Journal ArticleDOI
A High-Stability Diagnosis Model Based on a Multiscale Feature Fusion Convolutional Neural Network
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TLDR
In this paper, a multiscale feature fusion convolutional neural network (MFF-CNN) is proposed for the diagnosis of rotating machines based on deep learning models, which extracts, modulates, and fuses the input samples' multi-scale features to focus more on the health state difference rather than the noise disturbance and workload difference.Abstract:
Recently, the diagnosis of rotating machines based on deep learning models has achieved great success. Many of these intelligent diagnosis models are assumed that training and test data are subject to independent identical distributions (IIDs). Unfortunately, such an assumption is generally invalid in practical applications due to noise disturbances and changes in workload. To address the above problem, this article presents a high-stability diagnosis model named the multiscale feature fusion convolutional neural network (MFF-CNN). MFF-CNN does not rely on tedious data preprocessing and target domain information. It is composed of multiscale dilated convolution, self-adaptive weighting, and the new form of maxout (NFM) activation. It extracts, modulates, and fuses the input samples’ multiscale features so that the model focuses more on the health state difference rather than the noise disturbance and workload difference. Two diagnostic cases, including noisy cases and variable load cases, are used to verify the effectiveness of the present model. The results show that the present model has a strong health state identification capability and anti-interference capability for variable loads and noise disturbances.read more
Citations
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Journal ArticleDOI
A recursive sparse representation strategy for bearing fault diagnosis
TL;DR: In this article, a recursive sparse representation (RSR) algorithm is proposed to solve the fault diagnosis of bearings from sparse representation in the time and frequency domains, where the tunable Q-factor wavelet transform (TQWT) filtering strategy is used to adaptively obtain the best wavelet with the signal vibration features.
Journal ArticleDOI
Comprehensive Remaining Useful Life Prediction for Rolling Element Bearings Based on Time-Varying Particle Filtering
TL;DR: In this paper , a time-varying particle filter (TVPF)-based comprehensive RUL prediction model is developed, which has the capability to select the optimal state model with a sliding window, according to the characteristics of the data.
Journal ArticleDOI
Series Arc Fault Detection Based on Wavelet Compression Reconstruction Data Enhancement and Deep Residual Network
TL;DR: An arc fault detection model based on residual network (ResNet) is proposed from the perspective of computer vision, and an appropriate data enhancement method based on wavelet compression reconstruction is given to solve the over-fitting phenomenon of deep network ResNet152 and improves the accuracy of ResNet 50/101/152.
Journal ArticleDOI
A novel prediction network for remaining useful life of rotating machinery
Journal ArticleDOI
Multitask Learning-Based Self-Attention Encoding Atrous Convolutional Neural Network for Remaining Useful Life Prediction
TL;DR: In this paper , a multitask learning-based self-attention encoding atrous convolutional neural network (MSA-CNN) is proposed to predict the remaining useful life (RUL) of a turbofan engine.
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