A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
Fault diagnosis in rotating machines based on transfer learning: Literature review
With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …
significant attention in recent years. However, traditional data-driven diagnosis approaches …
Novel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain
Unsupervised cross-domain fault diagnosis of bearings has practical significance; however,
the existing studies still face some problems. For example, transfer diagnosis scenarios are …
the existing studies still face some problems. For example, transfer diagnosis scenarios are …
Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing
Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which
plays a vital role in guaranteeing the reliability, safety, and economical efficiency of …
plays a vital role in guaranteeing the reliability, safety, and economical efficiency of …
Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds
The existing deep transfer learning-based intelligent fault diagnosis studies for machinery
mainly consider steady speed scenarios, and there exists a problem of low diagnosis …
mainly consider steady speed scenarios, and there exists a problem of low diagnosis …
Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects
Y Feng, J Chen, J **e, T Zhang, H Lv, T Pan - Knowledge-Based Systems, 2022 - Elsevier
The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …
strong capability of automatic feature extraction and accurate identification for fault signals …
Supervised contrastive learning-based domain adaptation network for intelligent unsupervised fault diagnosis of rolling bearing
Fault diagnosis of rolling bearing is essential to guarantee production efficiency and avoid
catastrophic accidents. Domain adaptation is emerging as a critical technology for the …
catastrophic accidents. Domain adaptation is emerging as a critical technology for the …
A bearing fault diagnosis method without fault data in new working condition combined dynamic model with deep learning
K Xu, X Kong, Q Wang, S Yang, N Huang… - Advanced Engineering …, 2022 - Elsevier
Bearing fault diagnosis plays an important role in rotating machinery equipment's safe and
stable operation. However, the fault sample collected from the equipment is seriously …
stable operation. However, the fault sample collected from the equipment is seriously …
Self-supervised signal representation learning for machinery fault diagnosis under limited annotation data
Recently, convolutional neural networks (CNNs) have achieved remarkable success in
machinery fault diagnosis. However, these methods usually require mass of manually …
machinery fault diagnosis. However, these methods usually require mass of manually …
Unsupervised health indicator construction by a novel degradation-trend-constrained variational autoencoder and its applications
Health indicator (HI) affects the accuracy and reliability of the remaining useful life (RUL)
prediction model. The hidden variables of variational autoencoder (VAE) can represent the …
prediction model. The hidden variables of variational autoencoder (VAE) can represent the …