Open benchmarks for assessment of process monitoring and fault diagnosis techniques: A review and critical analysis
The present paper brings together openly available datasets and simulators for testing of
process monitoring and fault diagnosis techniques. Some general characteristics of these …
process monitoring and fault diagnosis techniques. Some general characteristics of these …
A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis
In modern industrial equipment maintenance, transfer learning is a promising tool that has
been widely utilized to solve the problem of the insufficient generalization ability of …
been widely utilized to solve the problem of the insufficient generalization ability of …
Adversarial deep transfer learning in fault diagnosis: progress, challenges, and future prospects
Y Guo, J Zhang, B Sun, Y Wang - Sensors, 2023 - mdpi.com
Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the
superiorities of deep learning in feature representation with the merits of transfer learning in …
superiorities of deep learning in feature representation with the merits of transfer learning in …
A universal multi-source domain adaptation method with unsupervised clustering for mechanical fault diagnosis under incomplete data
Recently, due to the difficulty of collecting condition data covering all mechanical fault types
in industrial scenarios, the fault diagnosis problem under incomplete data is receiving …
in industrial scenarios, the fault diagnosis problem under incomplete data is receiving …
A progressive multi-source domain adaptation method for bearing fault diagnosis
Based on massive samples collected from various working conditions, multi-source domain
adaptation-based fault diagnosis methods have been a promising way to improve the …
adaptation-based fault diagnosis methods have been a promising way to improve the …
A fine-grained feature decoupling based multi-source domain adaptation network for rotating machinery fault diagnosis
Multi-source domain adaptation, an effective solution for rotating machinery fault diagnosis,
has achieved great success. However, previous multi-source domain adaptation based …
has achieved great success. However, previous multi-source domain adaptation based …
A Hybrid Temporal Data Mining Method for Intelligent Train Braking Systems
WJ Liu, GC Wan, MS Tong - IEEE Access, 2022 - ieeexplore.ieee.org
As big data mining technology penetrates into various fields, cross-domain topics driven by
data predictive analysis have become important entry points for solving traditional problems …
data predictive analysis have become important entry points for solving traditional problems …
Time-frequency Hypergraph Neural Network for Rotating Machinery Fault Diagnosis with Limited Data
Due to the scarcity of fault samples and the weakness of processing higher-order interactive
information, the most existing intelligence methods fail to achieve the optimal effect in fault …
information, the most existing intelligence methods fail to achieve the optimal effect in fault …
Transfer Learning with Time Series Prediction
A Thompson - Available at SSRN 4214809, 2022 - papers.ssrn.com
Transfer learning is a concept in machine learning in which we relax the assumption that the
training and testing data be from the same distribution. This allows us to build a model on a …
training and testing data be from the same distribution. This allows us to build a model on a …
Enhancing Brain Tumor Diagnosis with Deep Transfer Learning: A Multi-Modal Approach
Y Vishe, S Hariharan - 2024 2nd International Conference on …, 2024 - ieeexplore.ieee.org
The Tumour Detection model makes it easier to locate the tumor in a human brain by using a
multi-modal approach. Our model integrates ResNet and ResUNet to predict as well as …
multi-modal approach. Our model integrates ResNet and ResUNet to predict as well as …