ArticleEntropy (Basel, Switzerland)2022
Rolling Bearing Fault Diagnosis Using Multi-Sensor Data Fusion Based on 1D-CNN Model.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Physics-Guided Dual-Sensor Framework for Bearing Fault Diagnosis in PMDC Motor Drives.Sensors (Basel, Switzerland) · 2026Article
- MSFF-Net: Multi-Sensor Frequency-Domain Feature Fusion Network with Lightweight 1D CNN for Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble Learning.Sensors (Basel, Switzerland) · 2025Article
- FCRNet: Fast Fourier convolutional residual network for ventilator bearing fault diagnosis.PloS one · 2025Article
- A motor bearing fault diagnosis model based on multi-adversarial domain adaptation.Scientific reports · 2024Article
- Double-Layer Distributed and Integrated Fault Detection Strategy for Non-Gaussian Dynamic Industrial Systems.Entropy (Basel, Switzerland) · 2024Article
- Intelligent Fault Diagnosis Method for Rotating Machinery Based on Recurrence Binary Plot and DSD-CNN.Entropy (Basel, Switzerland) · 2024Article
- Corn Harvester Bearing Fault Diagnosis Based on ABC-VMD and Optimized EfficientNet.Entropy (Basel, Switzerland) · 2023Article
- Boosted Convolutional Neural Network Algorithm for the Classification of the Bearing Fault form 1-D Raw Sensor Data.Sensors (Basel, Switzerland) · 2023Article
- An Attention EfficientNet-Based Strategy for Bearing Fault Diagnosis under Strong Noise.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
To satisfy the requirements of the end-to-end fault diagnosis of rolling bearings, a hybrid model, based on optimal SWD and 1D-CNN, with the layer of multi-sensor data fusion, is proposed in this paper. Firstly, the BAS optimal algorithm is adopted to obtain the optimal parameters of SWD. After that, the raw signals from different channels of sensors are segmented and preprocessed by the optimal SWD, whose name is BAS-SWD. By which, the sensitive OCs with higher values of spectrum kurtosis are extracted from the raw signals. Subsequently, the improved 1D-CNN model based on VGG-16 is constructed, and the decomposed signals from different channels are fed into the independent convolutional blocks in the model; then, the features extracted from the input signals are fused in the fusion layer. Finally, the fused features are processed by the fully connected layers, and the probability of classification is calculated by the cross-entropy loss function. The result of comparative experiments, based on different datasets, indicates that the proposed model is accurate, effective, and has a good generalization ability.
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