Fault transfer diagnosis of rolling bearings across different devices via multi-domain information fusion and multi-kernel maximum mean discrepancy

Applied Soft Computing, Год журнала: 2024, Номер 159, С. 111620 - 111620

Опубликована: Апрель 17, 2024

Язык: Английский

---

A novel method of rolling bearings fault diagnosis based on singular spectrum decomposition and optimized stochastic configuration network

Neurocomputing, Год журнала: 2024, Номер 574, С. 127278 - 127278

Опубликована: Янв. 18, 2024

Язык: Английский

---

A novel self-supervised representation learning framework based on time-frequency alignment and interaction for mechanical fault diagnosis

Knowledge-Based Systems, Год журнала: 2024, Номер 295, С. 111846 - 111846

Опубликована: Апрель 27, 2024

Язык: Английский

---

Multi-source fault data fusion diagnosis method based on hyper-feature space graph collaborative embedding

Advanced Engineering Informatics, Год журнала: 2025, Номер 64, С. 103092 - 103092

Опубликована: Янв. 2, 2025

Язык: Английский

---

A long-short-term feature extraction network based on soft-parameter-sharing for high-speed train bogies multi-object fault diagnosis under long-tailed distribution

Expert Systems with Applications, Год журнала: 2025, Номер 269, С. 126409 - 126409

Опубликована: Янв. 7, 2025

Язык: Английский

---

A new lifelong learning method based on dual distillation for bearing diagnosis with incremental fault types

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103136 - 103136

Опубликована: Янв. 22, 2025

Язык: Английский

---

An unsupervised multi-level fusion domain adaptation method for transfer diagnosis under time-varying working conditions

Mechanical Systems and Signal Processing, Год журнала: 2025, Номер 228, С. 112458 - 112458

Опубликована: Фев. 17, 2025

Язык: Английский

---

A Pre-training Method for Motor Fault Diagnosis Based on Siamese Residual Network

Lecture notes in electrical engineering, Год журнала: 2025, Номер unknown, С. 421 - 431

Опубликована: Янв. 1, 2025

Язык: Английский

---

Multisource Heterogeneous Data Fusion-Based Process Monitoring of the Reheating Furnace in Steel Production

ACS Omega, Год журнала: 2025, Номер unknown

Опубликована: Март 25, 2025

The reheating furnace is the key piece of equipment in hot rolling process steel production. In order to fully exploit all data recorded from production representing different information, this paper designs a monitoring algorithm with multisource information fusion by integrating multiple comprehensively monitor operating state furnace. Multisource combines variable and heating slab. To overcome challenge heterogeneous due sampling patterns, univariate time series multivariate are fused transformer. scheme, represented bidirectional gated recurrent unit for one-dimensional temporal representation, convolutional network two-dimensional eigenvalue decomposition correlation representation between variables. evaluate performance proposed method, computational experiments based on actual carried out. representations, highest predictions obtained comparison regression algorithms, respectively. By comparing fusing objects schemes, achieves accuracy (91.33%), precision (91.46%), recall (92.59%), proving effectiveness approach. compared statistical which achieve (95%), (93.45%), (97.08%).

Язык: Английский

---

A Bearing Fault Diagnosis Method Combining Multi-Source Information and Multi-Domain Information Fusion

Machines, Год журнала: 2025, Номер 13(4), С. 289 - 289

Опубликована: Март 31, 2025

In modern industries, bearings are often subjected to challenges from environmental noise and variations in operating conditions during their operation, which affects existing fault diagnosis methods that rely on signals single types of sensors. These fail provide comprehensive stable information, thereby affecting the diagnostic performance. To address this issue, paper introduces a multi-source multi-domain information fusion method for (M2IFD) bearings, integrating an attention mechanism enhance process. The proposed is structured into three main stages: initially, original signal undergoes transformation frequency time–frequency domains using envelope spectral transform (EST) Bessel (BT) extract richer features. second stage, features extracted independently each transformed domain combined with channel feature fusion, preserving unique source. Finally, further fused through improve classification accuracy. Extensive comparison experiments conducted Paderborn dataset illustrate M2IFD significantly enhances recognition accuracy across various conditions, showcasing its adaptability robustness. This approach presents new avenues bearing diagnosis, significant implications both theoretical practical applications.

Язык: Английский

---