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An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Learning criteria for training neural network classifiers
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Virtualization and deep recognition for system fault classification
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Amplitudes of characteristic frequencies for fault diagnosis of planetary gearbox
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A dependence-based feature vector and its application on planetary gearbox fault classification
inferredcites →https://doi.org/10.1016/j.measurement.2013.11.012openalex-snapshot
Condition monitoring and fault diagnosis of planetary gearboxes: A review
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An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition
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Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
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inferredcites →https://doi.org/10.1016/j.ymssp.2017.12.008openalex-snapshot
A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy
inferredcites →https://doi.org/10.1016/j.ymssp.2018.02.016openalex-snapshot
Artificial intelligence for fault diagnosis of rotating machinery: A review
inferredcites →https://doi.org/10.1088/1361-6501/aa50e7openalex-snapshot
An integrated approach to planetary gearbox fault diagnosis using deep belief networks
inferredcites →https://doi.org/10.1109/cvpr.1993openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
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Sparse Feature Identification Based on Union of Redundant Dictionary for Wind Turbine Gearbox Fault Diagnosis
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Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis
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Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine
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Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
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