Edge computing on IoT for machine signal processing and fault diagnosis: A review
Edge computing is an emerging paradigm that offloads the computations and analytics
workloads onto the Internet of Things (IoT) edge devices to accelerate the computation …
workloads onto the Internet of Things (IoT) edge devices to accelerate the computation …
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 …
Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects
Y Feng, J Chen, J Xie, 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 …
Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks
In recent years, intelligent data-driven prognostic methods have been successfully
developed, and good machinery health assessment performance has been achieved …
developed, and good machinery health assessment performance has been achieved …
Federated transfer learning for bearing fault diagnosis with discrepancy-based weighted federated averaging
Generally, high performance of deep learning (DL)-based machinery fault diagnosis
methods relies on abundant labeled fault samples under various working conditions, while …
methods relies on abundant labeled fault samples under various working conditions, while …
Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions
Federated learning has been receiving increasing attention in the recent years, which
improves model performance with data privacy among different clients. The intelligent fault …
improves model performance with data privacy among different clients. The intelligent fault …
Targeted transfer learning through distribution barycenter medium for intelligent fault diagnosis of machines with data decentralization
Deep transfer learning-based fault diagnosis of machines is achieved based on the
assumption that the source and target domain data could be centralized to assess the …
assumption that the source and target domain data could be centralized to assess the …
Spatial–Temporal Federated Transfer Learning with multi-sensor data fusion for cooperative positioning
With the development of advanced embedded and communication systems, location
information has become a crucial factor in supporting context-aware or location-aware …
information has become a crucial factor in supporting context-aware or location-aware …
Multi-source weighted source-free domain transfer method for rotating machinery fault diagnosis
The mainstream approach to addressing the issues of insufficient historical data and high
annotation costs in the domain of rotating machinery is to build transfer learning models …
annotation costs in the domain of rotating machinery is to build transfer learning models …
Multi-hop graph pooling adversarial network for cross-domain remaining useful life prediction: A distributed federated learning perspective
Accurate remaining useful life (RUL) prediction has gained increasing attention in modern
maintenance management. Considering the data privacy requirements of distributed multi …
maintenance management. Considering the data privacy requirements of distributed multi …