A Bayesian fusion method for composite damage identification using Lamb wave

Huo, Haode; He, Jingjing; Guan, Xuefei

doi:10.1177/1475921720945000

Cited by 48 publications

(24 citation statements)

References 47 publications

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“…In this work, two merging approaches have been employed. e two classifiers were given equal weights during the first method (i.e., the equal weighting method), and the Bayesian merger method [27] was utilized in the second approach.…”

Section: Merging Approach Since Multimodal Signals Have Been Extractedmentioning

confidence: 99%

Multimodal Opera Performance Form Based on Human-Computer Interaction Technology

2022

International Transactions on Electrical Energy Systems

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“Audience Engagement (AE)” describes how a stage performance affects the audience’s thoughts, provokes a bodily response, and spurs cognitive growth. With little audience involvement, theatre performing arts like opera typically have difficulty keeping audiences’ attention. The brain-computer interaction (BCI) technology could be used in opera performances to alter the audience’s emotional experience. Nevertheless, for such BCI systems to function, they must accurately identify their participants’ present emotional states. Although difficult to evaluate, audience participation is a vital sign of how well an opera performs. Practical methodological approaches for real-time perception and comprehension of audience emotions include psychological and physiological assessments. Hence, a multimodal emotional state detection technique (MESDT) for enhancing the AE in opera performance using BCI has been proposed. Three essential steps make up a conceptual MESDT architecture. An electroencephalogram (EEG) and other biological signs from the audience are first captured. Second, the acquired signals are processed, and the BCI tries to determine the user’s present psychological response. Third, an adaptive performance stimulus (APS) is triggered to enhance AE in opera performance, as determined by a rule base. To give the opera audience a high-quality viewing experience, the immersive theatre performance has been simulated. Fifty individuals have been used in the experimental assessment and performance studies. The findings demonstrated that the proposed technology had been able to accurately identify the decline in AE and that performing stimuli had a good impact on enhancing AE during an opera performance. It has been further shown that the suggested design improves the overall performance of AE by 5.8% when compared to a typical BCI design (one that uses EEG characteristics solely) for the proposed MESDT framework with BCI.

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Section: Merging Approach Since Multimodal Signals Have Been Extractedmentioning

confidence: 99%

Multimodal Opera Performance Form Based on Human-Computer Interaction Technology

2022

International Transactions on Electrical Energy Systems

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“…In the paper a framework for data classification based on Bayesian inference is proposed. Bayesian approach, due to its generality and flexibility, is often adopted for data classification in the SHM field [62][63][64][65][66].…”

Section: Definition Of Data Classification Methodsmentioning

confidence: 99%

Application of PZT Ceramic Sensors for Composite Structure Monitoring Using Harmonic Excitation Signals and Bayesian Classification Approach

et al. 2021

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The capabilities of ceramic PZT transducers, allowing for elastic wave excitation in a broad frequency spectrum, made them particularly suitable for the Structural Health Monitoring field. In this paper, the approach to detecting impact damage in composite structures based on harmonic excitation of PZT sensor in the so-called pitch–catch PZT network setup is studied. In particular, the repeatability of damage indication for similar configuration of two independent PZT networks is analyzed, and the possibility of damage indication for different localization of sensing paths between pairs of PZT sensors with respect to damage locations is investigated. The approach allowed for differentiation between paths sensitive to the transmission mode of elastic wave interaction and sensitive reflection mode. In addition, a new universal Bayesian approach to SHM data classification is provided in the paper. The defined Bayesian classifier is based on asymptotic properties of Maximum Likelihood estimators and Principal Component Analysis for orthogonal data transformation. Properties of the defined algorithm are compared to the standard nearest-neighbor classifier based on the acquired experimental data. It was shown in the paper that the proposed approach is characterized by lower false-positive indications in comparison with the nearest-neighbor algorithm.

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“…In general, the proposed model can yield improved prediction results, particularly for the tail regions of the stress–strain data. The detailed error results in Table 5 indicate that the proposed model outperforms the reference model at all temperatures except 10

C. It should be noted that other probabilistic methods such as Bayesian model assessment [ 38 , 39 , 40 ] can also be employed to compare the performance of the models.…”

Section: Model Validations and Comparisonsmentioning

confidence: 98%

A General Temperature-Dependent Stress–Strain Constitutive Model for Polymer-Bonded Composite Materials

et al. 2021

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This study develops a general temperature-dependent stress–strain constitutive model for polymer-bonded composite materials, allowing for the prediction of deformation behaviors under tension and compression in the testing temperature range. Laboratory testing of the material specimens in uniaxial tension and compression at multiple temperatures ranging from −40 ∘C to 75 ∘C is performed. The testing data reveal that the stress–strain response can be divided into two general regimes, namely, a short elastic part followed by the plastic part; therefore, the Ramberg–Osgood relationship is proposed to build the stress–strain constitutive model at a single temperature. By correlating the model parameters with the corresponding temperature using a response surface, a general temperature-dependent stress–strain constitutive model is established. The effectiveness and accuracy of the proposed model are validated using several independent sets of testing data and third-party data. The performance of the proposed model is compared with an existing reference model. The validation and comparison results show that the proposed model has a lower number of parameters and yields smaller relative errors. The proposed constitutive model is further implemented as a user material routine in a finite element package. A simple structural example using the developed user material is presented and its accuracy is verified.

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A Bayesian fusion method for composite damage identification using Lamb wave

Cited by 48 publications

References 47 publications

Multimodal Opera Performance Form Based on Human-Computer Interaction Technology

Multimodal Opera Performance Form Based on Human-Computer Interaction Technology

Application of PZT Ceramic Sensors for Composite Structure Monitoring Using Harmonic Excitation Signals and Bayesian Classification Approach

A General Temperature-Dependent Stress–Strain Constitutive Model for Polymer-Bonded Composite Materials

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