Fault Diagnosis of Cascaded Multilevel Inverter Using Multiscale Kernel Convolutional Neural Network

Sivapriya, A.; Kalaiarasi, N.; Verma, Rajesh; Bharatiraja, C.; Munda, Josiah L.

doi:10.1109/access.2023.3299852

Cited by 9 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This technique significantly reduces the number of model parameters, resulting in faster and more efficient model calculations and training. CNN exhibits strong fault tolerance, parallel processing, and self-learning capabilities, enabling it to handle samples with faults and distortions [21]. A onedimensional CNN is chosen to train and analyze wind turbine bearing fault vibration samples, as the vibration signal from the bearing is one-dimensional data.…”

Section: Cnn Based Wind Bearing Fault Diagnosis Cnn Model Constructionmentioning

confidence: 99%

A Deep Learning Fault Diagnose Method for Turbine Bearing: Digital Twin Mechanism

Islam,

Sarwar,

Hamza

et al. 2024

ejaset

View full text Add to dashboard Cite

This study presents a digital twin (DT) based wind turbine bearing fault diagnosis approach to address the issues of insufficient fault sample size and inaccurate diagnosis. To assist in diagnosing bearing faults in wind turbines, a DT system was built. Bearing vibration signal enhancement processing, which is based on the Hilbert-Huang transform, is used to improve the data samples of vibration signals and decrease the noise in these signals. In order to diagnose bearing defects in wind turbines, a convolutional neural network model was trained and tested using data-enhanced samples. The experimental results showed that the suggested method is feasible and effective, increased the stability and accuracy of defect diagnosis in wind turbine bearings, and solved the problem of data augmentation in one-dimensional vibration signals.

show abstract

Section: Cnn Based Wind Bearing Fault Diagnosis Cnn Model Constructionmentioning

confidence: 99%

A Deep Learning Fault Diagnose Method for Turbine Bearing: Digital Twin Mechanism

Islam,

Sarwar,

Hamza

et al. 2024

ejaset

View full text Add to dashboard Cite

show abstract

Comprehensive analysis of faults and diagnosis techniques in cascaded multi-level inverters

Gatla,

Kumar,

Shashavali

et al. 2024

Heliyon

View full text Add to dashboard Cite

Lightweight MDSCA-Net: an end-to-end CAN bus fault diagnosis framework

Lu,

Huang,

Liu

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Controller area network (CAN) buses are widely used as low-cost, highly flexible field buses in various scenarios, such as in vehicle networks for automobiles and communication networks for industrial sites. They typically operate in harsh environments,and faults inevitably occur. CAN bus faults cannot be efficiently diagnosed via traditional manual detection. Herein, we proposea lightweight MDSCA-Net for CAN bus fault diagnosis. Deep separable convolution (DSConv) is used in the model instead ofordinary convolution to reduce the number of parameters and floating-point operations. Additionally, the noise immunity of themodel is improved by designing a multiscale denoising module (MDM). A multiscale deep separable convolutional fusion SE at tention (MDSCSA) module is designed to capture the channel dimension details of the features. Furthermore, a spatial attentionmodule (SAM) is utilized to capture the spatial dimension details of the features. Finally, a residual (Res) module stabilizes themodel performance. Experimental results on the CAND dataset indicated that the proposed method achieved a diagnostic accuracyof 99% in a noise-free environment, and compared with other fault diagnosis methods, it had better noise immunity and robustnessin a noisy environment, which is of considerable practical significance for ensuring the stable operation of CAN buses.

show abstract

Fault Diagnosis of Cascaded Multilevel Inverter Using Multiscale Kernel Convolutional Neural Network

Cited by 9 publications

References 43 publications

A Deep Learning Fault Diagnose Method for Turbine Bearing: Digital Twin Mechanism

A Deep Learning Fault Diagnose Method for Turbine Bearing: Digital Twin Mechanism

Comprehensive analysis of faults and diagnosis techniques in cascaded multi-level inverters

Lightweight MDSCA-Net: an end-to-end CAN bus fault diagnosis framework

Contact Info

Product

Resources

About