. Recent works have shown that energy release rate models work effectively for predicting material stiffness degradation based on matrix-cracking. However, since delamination and matrix-cracking damage modes are known to co-exist and fuel each other's progression, it is desirable to investigate extension of these models for multiple damage modes. To that end, this paper analyzes several multiple damage-mode models from composite modeling literature and assesses them against experimental data from run-to-failure aging experiments. These models aim to estimate and correlate strain energy release rate and the residual stiffness as a function of the damage extent. Model review in this work reports modeling behavior and mathematical complexity along with strengths and limitations of these models. This is expected to guide selection of suitable model for a more robust prognostic solution generalized for more realistic degradation scenarios
Sequential Monte-Carlo sampling based on a committee of artificial neural networks for posterior state estimation and residual lifetime prediction
Full text linkThe application of Bayesian methods to the problem of fatigue crack growth prediction has been growing in recent years. In particular, sequential Monte-Carlo sampling is often presented as an efficient model-based technique to filter the sequential measures of the damage evolution provided as an input to the algorithm. However, a lot of measures are required to reliably identify the system state condition and the underlying model parameters. Many studies rely on the availability of a relatively dense sequence of crack length measures during damage evolution, made in most cases impractical by the consequent maintenance costs. Thus, real-time damage diagnosis is a requirement to enable prognostic health management. This work focuses on the application of sequential Monte-Carlo sampling to estimate the probabilistic residual life of a structural component subjected to fatigue crack propagation, while real-time estimation of crack length is provided through a committee of artificial neural networks, trained with finite element simulated strain patterns. Multiple crack length observations are available at each discrete time and are provided as the input to the prognostic system, based on a sequential importance resampling algorithm. Each time a new set of measures is available, the algorithm evaluates the posterior distribution of the augmented state vector, including the crack length and a material parameter governing damage evolution. This filtered information is used to numerically update the probability density functions of the residual life of the component. The methodology is applied first to a simulated crack and then to a metallic stiffened panel specimen subject to fatigue crack growth
UTILIZZO CONGIUNTO DELL’EQUAZIONE NASGRO E DI MODELLI STATISTICI PER IL MONITORAGGIO IN TEMPO REALE DI CRICCHE A FATICA
No full textOn-line Updating of Dynamic State-Space Model for Bayesian Filtering through Markov chain Monte Carlo Techniques
No full textReal-time prognosis of crack growth evolution using sequential Monte Carlo methods and statistical model parameters
Full text linkA probabilistic method to monitor and predict fatigue crack propagation is presented in this work. The technique makes use of sequential Monte Carlo sampling combined with the probability density functions of the model parameters. The technique leads to an adaptive dynamic state-space model for crack evolution able to identify the most probable parameters, enhancing the estimation of the residual life of the system. The lifetime predictor presented here could be implemented in advanced maintenance strategies for critical structures employed in civil, industrial, and aerospace fields. The algorithm is first applied to a simulated crack growth, and then to some experimental crack propagations from laboratory tests on helicopter panels. The applicability within on-line continuous monitoring systems is discussed at the end of the paper
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