A robust locating multi-optima approach for damage identification of plate-like structures

Lu, Kunfeng; Li, Y.Y.

doi:10.1016/j.asoc.2018.11.035

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…Species with strong survival ability stay in the microhabitat, while those with weak survival ability are eliminated, and under this mechanism of "survival of the fittest," species in the microhabitat evolve. Using the microhabitat technique, each generation of individuals is divided into several classes, and a number of individuals with greater adaptability in each class are selected as the best representatives of a class to form a swarm, which dynamically forms a relatively independent search space to achieve simultaneous search of multiple extremal regions, in order to overcome the defects of early convergence and easy to fall into local optimum of the basic particle swarm algorithm, and obtain better recognition accuracy and convergence speed (Lu and Li, 2019;Rani and Mahapatra, 2019). Thus, this paper adopts the improved small habitat particle swarm algorithm with high reasonableness and feasibility.…”

Section: Solution Of the Lower Modelmentioning

confidence: 99%

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

Zhang

et al. 2022

Front. Energy Res.

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The power-to-gas (P2G) technology transforms the unidirectional coupling of power network and natural gas network into bidirectional coupling, and its operational characteristics provide an effective way for wind and solar energy accommodation. The paper proposes a bi-level optimal dispatch model for the integrated energy system with carbon capture system and P2G facility. The upper model is an optimal allocation model for coal-fired units, and the lower model is an economic dispatch model for the integrated energy system. Moreover, the upper model is solved by transforming the model into a mixed-integer linear programming problem and calling CPLEX, and the lower model is a multi-objective planning problem, which is solved by improving the small-habitat particle swarm algorithm. Finally, the simulation is validated by the MATLAB platform, and the results show that the simultaneous consideration of carbon capture system and P2G facility improves the economics of the integrated energy system and the capacity of wind and solar energy accommodation.

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Section: Solution Of the Lower Modelmentioning

confidence: 99%

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

Zhang

et al. 2022

Front. Energy Res.

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“…Enhancing the accuracy and reliability of RUL prediction necessitates thorough research into each stage of HI construction. In the HI data processing phase aimed at denoising, the preservation of potential degradation information while effectively eliminating noise from sensor signals has consistently posed a technical challenge [16,17]. This objective necessitates the consideration of multiple factors, including the extent of noise and the integrity of the signal.…”

Section: Introductionmentioning

confidence: 99%

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

Yang,

Sun

et al. 2024

Meas. Sci. Technol.

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In data-driven prognosis methods, the accuracy of predicting the remaining useful life (RUL) of mechanical systems is predominantly contingent upon the efficacy of system health indicators, typically amalgamated from statistical features derived from collected signals. Nevertheless, the majority of extant health indicators are beset by two principal shortcomings: (1) During traditional data denoising processes, degradation information from raw data is prone to loss owing to the lack of incorporation of the true physical properties of the data; and (2) The performance evaluation of constructed health indicators is imbalanced due to the influence of network structures on single models, often resulting in strong performance in only one or two indicators. To overcome such shortcomings, a mechanical health indicator construction method based on physical properties was proposed, termed 1D-WGAN-GP Joint Attention LSTM-DenseNet (ALD). Firstly, artificial sample data is generated by analysing the physical properties of the original dataset, which is then used to train the 1D-WGAN-GP model to achieve data denoising. Subsequently, the fusion of the Attention LSTM (A-LSTM) network and DenseNet network is utilised to extract crucial feature vectors of health indicators under varying health conditions from the denoised data. Finally, the extracted feature vectors are used to construct system health indicators using the Euclidean distance method, and these indicators are used for predicting the system's RUL. The results indicate that the proposed method outperformed traditional methods in terms of denoising effectiveness. Further, through ablation experiment analysis, the health indicators constructed by the proposed method demonstrated obvious complementarity in terms of monotonicity, correlation, robustness, and comprehensive evaluation. In RUL prediction applications, the proposed method also exhibited good performance, thereby validating its effectiveness.

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“…Although all these mentioned studies, in general, showed high potential in damage localization, they did not indicate a quantitative relationship between the damage indices and the reduction in stiffness. To solve this problem, many authors combined damage indicators with an optimization process [23][24][25][26] or used a hybrid model between optimization and feedforward neural network (FNN) coupled with damage indicator [27,28]. The first approach identifies the damage by solving inverse problems or updating some model parameters until meeting a superior agreement between the FE model and the measured modal parameters.…”

Section: Introductionmentioning

confidence: 99%

An efficient stochastic-based coupled model for damage identification in plate structures

Trinh

Roeck

et al. 2022

Engineering Failure Analysis

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Mode shape-based method has shown its dominance in failure identification of beams. However, it is a challenge for fault detection in a plate structure. It requires combined information in two directions to determine the damage location. In this study, a damage index, namely mode shape derivative based damage identification (MSDBDI), is applied to localize damage in fixed-free plate structures. Two-dimensional (2D) displacement mode shapes and their derivatives are used to identify the MSDBDI index. It is a fact that this indicator is stuck in indicating the damage severity. Hence, a coupled model between an artificial neural network (ANN) and antlion optimizer (ALO), so-called ALOANN is used to overcome this drawback. In this method, ALO instead of a backpropagation algorithm is used to look for the best initial values of learnable parameters of ANN i.e. weights and biases through mean squared error (MSE). These obtained parameters are added to ANN for damage identification. The efficiency of the proposed approach is tested with two numerical studies of the plate structures with single and multiple damage scenarios. In the first application, damage scenarios in a plate-like structure are detected. In the second application, ALO first is used to build a FE model of a composite structure based on a vibration experiment. Then the slab of the updated model is assumed to be suffered several damage scenarios. In both applications, failures are localized by using damage index. Then, the proposed approach is used to quantify the corresponding extent by means of changes in frequencies and displacement mode shapes. Values of damage index are achieved from modal properties of one or three out of the first five modes of the two considered structures. A conventional ANN also is investigated for comparison. Results of damage identification indicate that the damage indicator coupled with ALOANN show better performance compared with using ANN alone even when a noise level is assigned to modal properties.

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A robust locating multi-optima approach for damage identification of plate-like structures

Cited by 7 publications

References 35 publications

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

An efficient stochastic-based coupled model for damage identification in plate structures

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