An Online Multiple Open-Switch Fault Diagnosis Method for T-Type Three-Level Inverters Based on Multimodal Deep Residual Filter Network

Xing, Zhikai; He, Yigang; Zhang, Weiwei

doi:10.1109/tie.2022.3222663

Cited by 20 publications

(2 citation statements)

References 25 publications

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“…In [ 18 ], a one-dimensional CNN with an improved stochastic gradient optimization method is introduced for extracting and classifying inverter fault features. In [ 19 ], a multimodal deep residual filter network (DRFN) is proposed, achieving a 99.18% accuracy rate in identifying open-circuit faults in T-type three-level inverters. However, this approach requires the collection of voltage and current data and involves a complex model with high computational demands.…”

Section: Introductionmentioning

confidence: 99%

An Open-Circuit Fault Diagnosis Method for Three-Level Neutral Point Clamped Inverters Based on Multi-Scale Shuffled Convolutional Neural Network

Yan,

Wu,

Cao

et al. 2024

Sensors

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This study constructs a power switching device open-circuit fault diagnosis model for a three-level neutral point clamped inverter based on the multi-scale shuffled convolutional neural network (MSSCNN) and extracts and classifies the fault information contained in the output current of inverters. The model employs depthwise separable convolution and channel shuffle techniques to improve diagnostic accuracy and reduce model complexity. The experimental results show that the new model has lower model complexity, better noise resistance and higher average diagnostic accuracy compared with fault diagnosis models based on CNN, ResNet, ShuffleNet V2 and Mobilenet V3 networks.

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Section: Introductionmentioning

confidence: 99%

An Open-Circuit Fault Diagnosis Method for Three-Level Neutral Point Clamped Inverters Based on Multi-Scale Shuffled Convolutional Neural Network

Yan,

Wu,

Cao

et al. 2024

Sensors

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show abstract

“…This sophisticated model serves as the foundation for subsequent stages, where the compressed model is meticulously crafted using an array of techniques. These techniques, including knowledge distillation [11], network pruning [12], quantization [13], and low-rank factorization [14] are strategically employed to systematically reduce the size and computational demands of the model. The artful application of these methods ensures that the compressed model maintains a delicate balance, preserving its overall performance even as it undergoes a process of size and computational optimization.…”

Section: Introductionmentioning

confidence: 99%

Wave-ConvNeXt: An Efficient and Precise Fault Diagnosis Method for IIoT Leveraging Tailored ConvNeXt and Wavelet Transform

Zhang,

Lin,

Yang

et al. 2024

IEEE Internet Things J.

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The burgeoning field of the Industrial Internet of Things (IIoT) necessitates advanced fault diagnosis methods capable of navigating the dual challenges of high predictive accuracy and the constraints of edge computing environments. Our study introduces Wave-ConvNeXt, a novel fault diagnosis model that seamlessly integrates the state-of-the-art ConvNeXt architecture with Wavelet Transform. This innovative model stands out for its lightweight design yet delivers exceptional accuracy in fault diagnosis. In Wave-ConvNeXt, we re-engineer the ConvNeXt model for IIoT applications by adopting onedimensional convolution, tailored for processing high-frequency, non-periodic inputs. This adaptation is complemented by replacing the traditional "patchify" layer with a Wavelet transform layer, which simplifies input signals into sub-signals, thereby easing learning complexities and diminishing the dependence on elaborate deep architectures. Further enhancing this model, we incorporate a squeeze-and-excitation module, enriching its ability to prioritize channel-wise feature relevance, akin to self-attention mechanisms. This integration is rigorously validated through an ablation study. Wave-ConvNeXt epitomizes a holistic approach, enabling an end-to-end optimization of feature learning and fault classification. Our empirical analysis on two real-world IIoT datasets demonstrates Wave-ConvNeXt's superiority over existing models. It not only elevates prediction accuracy but also significantly curtails computational complexity. Additionally, our exploration into the impact of various mother wavelets reveals the effectiveness of using wavelet basis functions with smaller support, bolstering diagnostic precision. The source code of Wave-ConvNeXt is available at https://github.com/leviszhang/waveConvNeXt.

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Characteristics of Common-Mode Voltage Offset in Small Sectors and OC Fault Diagnosis Method for Three-Level Inverter

Song,

Wang,

Guo

et al. 2023

J Fail. Anal. and Preven.

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An Online Multiple Open-Switch Fault Diagnosis Method for T-Type Three-Level Inverters Based on Multimodal Deep Residual Filter Network

Cited by 20 publications

References 25 publications

An Open-Circuit Fault Diagnosis Method for Three-Level Neutral Point Clamped Inverters Based on Multi-Scale Shuffled Convolutional Neural Network

An Open-Circuit Fault Diagnosis Method for Three-Level Neutral Point Clamped Inverters Based on Multi-Scale Shuffled Convolutional Neural Network

Wave-ConvNeXt: An Efficient and Precise Fault Diagnosis Method for IIoT Leveraging Tailored ConvNeXt and Wavelet Transform

Characteristics of Common-Mode Voltage Offset in Small Sectors and OC Fault Diagnosis Method for Three-Level Inverter

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