Position estimation for timing belt drives of precision machinery using structured neural networks

Kılıç, Esma; Dogruer, C.U.; Dölen, Melik; Koku, Ahmet Buğra

doi:10.1016/j.ymssp.2011.10.013

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…In this methodology, a SNN is devised in a modular form with the guidance of the available information on the system. Detailed discussions on SNN design technique thorough various applications are presented in Kilic et al, 21 Artmeyer et al 25 and Kilic et al 27 The design approach employed in this article (so called 'sketchy guidance') is elaborated in Kilic 28 and is especially helpful while designing a neural network based observers with accurate prediction capability when full-scale models on the nonlinear dynamic system are not available.…”

Section: Structured Neural Network Designmentioning

confidence: 99%

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Prediction of slip in cable-drum systems using structured neural networks

Kılıç

Dölen

2013

Proceedings of the Institution of Mechanical Engineers, Part C:

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This study focuses on the slip prediction in a cable-drum system using artificial neural networks for the prospect of developing linear motion sensing scheme for such mechanisms. Both feed-forward and recurrent-type artificial neural network architectures are considered to capture the slip dynamics of cable-drum mechanisms. In the article, the network development is presented in a progressive (step-by-step) fashion for the purpose of not only making the design process transparent to the readers but also highlighting the corresponding challenges associated with the design phase (i.e. selection of architecture, network size, training process parameters, etc.). Prediction performances of the devised networks are evaluated rigorously via an experimental study. Finally, a structured neural network, which embodies the network with the best prediction performance, is further developed to overcome the drift observed at low velocity. The study illustrates that the resulting structured neural network could predict the slip in the mechanism within an error band of 100 µm when an absolute reference is utilized.

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Section: Structured Neural Network Designmentioning

confidence: 99%

“…Likewise, the switching network in Figure 10 is to carry out the following function Design of such neural networks is elaborated in Kilic et al 27 Consequently, the switching network in the form of a RNN could be developed as…”

Section: Structured Neural Network Designmentioning

confidence: 99%

Prediction of slip in cable-drum systems using structured neural networks

Kılıç

Dölen

2013

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…The design of motion profiles of machine elements has been widely studied in research areas such as mechanical engineering and control engineering. Commonly, the transmission chain between the actuator coordinate, commanded by the user, and the coordinate that describes the desired motion, is linear (pulley belt [1], gear chain [2], rack and pinion, etc.) ( Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Feasibility of motion laws for planar one degree of freedom linkage mechanisms at dead point configurations

García

Fontanet

Nebot

2018

Mechanical Systems and Signal Processing

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This paper proposes an analytical solution of the Inverse Kinematics (IK) problem at dead point configurations for any planar one degree of freedom linkage mechanism, with regard to the continuity C n of the motion law. The systems analyzed are those whose elements are linked with lower pairs and do not present redundancies. The study aims to provide the user with some rules to facilitate the design of feasible motion profiles to be reproduced by conventional electrical actuators at these configurations. During the last decades, sev- eral methods and techniques have been developed to study this specific configuration. However, these techniques are mainly focused on solving numerically the IK indetermi- nacy, rather than analyzing the motion laws that the mechanisms are able to perform at these particular configurations. The analysis presented in this paper has been carried out differentiating and applying l’Hôpital’s rule to the system of constraint equations / ð q Þ of the mechanism. The study also considers the feasibility of the time-domain profiles to be reproduced with conventional electrical actuators (i.e. AC/DC motors, linear actuators, etc.). To show the usefulness and effectiveness of the method, the development includes the analytical application and numerical simulations for two common one degree of free- dom systems: a slider-crank and a four linkage mechanisms. Finally, experimental results are presented on a four linkage mechanism test bed.Postprint (author's final draft

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“…The soft sensing technique can also be applied to improve the measurement quality of some variables by incorporating information from various sources. Many achievements of soft sensing based on Neural Network (NN) [17][18][19][20][21] and Support Vector Regression (SVR) [22,23] are reported. These applications of soft sensing technique are closely related to the motion data process.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Behavior Analysis of Touchdown Process in Active Magnetic Bearing System Based on a Machine Learning Method

Sun

Yan

Zhao

et al. 2017

Science and Technology of Nuclear Installations

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Magnetic bearings are widely applied in High Temperature Gas-cooled Reactor (HTGR) and auxiliary bearings are important backup and safety components in AMB systems. The performance of auxiliary bearings significantly affects the reliability, safety, and serviceability of the AMB system, the rotating equipment, and the whole reactor. Research on the dynamic behavior during the touchdown process is crucial for analyzing the severity of the touchdown. In this paper, a data-based dynamic analysis method of the touchdown process is proposed. The dynamic model of the touchdown process is firstly established. In this model, some specific mechanical parameters are regarded as functions of deformation of auxiliary bearing and velocity of rotor firstly; furthermore, a machine learning method is utilized to model these function relationships. Based on the dynamic model and the Kalman filtering technique, the proposed method can offer estimation of the rotor motion state from noisy observations. In addition, the estimation precision is significantly improved compared with the method without learning. The proposed method is validated by the experimental data from touchdown experiments.

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Position estimation for timing belt drives of precision machinery using structured neural networks

Cited by 10 publications

References 16 publications

Prediction of slip in cable-drum systems using structured neural networks

Prediction of slip in cable-drum systems using structured neural networks

Feasibility of motion laws for planar one degree of freedom linkage mechanisms at dead point configurations

Dynamic Behavior Analysis of Touchdown Process in Active Magnetic Bearing System Based on a Machine Learning Method

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