An Experimental Setup to Detect the Crack Fault of Asymmetric Rotors Based on a Deep Learning Method

Wang, Chong-Yu; Zheng, Zhaoli; Guo, Ding; Xie, Yonghui; Xie, Yonghui; Zhang, Di

doi:10.3390/app13031327

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…On the other hand, we have the following articles. In [12], it is presented that crack is a common fault of rotor systems. The research on crack fault detection methods is mainly divided into numerical and experimental studies.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for the Control of Speed of the Bearing Motor with Winding Split Using DSP

Dantas Neto,

Lopes,

Fonsêca

et al. 2024

Energies

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This article describes the study and digital implementation of a system onboard a TMS 3208F28335 ® DSP for vector control of the bearing motor speed with four poles split winding with 250 W of power. Smart techniques: ANFIS and Neural Networks were investigated and computationally implemented to evaluate the bearing motor performance under the following conditions: operating as an estimator of uncertain parameters and as a speed controller. Therefore, the MATLAB program and its toolbox were used for the simulations and the parameter adjustments involving the structure ANFIS (Adaptive-Network-Based Fuzzy Inference System) and simulations with the Neural Network. The simulated results showed a good performance for the two techniques applied differently: the estimator and a speed controller using both a model of the induction motor operating as a bearing motor. The experimental part for velocity vector control uses three control loops: current, radial position, and speed, where the configurations of the peripherals, that is, the interfaces or drivers for driving the bearing motor.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for the Control of Speed of the Bearing Motor with Winding Split Using DSP

Dantas Neto,

Lopes,

Fonsêca

et al. 2024

Energies

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“…In recent years, the effective application of artificial intelligence methods and neural networks for the study of vibrations in order to detect cracks has been growing [6][7][8]. Experimental studies of the vibro-acoustic characteristics of rotors, bearings, gearboxes and other machine elements based on deep learning make it possible to effectively diagnose the condition and implement fault detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

Experimental studies of noise and vibration during milling of cast iron parts

et al. 2023

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Machining processes of cast iron parts are accompanied by significant noise and vibration. Exceeding the standard values of vibration and noise adversely affects the health of the operator and reduces the quality of the final product. The article deals with the scientific problem associated with the study of the causes of increased vibrations and noise during the milling of cast iron products. It has been experimentally shown that the existing ones create increased sound pressure in the range from 250 to 8000 Hz, while the sound pressure level reaches 80-92 dBa, which is 15-20 dBa higher than the maximum allowable. In the higher frequency part of the spectrum, the decrease in the intensity of sound radiation is from 4 to 7 dB per octave. The data obtained are the initial information for the calculation of noise reduction systems, based on compliance with sanitary standards. To simplify engineering calculations, vibrational energy loss coefficients for cast iron solid and hollow products are given.

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“…As for the non-traditional methods, the convolutional neural network was adopted for crack localization via vibration responses in time domain from an experimental rig in [39]. The convolutional neural network and deep metric learning method was proposed for crack position diagnosis in a hollow shaft rotor system using the amplitude-frequency responses [40].…”

Section: Introductionmentioning

confidence: 99%

An Improved Crack Breathing Model and Its Application in Crack Identification for Rotors

Liu

Cao

2023

Machines

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The crack breathing model and crack identification method for rotors using nonlinearity induced by cracks are studied in this work. Firstly, the finite element method is utilized to model a rotor–bearing system with a response-dependent breathing crack to obtain the numerical data for crack identification. During the modelling, an improved breathing crack model is proposed, focused on the unreasonable assumption about crack closure line in the original crack closure line position (CCLP) model. Compared with the original model, the improved breathing model can reflect the nonlinear behavior of cracks better. Secondly, based on the established model, super-harmonic features at 1/3 and 1/2 of the critical rotating speeds under different crack locations and crack depths are extracted for crack identification. Additionally, the super-harmonic features from two measurement points are used as inputs into an artificial neural network with a Levenberg–Marquardt back-propagation algorithm, corresponding crack positions and depths as outputs. The robustness of the method is tested by examining the identification results under different levels of noise. The results show that the proposed crack identification method is efficient for simultaneous identification of crack depth and position in operating rotors.

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An Experimental Setup to Detect the Crack Fault of Asymmetric Rotors Based on a Deep Learning Method

Cited by 11 publications

References 30 publications

Artificial Intelligence for the Control of Speed of the Bearing Motor with Winding Split Using DSP

Artificial Intelligence for the Control of Speed of the Bearing Motor with Winding Split Using DSP

Experimental studies of noise and vibration during milling of cast iron parts

An Improved Crack Breathing Model and Its Application in Crack Identification for Rotors

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