Research on the detection of axle abnormal noise based on maximum autocorrelation kurtosis deconvolution

Ni, Zexing; He, Dan; Wang, Xiufeng; Zhang, Ying

doi:10.1016/j.apacoust.2023.109228

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…In this section, a ResNet convolutional neural network model improved by SE module is used to extract depth features [26,27]. As the number of layers increases, parameters increase and model complexity increases, serious problems such as overfitting, gradient disappearance and gradient explosion will occur in common neural networks.…”

Section: Se-resnet18 Convolutional Neural Networkmentioning

confidence: 99%

Wind turbines fault diagnosis method under variable working conditions based on AMVMD and deep discrimination transfer learning network

Shi,

Jia,

et al. 2024

Meas. Sci. Technol.

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With the wide application of wind turbines, the bearing fault diagnosis of wind turbines has become a research hotspot. Under complex variable working conditions, the vibration signals of bearing components show non-stationary characteristics. Therefore, it is challenging to extract fault features using typical fault diagnosis methods. This paper proposes Adaptive Multivariate Variational Mode Decomposition combined with an improved Deep Discrimination Transfer Learning Network (AMVMD-IDDTLN) for bearing fault diagnosis of wind turbines under variable working conditions. First, the AMVMD method is used for the adaptive decomposition of the original signal, and use SE-ResNet18 convolutional neural network to obtain the transfer features of the source domain and target domain. Then, marginal distribution differences and conditional differences are assessed by DDM measures. The whole model is optimized by cross-entropy and IJDA loss function, and the identification and classification of cross-working fault characteristics of the wind turbine- bearings are realized. The model achieves 99.48% transfer learning for the ten classifications of CWRU data set, 97% transfer learning for the four classifications of UPB data set, and 90% transfer learning for wind turbine bearing data across working conditions and across equipment. It is concluded that: Compared with similar models, the AMVMD-IDDTLN model proposed in this paper has higher diagnostic accuracy and faster convergence rate, which has certain practicality.

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Section: Se-resnet18 Convolutional Neural Networkmentioning

confidence: 99%

Wind turbines fault diagnosis method under variable working conditions based on AMVMD and deep discrimination transfer learning network

Shi,

Jia,

et al. 2024

Meas. Sci. Technol.

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“…With the advancement of science and technology, the dynamic properties of mechanical systems, structures, and products' have significantly improved, and their vibration and noise issues have gradually been effectively controlled. The focus of scholars on mechanical system vibration and noise has begun to change qualitatively, and product design has shifted from how to minimize noise to how to design for and control sound quality [1][2][3]. Sound quality is an acoustic indicator reflecting human subjective feelings, and includes loudness, roughness, sharpness, restlessness, etc.…”

Section: Introductionmentioning

confidence: 99%

Sound Quality Performance of Orthogonal Antisymmetric Composite Laminates Embedded with SMA Wires

Huang

Wang

et al. 2023

Materials

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Orthogonal antisymmetric composite laminates embedded with shape memory alloys (SMAs) wires have the potential to improve the sound quality of vibro-acoustics by taking advantage of the special superelasticity, temperature phase transition, and pre-strain characteristics of SMAs. In this research, space discretion and mode decoupling were employed to establish a vibro-acoustic sound quality model of SMA composite laminates. The association between the structural material parameters of SMA composite laminates and the sound quality index is then approached through methodologies. Numerical analysis was implemented to discuss the effects of SMA tensile pre-strain, SMA volume fraction, and the ratio of resin-to-graphite in the matrix on the vibro-acoustic sound quality of SMA composite laminates within a temperature environment. Subsequently, the sound quality test for SMA composite laminates is thus completed. The theoretically predicted value appears to agree well with the experimental outcomes, which validates the accuracy and applicability of the dynamic modeling theory and method for the sound quality of SMA composite laminates. The results indicate that attempting to alter the SMA tensile pre-strain, SMA volume fraction, and matrix material ratio can be used to modify loudness, sharpness, and roughness, which provides new ideas and a theoretical foundation for the design of composite laminates with decent sound quality.

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Morphological dictionary learning based sparse classification for small electric motor state recognition under unbalanced samples

Xue,

He,

et al. 2025

Applied Acoustics

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Research on the detection of axle abnormal noise based on maximum autocorrelation kurtosis deconvolution

Cited by 4 publications

References 27 publications

Wind turbines fault diagnosis method under variable working conditions based on AMVMD and deep discrimination transfer learning network

Wind turbines fault diagnosis method under variable working conditions based on AMVMD and deep discrimination transfer learning network

Sound Quality Performance of Orthogonal Antisymmetric Composite Laminates Embedded with SMA Wires

Morphological dictionary learning based sparse classification for small electric motor state recognition under unbalanced samples

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