Real-time detection of anomalies in data streams is a foundation of modern applied analysis in complex systems. It enables experts to design rapid, efficient, reliable, and high-performance decision ...

The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised ...

Abstract: Unsupervised anomaly detection (AD) methods based on deep learning have attracted great attention in unlabeled data mining. The performance of these AD methods usually depends on the ...

Crimp force monitoring (CFM) has long been the standard for fault detection in wire assemblies. The technique can reliably detect many defects, including wrong strip length, missing strands, wrong ...

Autoencoders are a class of neural networks that aim to learn efficient representations of input data by encoding and then reconstructing it. They comprise two main parts: the encoder, which ...

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Fault detection is an essential task for large-scale industrial maintenance. However, in practical applications, due to the possible harm caused by the collection of fault data, the fault samples that ...