Fatigue crack growth prediction of 7075 aluminum alloy based on the GMSVR model optimized by the artificial bee colony algorithm

Yang, Dalian; Liu, Yilun; Li, Songbai; Tao, Jie; Liu, Chi; Yi, Jiuhuo

doi:10.1108/ec-11-2015-0362

Cited by 11 publications

(8 citation statements)

References 23 publications

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“…Due to various causes such as corrosion and temperature fluctuation, cracks may result in failure [5]. If these cracks are not detected in time, a sudden structural failure can lead to catastrophic damages [6,7]. Due to the various types of crack in different structures, currently, industries have a number of different damage-detection methods, such as ultrasonic methods [8][9][10], X-ray methods and vibration-based damage identification methods.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Vibration Analysis of a Beam with a Breathing Crack

Hui

Liu

2019

Applied Sciences

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The phenomena of sub- and super-harmonic responses make up one of the prominent nonlinear characteristics of a beam with a breathing crack. In order to fully understand the behaviors of sub- and super-harmonic resonances, it is necessary to analyze the nonlinear vibration of a beam-like structure with a breathing crack. In this study, a new stiffness model that considers the influence of the partial crack closure is proposed to model the stiffness variation of the cracked beam. Based on the finite element model of a beam with a breathing crack, the multiple-scale method is proposed to analyze the nonlinear vibration of a cracked beam subjected to harmonic excitation, and the relation between the nonlinear vibration of the cracked beam and the system parameters is obtained. An experiment is conducted to validate the analytical results. The study shows that the nonlinear responses of a beam with a breathing crack are affected by both the structural parameters and the crack parameters.

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Section: Introductionmentioning

confidence: 99%

Nonlinear Vibration Analysis of a Beam with a Breathing Crack

Hui

Liu

2019

Applied Sciences

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“…Statistical learning theory is specialized for small sample situations of machine learning theory; given this, SVM has a good generalization ability. In addition, SVM is a convex quadratic optimization problem, which guarantees that the obtained extremum solution is also the global optimal solution [36,37]. Collectively, these characteristics allow it to avoid the local extremum and dimensional disaster problems that are unavoidable when using a neural network.…”

Section: Support Vector Machinementioning

confidence: 99%

Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer

Yang

Miao

Fanyu

et al. 2019

Shock and Vibration

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Bearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters are difficult to select. To address these limitations, an escape mechanism and adaptive convergence conditions were introduced to the ALO algorithm. As a result, the EALO method was proposed and has been applied to the more accurate selection of SVM model parameters. To assess the model, the vibration acceleration signals of normal, inner ring fault, outer ring fault, and ball fault bearings were collected at different rotation speeds (1500 r/min, 1800 r/min, 2100 r/min, and 2400 r/min). The vibration signals were decomposed using the variational mode decomposition (VMD) method. The features were extracted through the kernel function to fuse the energy value of each VMD component. In these experiments, the two most important parameters for the support vector machine—the Gaussian kernel parameter σ and the penalty factor C—were optimized using the EALO algorithm, ALO algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. The performance of these four methods to optimize the two parameters was then compared and analyzed, with the EALO method having the best performance. The recognition rates for bearing faults under different tested rotation speeds were improved when the SVM model parameters optimized by the EALO were used.

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“…Hence, the ability to clearly diagnose the presence of misalignment using vibration analysis can be vital in reducing costly machine unscheduled downtime. Vibration based identification of faults, such as rolling bearing fault [1], gear fault [2], crack [3], and rub [4], is welldeveloped and widely used in practice. However, because more attention is paid to the study of the misalignment of the series connection rotor (such as steam turbine rotor) than that of a coaxial rotor, the misalignment of a coaxial dual-rotor system remains as an outstanding area, where the basic understanding is somewhat lacking.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Analysis of a Coaxial Dual-Rotor System with Coupling Misalignment

Zhang

Huang

et al. 2020

Shock and Vibration

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The finite element model of a dual-rotor system was established by Timoshenko beam element. The dual-rotor system is a coaxial rotor whose supporting structure is similar to that of an aero-engine rotor system. The inner rotor is supported by three bearings, which makes it a redundantly supported rotor. The outer rotor connects the inner rotor by an intershaft bearing. The spectrum characteristics of the dual-rotor system under unbalanced excitation and misalignment excitation were analysed in order to study the influence of coupling misalignment of the inner rotor on the spectral characteristics of the rotor system. The results indicate that the vibration caused by the misaligned coupling of the inner rotor will be transmitted to the outer rotor through the intershaft bearing. Multiple harmonic frequency components, mainly 1x and 2x, will be excited by the coupling misalignment. The amplitudes of all harmonic frequencies increase with the misalignment in both the inner and outer rotors. The vibration level of the outer rotor affected by the misalignment is lower than that of the inner rotor because it is far from the misaligned coupling. Harmonic resonance occurs when any harmonic frequencies of the misalignment response coincide with a natural frequency of the system. In order to verify the theoretical model, experiments are performed on a test rig. Both the experimental and simulation results are in good accordance with each other.

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Fatigue crack growth prediction of 7075 aluminum alloy based on the GMSVR model optimized by the artificial bee colony algorithm

Cited by 11 publications

References 23 publications

Nonlinear Vibration Analysis of a Beam with a Breathing Crack

Nonlinear Vibration Analysis of a Beam with a Breathing Crack

Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer

Spectrum Analysis of a Coaxial Dual-Rotor System with Coupling Misalignment

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