Fault diagnosis of wind turbines with generative adversarial network-based oversampling method

Yang, Shuai; Zhou, Yifei; Chen, Xu; Deng, Chao; Li, Chuan

doi:10.1088/1361-6501/acad20

Cited by 14 publications

(8 citation statements)

References 23 publications

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“…In order to reduce bias errors caused by model imbalance and improve diagnostic accuracy, deep learning technology serves as a powerful data augmentation method, providing an effective solution for monitoring and fault diagnosis of PV systems. Its strong feature extraction capabilities enable automatic collection and enhancement or reconstruction of sample distribution characteristics, making it perform exceptionally well in handling complex data from PV systems [25,26]. Among these, generative adversarial networks (GANs) serve as powerful deep learning generators capable of generating diverse data based on extracted distribution characteristics, thereby improving fault diagnosis accuracy.…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

Liu,

Qi,

et al. 2024

Meas. Sci. Technol.

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The energy loss and substantial costs associated with faults in photovoltaic (PV) systems impose significant limitations on their efficiency and reliability. Addressing current issues in PV fault diagnosis such as the lack of typical fault data, imbalanced data distribution, and poor diagnostic performance, this paper proposes an intelligent fault diagnosis method for PV systems, Deep Digital Twins with Information Gain Stacking Sparse Autoencoders(DDT-IGSSAE).Initially, the method designs a novel DDT modeling framework tailored to actual PV system specifications. This framework utilizes a mechanism simulation model to generate typical data under various states. Simultaneously, a deep data model is constructed to learn the distribution characteristics of the mechanism model and complete data diversification, achieving the fusion and complementation of data from both models. Subsequently, a diagnostic network using IGSSAE is introduced. This network utilizes Information Gain Ratio (IGR) to assess feature classification contributions, enabling automatic feature selection. Based on the input features, a Stacked Sparse Autoencoder (SSAE) fault classification network is designed, incorporating multi-level feature compression to enhance the model's stability and diagnostic accuracy. Finally, a case study is conducted using a 250 kW grid-connected PV system, thoroughly validating the method's effectiveness with a diagnostic accuracy of 98.4%.

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Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

Liu,

Qi,

et al. 2024

Meas. Sci. Technol.

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“…In 2020, Yang Hongjie et al [3] used BEGAN to realize the signal reconstruction of single-carrier BPSK and 8PSK and validated the quality of the reconstructed signal by comparing the symbol rate, spectral characteristics, constellation diagram, and other features of the reconstructed signal with the real signal. However, this scheme encountered challenges in realizing signal reconstruction for a larger number of sampling points.…”

Section: Introductionmentioning

confidence: 99%

TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN

Li,

Fan,

Hou

et al. 2024

Electronics

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Reconstruction techniques for communication signals represent a significant research focus within the field of signal processing. To overcome the difficulty and low precision in reconstructing OFDM signals, we introduce a signal reconstruction technique called TOR-GAN (Transformer-Based OFDM Signal Reconstruction GAN). Reconstructing IQ sequences using a CNN and RNN presents challenges in capturing the correlations between two signals. To tackle this issue, the VIT (vision in transformer) approach was introduced into the discriminator network. The IQ signal is treated as a single-channel, two-dimensional image, divided into blocks of 2 × 2 pixels, with absolute position embedding added. The generator network maps the input noise to the same dimension as the IQ signal dimension × embedding vector dimension and adds two identical position embedding data points to the network learning. In the transformer network, prob sparse attention is employed as a replacement for multi-head attention to tackle the issue of high computational complexity. Finally, combined with the MLP structure, the transformer-based generator and discriminator are designed. The signal similarity evaluation index was constructed, and experiments showed that the reconstructed signal under QPSK and BPSK modulation had good reconstruction quality in the time-domain waveform, constellation diagram, and spectrogram at a high SNR. Compared with other reconstruction algorithms, the proposed algorithm improved the quality of the reconstructed signal while reducing the complexity of the algorithm.

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“…Transfer learning (TL) and generative adversarial networks (GAN) have been introduced to alleviate the issue of data scarcity [7,8]. However, they still have certain limitations in industrial applications.…”

Section: Introductionmentioning

confidence: 99%

A hierarchical transformer-based adaptive metric and joint-learning network for few-shot rolling bearing fault diagnosis

Meng,

Zhang,

Guan

et al. 2023

Meas. Sci. Technol.

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Recently, deep learning techniques have significantly bolstered the advancement of intelligent fault diagnosis. However, in engineering practice, the limited availability of fault samples poses considerable challenges to the existing methods. To address this problem, a hierarchical Transformer-based adaptive metric and joint-learning network (HTAMJN) is suggested in this paper. Firstly, a hierarchical progressive fusion encoder based on orthogonal self-attention is devised, which effectively enhances the model’s attention to local features and establishes comprehensive interdependencies. To reduce the demand for labeled data, a metric method with a coupling mechanism is proposed to adaptively enhance the metric benchmark, making the category representation more discriminative. Additionally, a joint optimization scheme based on the episodic paradigm is formulated to improve the recognition capability of the network for different categories under few-shot scenarios. The outcomes demonstrate that the HTAMJN outperforms several comparison models in both few-shot diagnosis and cross-domain recognition tasks in experiments conducted on three bearing datasets, exhibiting superior performance.

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Fault diagnosis of wind turbines with generative adversarial network-based oversampling method

Cited by 14 publications

References 23 publications

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN

A hierarchical transformer-based adaptive metric and joint-learning network for few-shot rolling bearing fault diagnosis

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