A Novel Deep Convolutional Neural Network Combining Global Feature Extraction and Detailed Feature Extraction for Bearing Compound Fault Diagnosis [PDF]
Sensors, 2023 This study researched the application of a convolutional neural network (CNN) to a bearing compound fault diagnosis. The proposed idea lies in the ability of CNN to automatically extract fault features from complex raw signals.
Shuzhen Han +6 more
doaj +3 more sources
Compound Fault Diagnosis of Planetary Gearbox Based on Improved LTSS-BoW Model and Capsule Network [PDF]
SensorsThe identification of compound fault components of a planetary gearbox is especially important for keeping the mechanical equipment working safely. However, the recognition performance of existing deep learning-based methods is limited by insufficient ...
Guoyan Li +5 more
doaj +3 more sources
Compound Fault Diagnosis of Wind Turbine Gearbox via Modified Signal Quality Coefficient and Versatile Residual Shrinkage Network [PDF]
SensorsWind turbine gearbox fault diagnosis is critical to guarantee working efficiency and operational safety. However, the current diagnostic methods face enormous restrictions in handling nonlinear noise signals and intricate compound fault patterns. Herein,
Weixiong Jiang +4 more
doaj +3 more sources
Application of Deep Neural Network in Gearbox Compound Fault Diagnosis
Energies, 2023 Gearbox fault diagnosis is vital to ensure the efficient operation of rotating machinery, and most gearbox faults in industrial production occur in the form of compound faults. To realize the diagnosis of compound faults in gearboxes at different speeds,
Xiangfeng Zhang +3 more
doaj +3 more sources
Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation [PDF]
Entropy, 2020 Compound fault diagnosis is challenging due to the complexity, diversity and non-stationary characteristics of mechanical complex faults. In this paper, a novel compound fault separation method based on singular negentropy difference spectrum (SNDS) and ...
Guiji Tang, Tian Tian
doaj +3 more sources
Compound Fault Characteristic Analysis for Fault Diagnosis of a Planetary Gear Train
SensorsThe carrier eccentricity error and gear compound faults are most likely to occur simultaneously in an actual planetary gear train (PGT). Various faults and errors are coupled with each other to generate a complex dynamic response, which makes the ...
Yulin Ren +5 more
doaj +4 more sources
Adaptive Label Refinement Network for Domain Generalization in Compound Fault Diagnosis. [PDF]
Sensors (Basel)Domain generalization (DG) aims to develop models that perform robustly on unseen target domains, a critical but challenging objective for real-world fault diagnosis. The challenge is further complicated in compound fault diagnosis, where the rigidity of hard labels and the simplicity of label smoothing under-represent inter-class relations and ...
Du Q, Yao J, Yang J, Tu F, Yang S.
europepmc +5 more sources
Advances in Mechanical Engineering, 2023 In general, gearbox is prone to occur compound fault due to its harsh working environment and its fault vibration signal contains multi-components which correspond to each gearbox parts.
Lu Yan +5 more
doaj +2 more sources
An Imbalanced Fault Diagnosis Method Based on TFFO and CNN for Rotating Machinery
Sensors, 2022 Deep learning-based fault diagnosis usually requires a rich supply of data, but fault samples are scarce in practice, posing a considerable challenge for existing diagnosis approaches to achieve highly accurate fault detection in real applications.
Long Zhang +5 more
doaj +2 more sources
Research on the Sparse Representation for Gearbox Compound Fault Features Using Wavelet Bases [PDF]
Shock and Vibration, 2015 The research on gearbox fault diagnosis has been gaining increasing attention in recent years, especially on single fault diagnosis. In engineering practices, there is always more than one fault in the gearbox, which is demonstrated as compound fault ...
Chunyan Luo +5 more
doaj +2 more sources