On Securing the Global Economical Dispatch in DC Microgrid Clusters: An Event-Driven Approach

Jena, Satabdy; Padhy, Narayana Prasad; Srivastava, Anurag K.

doi:10.1109/tase.2023.3330928

IEEE Trans. Automat. Sci. Eng.

2024

DOI: 10.1109/tase.2023.3330928

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On Securing the Global Economical Dispatch in DC Microgrid Clusters: An Event-Driven Approach

Satabdy Jena,

Narayana Prasad Padhy,

Anurag K. Srivastava

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An adaptive thresholding approach for open set fault diagnosis

Wang,

Cheng,

Deng

2024

Meas. Sci. Technol.

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Cross-domain fault diagnosis using deep learning plays a critical role in ensuring the reliability and safety of mechanical systems. However, real-world industrial scenarios often involve unknown fault classes, which introduce significant challenges beyond environmental differences between training and testing phases. These unknown fault classes, which do not appear in the training data, create a cross-domain open set fault diagnosis problem where the target domain includes both known and unknown fault types with distinct distribution characteristics. Traditional domain adaptation methods that align source and target domains often overlook the spatial distribution of each class in the feature space, leading to potential negative transfer and misclassification of unknown faults. To address these challenges, this paper proposes a k-Nearest Neighbors based Adaptive Thresholding (KNNAT) method, which dynamically adjusts classification thresholds based on the spatial distribution of each class in the feature space. This approach effectively isolates unknown faults, reducing their impact on domain adaptation and improving the reliability of the diagnostic process. Extensive experiments on the publicly available CWRU bearing and PHM09 datasets demonstrate that the proposed KNNAT method outperforms other state-of-the-art methods, achieving higher accuracy and robustness in identifying known faults while successfully isolating unknown faults. These results highlight the potential of using the KNNAT method to enhance the reliability of mechanical systems in cross-domain fault diagnosis applications.

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An adaptive thresholding approach for open set fault diagnosis

Wang,

Cheng,

Deng

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

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On Securing the Global Economical Dispatch in DC Microgrid Clusters: An Event-Driven Approach

Cited by 1 publication

References 39 publications

An adaptive thresholding approach for open set fault diagnosis

An adaptive thresholding approach for open set fault diagnosis

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