A dynamic spectrum loss generative adversarial network for intelligent fault diagnosis with imbalanced data

Wang, Xin; Jiang, Hongkai; Liu, Yunpeng; Liu, Shaowei; Yang, Qiao

doi:10.1016/j.engappai.2023.106872

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…DDT-DSLGAN: Wang et al [38] proposed a data generation network dynamic spectrum loss generative adversarial network (DSLGAN) for intelligent fault diagnosis of imbalanced data. This data generation network is used to replace the deep data model in the DDT model for comparison of digital twin technology.…”

Section: Experiments 3-validating the Performance Of The Ssaementioning

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: Experiments 3-validating the Performance Of The Ssaementioning

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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Fault identification of product design using fuzzy clustering generative adversarial network (FCGAN) model

Wang,

Xue

2024

Soft Comput

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A novel adaptive cost-sensitive convolution neural network based dynamic imbalanced fault diagnosis framework for manufacturing processes

Ma,

Shi,

Peng

2024

Eng. Res. Express

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Due to the influences of sensor faults, communication lines, and human factors, it is difficult to collect and label fault data in large quantities, resulting in the imbalance between normal and fault data, and between fault and fault data. Those kinds of data imbalances violate the assumption of relatively balanced distribution of most traditional fault diagnosis methods. Associated with those trends, some imbalanced fault diagnosis methods have been put forward. However, most of those methods only consider that the proportion of various samples remains unchanged, that is, the imbalance rate is stable. In the actual manufacturing processes, the industrial data flows are fast, continuous, and dynamically changing. The imbalance rates of all kinds of samples often change continuously, showing the dynamic imbalanced characteristic. To solve this problem, a novel adaptive cost-sensitive convolution neural network based dynamic imbalanced fault diagnosis framework is designed for manufacturing processes. More specifically, a new adaptive cost-sensitive convolutional neural network is firstly constructed by coordinating the cross entropy loss function with a specific cost sensitive index, of which the dynamic imbalance rates and the diagnosis performance indicators are comprehensively considered. Subsequently, a dynamic time factor is reasonably designed and introduced to make the diagnosis model pay more attention to identification of new fault data in the industrial data flow, aiming at improving the fault diagnosis performance. Finally, sufficient simulation experiments are conducted by a typical manufacturing process, the hot rolling process, to demonstrate the superiority of the proposed framework compared with some classical algorithms.

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A dynamic spectrum loss generative adversarial network for intelligent fault diagnosis with imbalanced data

Cited by 3 publications

References 36 publications

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

Fault identification of product design using fuzzy clustering generative adversarial network (FCGAN) model

A novel adaptive cost-sensitive convolution neural network based dynamic imbalanced fault diagnosis framework for manufacturing processes

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