Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network

Jin, Yuhong; Hou, Lei; Lu, Zhenyong; Chen, Yushu

doi:10.1186/s10033-023-00856-y

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Moreover, NNs have the advantage of the ability of generalization and adaptation [27][28][29]. PR-NNs can produce good classification results in different fields like collision detection in robots [14] and the diagnosis of crack faults [30].…”

Section: Experimental Workmentioning

confidence: 99%

Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot

Mahmoud,

Abdel-Jaber,

Sharkawy

2024

Automation

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In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no collisions occur. The other three indicators predict collisions on the three links of the manipulator. The input data to train the PR-NN model are the values of torque exerted by the joints. The output of the model predicts and identifies the link on which the collision occurs. In our previous work, the position data for a 3-DOF robot were used to estimate the external collision torques exerted by the joints when applying collisions on each link, based on a recurrent neural network (RNN). The estimated external torques were used to design the current PR-NN model. In this work, the PR-NN model, while training, could successfully classify 56,592 samples out of 56,619 samples. Thus, the model achieved overall effectiveness (accuracy) in classifying collisions on the robot of 99.95%, which is almost 100%. The sensitivity of the model in detecting collisions on the links “Link 1, Link 2, and Link 3” was 97.9%, 99.7%, and 99.9%, respectively. The overall effectiveness of the trained model is presented and compared with other previous entries from the literature.

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Section: Experimental Workmentioning

confidence: 99%

Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot

Mahmoud,

Abdel-Jaber,

Sharkawy

2024

Automation

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Game analysis of product specifications for design optimisation using big sales data

Zhang,

Gao,

Simeone

et al. 2023

Journal of Engineering Design

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Research on the prediction method of wing structure noise based on the combination of conditional generative adversarial neural network and numerical methods

Jiang,

Liang,

Cheng

et al. 2024

Front. Phys.

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This paper presents a technique for predicting noise generated by airfoil structures that combines deep learning techniques with traditional numerical methods. In traditional numerical methods, accurately predicting the noise of airfoil structures requires significant computational resources, making it challenging to perform low-noise optimization design for these structures. To expedite the prediction process, this study introduces Conditional Generative Adversarial Networks (CGAN). By replacing the generator and discriminator of CGAN with traditional regression neural network models, the suitability of CGAN for regression prediction is ensured. In this study, the data computation was accelerated by expanding the kernel function in the traditional boundary element method using a Taylor series. Based on the resulting data, an alternative predictive model for wing structure noise was developed by integrating Conditional Generative Adversarial Networks (CGAN). Finally, the effectiveness and feasibility of the proposed method are demonstrated through three case studies.

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Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network

Cited by 3 publications

References 30 publications

Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot

Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot

Game analysis of product specifications for design optimisation using big sales data

Research on the prediction method of wing structure noise based on the combination of conditional generative adversarial neural network and numerical methods

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