Journal ArticleDOI
Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks
TLDR
A novel method by integrating the Convolutional Neural Networks with the Variational Mode Decomposition (VMD) algorithms to achieve an effective and efficient fault diagnosis of rolling bearings under different environments and states is developed.About:
This article is published in Applied Soft Computing.The article was published on 2020-10-01. It has received 103 citations till now. The article focuses on the topics: Convolutional neural network & Fault (power engineering).read more
Citations
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Journal ArticleDOI
A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels
TL;DR: A novel method based on an adaptive loss-weighted meta-residual network (ALWM-ResNet) is proposed to address fault diagnosis with noisy labels using a weighted network and a meta-network cloned from the original ResNet to establish a weighted function mapping to adaptively learn weights from data with clean labels.
Journal ArticleDOI
Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks
TL;DR: In this paper, a fast deep graph convolutional network is proposed to diagnose faults in the gearbox of wind turbines, which can extract the features of points with a large span of the defined graph samples.
Journal ArticleDOI
A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque
TL;DR: In this article, a hybrid multi-step prediction model combining variational mode decomposition (VMD), empirical wavelet transform (EWT) and long short-term memory (LSTM) network is proposed for shield tunneling machine cutterhead torque.
Journal ArticleDOI
A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings
TL;DR: Wang et al. as discussed by the authors proposed a novel time-frequency Transformer (TFT) model inspired by the massive success of vanilla Transformer in sequence processing, which designed a fresh tokenizer and encoder module to extract effective abstractions from the timefrequency representation (TFR) of vibration signals.
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LEFE-Net: A Lightweight Efficient Feature Extraction Network With Strong Robustness for Bearing Fault Diagnosis
TL;DR: In this article, an efficient feature extraction method based on the convolutional neural networks (CNN) was proposed, and the high-precision fault diagnosis task was completed using a lightweight network.
References
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Journal ArticleDOI
Variational Mode Decomposition
TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Journal ArticleDOI
A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method
TL;DR: A new CNN based on LeNet-5 is proposed for fault diagnosis which can extract the features of the converted 2-D images and eliminate the effect of handcrafted features and has achieved significant improvements.
Journal ArticleDOI
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
TL;DR: An end-to-end method that takes raw temporal signals as inputs and thus doesn’t need any time consuming denoising preprocessing and can achieve high accuracy when working load is changed is proposed.
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A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
TL;DR: Experimental results demonstrate the effectiveness of the proposed hybrid prognostics approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.
Journal ArticleDOI
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
TL;DR: Experimental results and comprehensive comparison analysis have demonstrated the superiority of the proposed MSCNN approach, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise.