Impact load identification of composite structure using genetic algorithms

Yan, Gang; Zhou, Li

doi:10.1016/j.jsv.2008.06.051

Cited by 94 publications

(50 citation statements)

References 19 publications

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“…The accuracy of the reconstructed impact force is qualitatively high comparing to previous works [1,2,4]. However, the variation in different case studies and measurement methods is a significant factor in the accuracy of the results.…”

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confidence: 57%

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Practical high-fidelity frequency-domain force and location identification

Ghaderi

Dick

Foley

et al. 2015

Computers & Structures

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Identifying the force information and location of an impact event is important for predicting and/or monitoring potential damage to the structures. Directly measuring the impact event and/or locating the impact force is not always possible due to the nature of the impact or the structure. In this work, a new force and location identification method is introduced which utilizes a spectral finite element method (SFEM) model of the structure. The identification technique is demonstrated and studied through its application to beam structures in order to identify impulsive loads. Wave propagation data collected with accelerometers placed on the structure are used in order to determine the impact information.When the impact force is applied between the accelerometers, the calculated force is distributed over the two accelerometer positions on either side of the impact location. The location identification process uses the distribution of the identified force information in order to locate the impact position. This method is performed by matching simulated data to the identified force data by tuning the impact location within the numerical model. When a sufficient level of agreement is achieved, the impact location is determined. In order to validate the results of the numerical studies, identified impact forces and locations are calculated for experimental data and good agreement is observed with the * Corresponding author

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confidence: 57%

“…Indirect methods for impact force identification have attracted researchers due to the nonlinearity of the impact problem and complexity of impact incidents [1,2,3,4]. Numerous techniques have been developed which uses inverse methods for impact force identification.…”

Section: Introductionmentioning

confidence: 99%

Practical high-fidelity frequency-domain force and location identification

Ghaderi

Dick

Foley

et al. 2015

Computers & Structures

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“…The input values are The node numbers of the input layer and output layer are the number of input and output values in the network, respectively. The node number of the hidden layer can be determined by [19] = √ + + ,…”

Section: Type Recognitionmentioning

confidence: 99%

“…In terms of computation amount, the discrete convolution relation of model function is more complex, resulting in a heavy workload [5,16,17]. With the development of computer technology, new algorithms have been introduced into the field of load recognition [18][19][20][21][22]. Neural networks are being developed and have excellent potential in this area [3,23].…”

Section: Introductionmentioning

confidence: 99%

Qualitative Recognition of Typical Loads in Low‐Speed Rotor System

Zhang

Zhao-jian

2017

Mathematical Problems in Engineering

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While the load variations within the low speed rotor systems affect the operating conditions and mechanical properties, they may also provide information on machine faults. Therefore, load recognition is of great significance in operational monitoring for detecting early warning signs of failure and diagnosing faults. In this paper, five types of typical loads in a low-speed rotor system are qualitatively analyzed. Moreover, a method is presented based on the vibration signals from a low-speed rotor system using the ensemble empirical mode decomposition (EEMD), energy feature extraction, and backpropagation neural network (BPNN). A low-speed rotor test bench was designed and manufactured for load recognition and an experiment was set up based on certain load characteristics. Loading tests for five representative categories were conducted and various vibration signals were collected simultaneously. The EEMD was shown to eliminate the mode mixing seen in traditional EMD, which resulted in a clear decomposition of the signal. Finally, the characteristics were imported into a BPNN after energy feature extraction, and the different types of load were accurately recognized. Comparing the experimental results to existing data, a total recognition rate of 92.38% was achieved, demonstrating that the proposed method is both reliable and efficient.

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“…Therefore, it is challenging to set an optimal sampling time interval to balance the tradeoff between reconstruction accuracy and efficiency. Approaches based on various basis functions were proposed to address this challenge, in which unknown dynamic forces were approximated by 2 Shock and Vibration basis functions, such as Gaussian basis functions [14], Bspline functions [15,16], triangle functions [17], exponential function [18], and Daubechies wavelet [19], and subsequently they were reconstructed by identifying the coefficients in these basis functions. These approaches could significantly reduce the number of unknowns (considerably less than that of data points) and shorten the identification time.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Reconstruction of a Dynamic Force Using Multiscale Wavelet Shape Functions

Yang

Zhu

2018

Shock and Vibration

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The shape function-based method is one of the very promising time-domain methods for dynamic force reconstruction, because it can significantly reduce the number of unknowns and shorten the reconstruction time. However, it is challenging to determine the optimum time unit length that can balance the tradeoff between reconstruction accuracy and efficiency in advance. To address this challenge, this paper develops an adaptive dynamic force reconstruction method based on multiscale wavelet shape functions and time-domain deconvolution. A concentrated dynamic force is discretized into units in time domain and the local force in each unit is approximated by wavelet scale functions at an initial scale. Subsequently, the whole response matrix is formulated by assembling the responses induced by the wavelet shape function forces of all time units which are calculated by the structural finite element model (FEM). Then, the wavelet shape function-based force-response equation is established for force reconstruction. Finally, the scale of the force-response equation is lifted by refining the wavelet shape function with high-scale wavelets and dynamic responses with more point data to improve the reconstruction accuracy gradually. Numerical examples of different structural types are analyzed to verify the feasibility and effectiveness of the proposed method.

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Impact load identification of composite structure using genetic algorithms

Cited by 94 publications

References 19 publications

Practical high-fidelity frequency-domain force and location identification

Practical high-fidelity frequency-domain force and location identification

Qualitative Recognition of Typical Loads in Low‐Speed Rotor System

Adaptive Reconstruction of a Dynamic Force Using Multiscale Wavelet Shape Functions

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