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Found 5 papers, 1 papers with code
TDANet: A Novel Temporal Denoise Convolutional Neural Network With Attention for Fault Diagnosis
Fault diagnosis plays a crucial role in maintaining the operational integrity of mechanical systems, preventing significant losses due to unexpected failures.
Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge
The multi-scale uncertainty analysis is obtained by using the distribution characteristics of global and local latent variables.
GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving
Yet, their proficiency in tackling geometry math problems, which necessitates an integrated understanding of both textual and visual information, has not been thoroughly evaluated.
Fault Detection and Classification of Aerospace Sensors using a VGG16-based Deep Neural Network
In this paper, we first propose a data augmentation method which inflates the stacked image to a larger size (correspondent to the VGG16 net developed in the machine vision realm).
Augmented Imagefication: A Data-driven Fault Detection Method for Aircraft Air Data Sensors
In this paper, a novel data-driven approach named Augmented Imagefication for Fault detection (FD) of aircraft air data sensors (ADS) is proposed.
