Data augmentation strategy for power inverter fault diagnosis based on wasserstein distance and auxiliary classification generative adversarial network

Sun, Quan; Peng, Fei; Yu, Xianghai; Li, Hongsheng

doi:10.1016/j.ress.2023.109360

Cited by 28 publications

(2 citation statements)

References 18 publications

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“…The results demonstrated improved data quality and provided an effective tool for addressing the mentioned challenges. Sun et al [141] tackled the issue of limited fault samples by using GANs to increase the number of fault samples, satisfying the requirements of fault diagnosis. Their approach, called auxiliary classification GAN, utilized the Wasserstein distance as the model's optimization objective, enhancing the quality of generated data.…”

Section: Ganmentioning

confidence: 99%

Application of deep learning to fault diagnosis of rotating machineries

Su,

Xiang,

2024

Meas. Sci. Technol.

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Deep learning has attained remarkable achievements in diagnosing faults for rotary machineries. Capitalizing on the formidable learning capacity of deep learning, it has the potential to automate human labor and augment the efficiency of fault diagnosis in rotary machinery. These advantages have engendered escalating interest over the past decade. Although recent reviews of the literature have encapsulated the utilization of deep learning in diagnosing faults in rotating machinery, they no longer encompass the introduction of novel methodologies and emerging directions as deep learning methodologies continually evolve. Moreover, in practical application, novel issues and trajectories perpetually manifest, demanding a comprehensive exegesis. To rectify this lacuna, this article amalgamates current research trends and avant-garde methodologies while systematizing the utilization of anterior deep learning techniques. The evolution and extant status of deep learning in diagnosing faults for rotary machinery were delineated, with the intent of providing orientation for prospective research. Over the bygone decade, archetypal deep learning theory has empowered the diagnosis of faults in rotating machinery by directly establishing the nexus between mechanical data and fault conditions. In recent years, meta learning methods aimed at solving small sample scenarios and large model transformers aimed at mining big data features have both received widespread attention and development in the field of fault diagnosis of rotating machinery equipment. Although excellent results have been achieved in these two directions, there is no review and summary article yet, so it is necessary to update the review literature in the field of fault diagnosis of rotating machinery equipment. Lastly, predicated on a survey of the literature and the current developmental landscape, the challenges and prospective orientations of deep learning in rotary machinery fault diagnosis are presented.

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Section: Ganmentioning

confidence: 99%

Application of deep learning to fault diagnosis of rotating machineries

Su,

Xiang,

2024

Meas. Sci. Technol.

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“…Therefore, generative adversarial networks (GANs) have been widely developed to generate renewable energy scenarios. GAN models are also adopted for data augmentation [19,20] and real-time control frameworks [21,22]. Scenario generation-related studies have considered GANs [23][24][25], conditional GANs [26][27][28], conditional recurrent GANs [29], convolutional GANs [30], conditional Wasserstein GANs [31], least-square GANs [2], and interpretable GANs [32].…”

Section: Introductionmentioning

confidence: 99%

Long‐term scenario generation of renewable energy generation using attention‐based conditional generative adversarial networks

Li,

Yu,

Liu

et al. 2024

Energy Conversion and Econom

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Long‐term scenario generation of renewable energy is regarded as an important part of the optimal planning of renewable energy systems. This study proposes a scenario generation method for generating long‐term correlated scenarios of wind and photovoltaic outputs from historical renewable energy data. The generation of scenarios was divided into two processes: long‐term yearly sequence generation and intraday scenario generation of wind‐solar energy. In the long‐term yearly sequence generation process, the k‐means clustering algorithm and Markov chain Monte Carlo simulation method were developed to capture the seasonal and long‐term features of wind and photovoltaic energies. Furthermore, an attention‐based conditional generative adversarial network (ACGAN) was proposed to capture short‐term features. An attention structure and conditional classifiers were developed to capture features in the generated scenarios. To accelerate the convergence process and improve the quality of the generated scenarios, a gradient penalty was included in the ACGAN model. Numerical case studies were conducted to verify the validity of the proposed method using a real‐world dataset.

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