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Including sparse production knowledge into variational autoencoders to increase anomaly detection reliability

T Hammerbacher… - 2021 IEEE 17th …, 2021 - ieeexplore.ieee.org

Digitalization leads to data transparency for production systems that we can benefit from with
data-driven analysis methods like neural networks. For example, automated anomaly …

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Benchmarking Sensor Modalities with Few-shot Domain Adaptation for Cross-Domain Fault Diagnosis^*

M Usman, T Komatsu, KM Htun, Z Liu… - 2024 IEEE 20th …, 2024 - ieeexplore.ieee.org

Rotating machine fault diagnosis is challenging due to the inevitable differences in the
operating conditions of source and target machines which significantly impact diagnostic …

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Cross-domain fault diagnosis with one-dimensional convolutional neural network

Including sparse production knowledge into variational autoencoders to increase anomaly detection reliability

Benchmarking Sensor Modalities with Few-shot Domain Adaptation for Cross-Domain Fault Diagnosis^*

Cross-domain fault diagnosis with one-dimensional convolutional neural network

Including sparse production knowledge into variational autoencoders to increase anomaly detection reliability

Benchmarking Sensor Modalities with Few-shot Domain Adaptation for Cross-Domain Fault Diagnosis*

Benchmarking Sensor Modalities with Few-shot Domain Adaptation for Cross-Domain Fault Diagnosis^*