An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.05.024
Abstract: Abstract Planetary gearbox has complex structures and works under various non-stationary operating conditions. The vibration signals of planetary gearbox are complicated and usually polluted by noise and interference. It is difficult to extract effective features… read more here.
Keywords: gearbox fault; generative adversarial; fault; diagnosis ... See more keywords
Gearbox fault diagnosis method based on deep learning multi-task framework
Sign Up to like & getrecommendations! Published in 2023 at "International Journal of Structural Integrity"
DOI: 10.1108/ijsi-11-2022-0134
Abstract: PurposeIn gearbox fault diagnosis, identifying the fault type and severity simultaneously, as well as the compound fault containing multiple faults, is necessary.Design/methodology/approachTo diagnose multiple faults simultaneously, this paper proposes a multichannel and multi-task convolutional neural… read more here.
Keywords: fault diagnosis; gearbox fault; fault; multi task ... See more keywords
Dual-Enhanced Sparse Decomposition for Wind Turbine Gearbox Fault Diagnosis
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2018.2851423
Abstract: The gearbox is one of the most important components in a wind turbine (WT) system, and fault diagnosis of WT gearbox for maintenance cost reduction is of paramount importance. However, fault feature identification is a… read more here.
Keywords: decomposition; gearbox fault; gearbox; fault diagnosis ... See more keywords
Discriminative Sparse Autoencoder for Gearbox Fault Diagnosis Toward Complex Vibration Signals
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2022.3203440
Abstract: Single-layer representation learning (SLRL) is very promising in automatically learning features for gearbox fault diagnosis. Most of the existing autoencoder (AE)-based SLRL methods perform feature learning by mining intrinsic correlations of the data, whereas the… read more here.
Keywords: fault diagnosis; complex vibration; vibration signals; discriminative sparse ... See more keywords