Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review
J Yu, Y Zhang - Neural Computing and Applications, 2023 - Springer
Process fault detection and diagnosis (FDD) is a predominant task to ensure product quality
and process reliability in modern industrial systems. Those traditional FDD techniques are …
and process reliability in modern industrial systems. Those traditional FDD techniques are …
A systematic literature review on transfer learning for predictive maintenance in industry 4.0
The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and
digital technologies within industrial production and manufacturing systems. The objective of …
digital technologies within industrial production and manufacturing systems. The objective of …
Deep discriminative transfer learning network for cross-machine fault diagnosis
Many domain adaptation methods have been presented to deal with the distribution
alignment and knowledge transfer between the target domain and the source domain …
alignment and knowledge transfer between the target domain and the source domain …
A survey of transfer learning for machinery diagnostics and prognostics
In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …
components greatly influence operational safety and system reliability. Many data-driven …
Perspectives on nonstationary process monitoring in the era of industrial artificial intelligence
C Zhao - Journal of Process Control, 2022 - Elsevier
The development of the Internet of Things, cloud computing, and artificial intelligence has
given birth to industrial artificial intelligence (IAI) technology, which enables us to obtain fine …
given birth to industrial artificial intelligence (IAI) technology, which enables us to obtain fine …
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 …
A deep probabilistic transfer learning framework for soft sensor modeling with missing data
Soft sensors have been extensively developed and applied in the process industry. One of
the main challenges of the data-driven soft sensors is the lack of labeled data and the need …
the main challenges of the data-driven soft sensors is the lack of labeled data and the need …
Federated zero-shot industrial fault diagnosis with cloud-shared semantic knowledge base
B Li, C Zhao - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
Recently, a considerable literature has grown up around the few-sample fault diagnosis
task, in which few samples of fault data are available for model training. The lack of fault …
task, in which few samples of fault data are available for model training. The lack of fault …
Adaptive intermediate class-wise distribution alignment: a universal domain adaptation and generalization method for machine fault diagnosis
Many transfer learning methods have been proposed to implement fault transfer diagnosis,
and their loss functions are usually composed of task-related losses, distribution distance …
and their loss functions are usually composed of task-related losses, distribution distance …
Deep joint distribution alignment: A novel enhanced-domain adaptation mechanism for fault transfer diagnosis
Various domain adaptation (DA) methods have been proposed to address distribution
discrepancy and knowledge transfer between the source and target domains. However …
discrepancy and knowledge transfer between the source and target domains. However …