Compound fault diagnosis for rotating machinery: State-of-the-art, challenges, and opportunities
Compound fault, as a primary failure leading to unexpected downtime of rotating machinery,
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …
Zero-shot learning for compound fault diagnosis of bearings
Due to the concurrency and coupling of various types of faults, and the number of possible
fault modes grows exponentially, thereby compound fault diagnosis is a difficult problem in …
fault modes grows exponentially, thereby compound fault diagnosis is a difficult problem in …
Deep multi-scale separable convolutional network with triple attention mechanism: A novel multi-task domain adaptation method for intelligent fault diagnosis
Rotating components, as the core functional part of rotating machinery, their performance
directly determines the stability, reliability, and safety of the equipment operation. Effective …
directly determines the stability, reliability, and safety of the equipment operation. Effective …
Integrated decision-making with adaptive feature weighting adversarial network for multi-target domain compound fault diagnosis of machinery
X Zhang, J Wang, Z Zhang, B Han, H Bao… - Advanced Engineering …, 2024 - Elsevier
Due to the varying manufacturing demands placed upon mechanical equipment, it is often
operated under fluctuating loads and diverse working conditions over extended periods …
operated under fluctuating loads and diverse working conditions over extended periods …
Early fault diagnosis based on reinforcement learning optimized-SVM model with vibration-monitored signals
Effective fault diagnosis maximizes economic benefits by ensuring the stability of machinery
systems. Detecting the faults of the key components in machinery, such as rolling bearings …
systems. Detecting the faults of the key components in machinery, such as rolling bearings …
Bearing fault diagnosis based on mel frequency cepstrum coefficient and deformable space-frequency attention network
Y Zhao, N Zhang, Z Zhang, X Xu - IEEE Access, 2023 - ieeexplore.ieee.org
The main bearing is the core component of gas-fired generator, and its reliability directly
affects the stability of the whole system. Therefore, it is of great significance to study the fault …
affects the stability of the whole system. Therefore, it is of great significance to study the fault …
A partial-label U-Net learning method for compound-fault diagnosis with fault-sample class imbalance
J Zhang, X He - IEEE Transactions on Industrial Informatics, 2023 - ieeexplore.ieee.org
In the operation process of the rotating machinery, compound faults have various
combination forms and are difficult to reproduce, which results in the scarcity of training …
combination forms and are difficult to reproduce, which results in the scarcity of training …
Motor Current Time-varying Quadratic Phase Coupling Analysis and its Application in Traction Motor Fault Detection under Varying-speed Condition
J Yang, Z Li, P Zhang, K Zhang, Y Xu - IEEE Sensors Journal, 2024 - ieeexplore.ieee.org
The locomotive traction motors convert electrical energy into mechanical energy and are the
power source of the train. Therefore, their working condition is vital to the performance and …
power source of the train. Therefore, their working condition is vital to the performance and …
Compound fault diagnosis of diesel engines by combining generative adversarial networks and transfer learning
Z Cui, Y Lu, X Yan, S Cui - Expert Systems with Applications, 2024 - Elsevier
In order to solve the problem of compound fault diagnosis of diesel engine fuel injection
system under the condition of few samples, a comprehensive diagnosis method based on …
system under the condition of few samples, a comprehensive diagnosis method based on …
Rolling bearing fault diagnosis based on domain adaptation and preferred feature selection under variable working conditions
X Yu, W Chen, C Wu, E Ding, Y Tian, H Zuo… - Shock and …, 2021 - Wiley Online Library
In real industrial scenarios, with the use of conventional machine learning techniques, data‐
driven diagnosis models have a limitation that it is difficult to achieve the desirable fault …
driven diagnosis models have a limitation that it is difficult to achieve the desirable fault …