A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network

Wu, Jian-Da; Wang, Yu-Hsuan; Chiang, Peng-Hsin; Bai, Mingsian R.

doi:10.1016/j.eswa.2007.09.015

Cited by 15 publications

(3 citation statements)

References 16 publications

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“…In regard to the complexity of the Three Tank System dynamics by the nonlinear model (7), and in attempt to find a simple model structure which can capture in some appropriate sense the key dynamical properties of the physical plant, we investigate in this section the existence and the use of the RBFNN approach in the identification process of the benchmark dynamics that seems to be more efficient and adequate because its nonlinear characteristics (Roy and Ganguli, 2006;Wu et al, 2009). Thus, the identification problem can be stated as parametric functional optimization problem.…”

Section: The Proposed Methodologymentioning

confidence: 99%

A radial basis function neural network optimized through modified DIRECT algorithm based-model for a three interconnected water tank

Kouadri

Chiter

Zelmat

2010

Journal of Discrete Mathematical Sciences and Cryptography

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In many physical systems, it is difficult to obtain a model structure that is highly nonlinear and complex. However, models are usually linear, but not suitable in such form to model processes because they contain a significant number of simplifying hypotheses which are insufficient for the design of reliable controllers. The absence of robustness with respect to system parameters does not ensure the performance specifications of the control system knowing that the nominal parametric state rarely corresponds to the real one. For these raisons, it is beneficial to use a specific technique to characterize accurately system dynamics in an entirely uncertain environment. In this work, we present an approach to approximate and validate over a large operating range the dynamic behaviour of a Three Tank System benchmark based on a radial basis function neural network (RBFNN). The proposed RBFNN is applied to solve the parametric-identification problems for nonlinear and complex system by using a modified DIRECT algorithm to search the network parameters. The learning algorithm is developed by combining the DIRECT algorithm and a linear regression for fast convergence. Different experimental results have been performed to show the effectiveness of the RBFNN model to emulate the dynamic behaviour of the nonlinear and complex system under different situations.

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Section: The Proposed Methodologymentioning

confidence: 99%

A radial basis function neural network optimized through modified DIRECT algorithm based-model for a three interconnected water tank

Kouadri

Chiter

Zelmat

2010

Journal of Discrete Mathematical Sciences and Cryptography

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“…The number of hidden RBF units is an important factor to determine the predictive properties of the network. The number of hidden units is calculated automatically until the expected error value is achieved (Wu et al, 2009). The neural network uses various numbers of RBF units to evaluate the best predictive property.…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

“…The applications of neural networks upon the vibration of mechanical systems are given herein. Wu et al (2009) have presented an expert system for scooter fault diagnosis using sound emission signals based on adaptive order tracking and an artificial neural network (ANN). The different faults have presented different order figures and they have been used to determine the fault in mechanical systems.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the vibration characteristics of an experimental mechanical system using neural networks

Erkaya

2011

Journal of Vibration and Control

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This paper presents an investigation on the vibration analysis of a gearing mechanism using neural network predictors. The experimental system is positioned on a working table with changeable legs. The legs have different shapes such as L, H and O shapes, for finding the exact leg profiles for the experimental system. Two types of neural networks are used to predict vibrations of the system for different leg profiles. The results of two approaches indicate that the proposed neural network with Levenberg–Marquardt learning algorithm has a superior performance to predict vibration parameters of the system.

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Real-Time Acoustic Emission Characterization of Cracks in TBCs

Zhou¹,

Yang²,

Zhu³

2022

Thermal Barrier Coatings: Failure Theory and Evaluation Technology

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A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network

Cited by 15 publications

References 16 publications

A radial basis function neural network optimized through modified DIRECT algorithm based-model for a three interconnected water tank

A radial basis function neural network optimized through modified DIRECT algorithm based-model for a three interconnected water tank

Analysis of the vibration characteristics of an experimental mechanical system using neural networks

Real-Time Acoustic Emission Characterization of Cracks in TBCs

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