Interval optimization of rotor-bearing systems with dynamic behavior constraints using an interval genetic algorithm

Shiau, T. N.; Kang, Chung-Hao; Liu, De‐Shin

doi:10.1007/s00158-007-0199-y

Cited by 16 publications

(6 citation statements)

References 18 publications

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“…Ma et al [52] and Wang et al [53] used the interval arithmetic and the perturbations for uncertain dynamic characteristic analysis of linear rotor systems. An interval optimization scheme was proposed by Shiau et al [54] for rotor-bearing systems. Fu et al formulated the derivative-based interval method [55] and the interval precise integration method [56] to study the steady-state and transient response of an uncertain overhung rotor system.…”

Section: Introductionmentioning

confidence: 99%

Dynamics analysis of a hollow-shaft rotor system with an open crack under model uncertainties

Yang

et al. 2020

Communications in Nonlinear Science and Numerical Simulation

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This paper focuses on the vibration behaviors of a hollow-shaft rotor in presence of an open crack under inherent uncertainties. Non-probabilistic interval variables are used to represent the uncertain parameters, which releases the high demands of probabilistic knowledge in traditional methods. In modeling the shaft, local stiffness matrix of the cracked element is derived by using the neutral axis method. The periodic response of the rotor system is solved by combination of the finite element method (FEM) and the harmonic balance method (HBM). A simple mathematical function, termed as the uncertain response surrogate function (URSF), is constructed to estimate the vibration response in various cases where different parametric uncertainties are taken into consideration. To verify the robustness and accuracy of the URSF, the bounds of estimated response are compared with those obtained from the classical methods. Results show that the surrogate function has good accuracy and robustness, providing an effective method and guidance for diagnosing crack in uncertain context.

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Section: Introductionmentioning

confidence: 99%

Dynamics analysis of a hollow-shaft rotor system with an open crack under model uncertainties

Yang

et al. 2020

Communications in Nonlinear Science and Numerical Simulation

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“…The IGA first appeared at the paper presented by Shiau et al (16) With this algorithm, the interval analysis can be neglected in this optimization process, and the feasible interval set of design parameters that conforms to the optimization can be determined. The procedure for implementing the IGA is presented in the following simple example.…”

Section: Description Of the Igamentioning

confidence: 99%

“…The optimization algorithm can be decided upon by means of engineering experiences, such as a genetic algorithm, augmented Lagrange multiplier method, goal programming method or others, which are described elsewhere (Vanderpaats (24) ). In this paper, the hybrid genetic algorithm(Shiau et al (17) ) is presented. The allowable error in Table 3 is Table 3 Journal of System Design and Dynamics Vol.…”

Section: − ×mentioning

confidence: 99%

“…But the system equations of motion or interval differential formulations sometimes are not easy to be determined, especially with complicated systems. To account for this problem, Shiau et al (16), (18) presented the Interval Genetic Algorithm (IGA), which they referred to as the genetic algorithm (GA). The GA comprises a search strategy based upon the natural rules of the genetic evolution that was first developed by Holland (19) .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Coupled Interval Genetic Algorithm Technique with the Finite Element Method for the Interval Optimization of Structures

Shiau

Kang

Liu

et al. 2009

JSDD

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This paper combines a verified interval optimization method with the FEM for designing structures, which is denominated as the Hybrid Interval Genetic Algorithm (HIGA). This algorithm can neglect formulated equations and interval analysis, and while determining the optimum interval parameters. Furthermore, it can also maximize the design scope. In this paper, this algorithm is implemented for both a truss and frame structure. The interval optimizations include the static and dynamic responses of these structures. The results show that the algorithm which combines the IGA with the FEM can determine the feasible interval design parameters of structures with allowable objective errors.

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“…In this paper, the hybrid genetic algorithm (HGA) is applied to identify optimal designs of the piezoelectric motor with stability constraints. This hybrid optimization procedure first appeared in research by Shiau et al (19) With the hybrid procedure, the GA is applied to identify the initial design variables, first by searching widely over a large design space of the whole domain. As a second step, that adapted optimization algorithm is focused upon promising sub-regions to identify the best design in that region.…”

Section: Introductionmentioning

confidence: 99%

The Multi-Objective Optimal Design of the Disc Type Piezoelectric Motor with Stability Constraint

Shiau

Kang

Liu

et al. 2009

JSDD

Self Cite

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A verified hybrid optimization procedure, which combines the genetic algorithm (GA) with traditional optimization methods, is presented in this paper with a disc type piezoelectric motor. The optimization objectives include minimizing the frictional loss of the rotor and maximizing the efficiency and spin speed of the piezoelectric motor. The behavior constraints include spin speed, natural frequency, and stability. The Runge-Kutta method is applied to determine the dynamic response of this motor, and the Floquet theory is employed to analyze the motor's stability. The optimization algorithm (hybrid genetic algorithm, HGA) applies GAs to provide an initial design variables set, thereby avoiding the trial process; thereafter, traditional algorithms are employed to determine optimum results. With the single-objective and multi-objective optimizations by using the HGA, both the efficiency and spin speed of the motor can be improved, and the frictional loss of the coating can be decreased effectively, either individually or simultaneously. The designs of the designated performances can be derived as well.

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Interval optimization of rotor-bearing systems with dynamic behavior constraints using an interval genetic algorithm

Cited by 16 publications

References 18 publications

Dynamics analysis of a hollow-shaft rotor system with an open crack under model uncertainties

Dynamics analysis of a hollow-shaft rotor system with an open crack under model uncertainties

Coupled Interval Genetic Algorithm Technique with the Finite Element Method for the Interval Optimization of Structures

The Multi-Objective Optimal Design of the Disc Type Piezoelectric Motor with Stability Constraint

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