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 …
Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy
Transfer learning can effectively solve the target task identification problem with the
prerequisite of sharing all user data and target data, and has become one of the most …
prerequisite of sharing all user data and target data, and has become one of the most …
[HTML][HTML] Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
A Theissler, J Pérez-Velázquez, M Kettelgerdes… - Reliability engineering & …, 2021 - Elsevier
Recent developments in maintenance modelling fueled by data-based approaches such as
machine learning (ML), have enabled a broad range of applications. In the automotive …
machine learning (ML), have enabled a broad range of applications. In the automotive …
Applications of unsupervised deep transfer learning to intelligent fault diagnosis: A survey and comparative study
Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep
representation learning and plenty of labeled data. However, machines often operate with …
representation learning and plenty of labeled data. However, machines often operate with …
Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching
S Liu, H Jiang, Z Wu, Z Yi, R Wang - Reliability Engineering & System …, 2023 - Elsevier
In the health management of modern rotating machinery, domain adaptation is an effective
method to solve the diagnostic problems of insufficient labeled signals and poor …
method to solve the diagnostic problems of insufficient labeled signals and poor …
Density-based affinity propagation tensor clustering for intelligent fault diagnosis of train bogie bearing
Health monitor of bogie-bearing on the train can ensure constant operation of the rail transit
system. Since the metro or other rail transit have high safety requirements, it is hard to …
system. Since the metro or other rail transit have high safety requirements, it is hard to …
ChatGPT-like large-scale foundation models for prognostics and health management: A survey and roadmaps
PHM technology is vital in industrial production and maintenance, identifying and predicting
potential equipment failures and damages. This enables proactive maintenance measures …
potential equipment failures and damages. This enables proactive maintenance measures …
Multisource domain feature adaptation network for bearing fault diagnosis under time-varying working conditions
Intelligent fault diagnosis methods based on domain adaptation (DA) have been extensively
employed for tackling domain shift problems, and the basic diagnosis tasks under time …
employed for tackling domain shift problems, and the basic diagnosis tasks under time …
[HTML][HTML] Rotating machinery fault detection and diagnosis based on deep domain adaptation: A survey
S Zhang, SU Lei, GU Jiefei, LI Ke, Z Lang… - Chinese Journal of …, 2023 - Elsevier
In practical mechanical fault detection and diagnosis, it is difficult and expensive to collect
enough large-scale supervised data to train deep networks. Transfer learning can reuse the …
enough large-scale supervised data to train deep networks. Transfer learning can reuse the …