Multiview Shapelet Prototypical Network for Few-Shot Fault Incremental Learning
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2024.3413304
Abstract: Few-shot new faults are constantly emerging due to the dynamic environments and operations in the industrial process. It is a challenge for existing fault diagnosis methods to diagnose few-shot new faults without forgetting old faults… read more here.
Keywords: fault incremental; multiview shapelet; shot fault; shapelet prototypical ... See more keywords
Domain-Knowledge-Driven Intelligent Attribute Definition for Zero-Shot Fault Diagnosis of Bearings
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2025.3552711
Abstract: To address the issue in zero-shot fault diagnosis (ZSFD) where fault attribute definitions (FADs) rely heavily on manual design and the accuracy of FAD depends on the expertise of developers, this article embedded expert knowledge… read more here.
Keywords: zero shot; shot fault; fault diagnosis; fault ... See more keywords
Diffusion-Enhanced Dual-Domain Adversarial Network: A Zero-Shot Fault Diagnosis Method for Electrohydrostatic Actuators
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2025.3595601
Abstract: To address the zero-shot fault diagnosis challenge in electrohydrostatic actuator (EHA), a diffusion-enhanced dual-domain adversarial network (DEDDAN) is proposed. Acquired experimental signals were stacked and reorganized in the time domain into grayscale images. A denoising… read more here.
Keywords: diffusion enhanced; zero shot; fault diagnosis; shot fault ... See more keywords
Reweighted Regularized Prototypical Network for Few-Shot Fault Diagnosis.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3232394
Abstract: In this article, we study the challenging few-shot fault diagnosis (FSFD) problem where limited faulty samples are available. Metric-based meta-learning methods have been a prevalent approach toward FSFD; however, most of them rely on learning… read more here.
Keywords: fault diagnosis; shot fault; regularized prototypical; reweighted regularized ... See more keywords