Mitigating overconfidence in unknown sample predictions: A confidence-enhanced one-versus-all network for open-set transfer fault diagnosis

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113013 - 113013

Опубликована: Янв. 1, 2025

Язык: Английский

---

Cross-sensor contrastive learning-based pre-training for machinery fault diagnosis under sample-limited conditions

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113075 - 113075

Опубликована: Фев. 1, 2025

Язык: Английский

---

A limited annotated sample fault diagnosis algorithm based on nonlinear coupling self-attention mechanism

Engineering Failure Analysis, Год журнала: 2025, Номер unknown, С. 109474 - 109474

Опубликована: Март 1, 2025

Язык: Английский

---

Federated contrastive prototype learning: An efficient collaborative fault diagnosis method with data privacy

Knowledge-Based Systems, Год журнала: 2023, Номер 281, С. 111093 - 111093

Опубликована: Окт. 17, 2023

Язык: Английский

---

Trustworthy Bayesian Deep Learning Framework for Uncertainty Quantification and Confidence Calibration: Application in Machinery Fault Diagnosis

Reliability Engineering & System Safety, Год журнала: 2024, Номер 255, С. 110657 - 110657

Опубликована: Ноя. 13, 2024

Язык: Английский

---

Interpretable temporal degradation state chain based fusion graph for intelligent bearing fault detection

Advanced Engineering Informatics, Год журнала: 2024, Номер 59, С. 102342 - 102342

Опубликована: Янв. 1, 2024

Язык: Английский

---

SCG-GFFE: A Self-Constructed graph fault feature extractor based on graph Auto-encoder algorithm for unlabeled single-variable vibration signals of harmonic reducer

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102579 - 102579

Опубликована: Май 3, 2024

Язык: Английский

---

Multisensor Fusion on Hypergraph for Fault Diagnosis

IEEE Transactions on Industrial Informatics, Год журнала: 2024, Номер 20(8), С. 10008 - 10018

Опубликована: Май 1, 2024

Multisensor information fusion techniques based on deep learning are crucial for machinery fault diagnosis. However, there two major issues in previous research. First, the relationship between multisensor samples is disregarded, which important to enhance diagnostic performance. Second, structure of algorithm becomes extremely complex with prolonged training when dealing equipped a large number sensors. To address aforementioned issues, our study proposes new mechanism that fuses hypergraphs, by building single-sensor hypergraph and sensor space embed as nodes. In addition, dual-branch neural network designed compute hypergraphs obtain feature representation diagnose faults. The validated datasets its

Язык: Английский

---

A prior knowledge-enhanced self-supervised learning framework using time-frequency invariance for machinery intelligent fault diagnosis with small samples

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108503 - 108503

Опубликована: Май 2, 2024

Язык: Английский

---

Utilizing Bayesian generalization network for reliable fault diagnosis of machinery with limited data

Knowledge-Based Systems, Год журнала: 2024, Номер 305, С. 112628 - 112628

Опубликована: Окт. 11, 2024

Язык: Английский

---