ArticleSensors (Basel, Switzerland)2022
A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A fault diagnosis method for complex systems based on hierarchical belief rule base with One-vs-Rest strategy.Scientific reports · 2026Article
- Fault Types and Diagnostic Methods of Manipulator Robots: A Review.Sensors (Basel, Switzerland) · 2025Review
- MR-FuSN: A Multi-Resolution Selective Fusion Approach for Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Compound Fault Diagnosis of Wind Turbine Gearbox via Modified Signal Quality Coefficient and Versatile Residual Shrinkage Network.Sensors (Basel, Switzerland) · 2025Article
- Multilayer Fused Correntropy Reprsenstation for Fault Diagnosis of Mechanical Equipment.Sensors (Basel, Switzerland) · 2024Article
- A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA.Sensors (Basel, Switzerland) · 2024Article
- Article
- Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Modeling of Magnetoelectric Microresonator Using Numerical Method and Simulated Annealing Algorithm.Micromachines · 2023Article
- Intelligent Bearing Fault Diagnosis Based on Feature Fusion of One-Dimensional Dilated CNN and Multi-Domain Signal Processing.Sensors (Basel, Switzerland) · 2023Article
- Review
- Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox.Sensors (Basel, Switzerland) · 2023Article
- Chaotic medical image encryption method using attention mechanism fusion ResNet model.Frontiers in neuroscience · 2023Article
- Res-FLNet: human-robot interaction and collaboration for multi-modal sensing robot autonomous driving tasks based on learning control algorithm.Frontiers in neurorobotics · 2023Article
- Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model.Entropy (Basel, Switzerland) · 2022Article
- Explainable AI for Bearing Fault Prognosis Using Deep Learning Techniques.Micromachines · 2022Article
- Mediating Effects of Adolescent Physical Activity, Self-Rated Health and Family Income.Frontiers in public health · 2022Article
- An Annealing Model Analysis and Research on the In of Community Public Sports on Economic Development.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Bearings are widely used in various electrical and mechanical equipment. As their core components, failures often have serious consequences. At present, most parameter adjustment methods are still manual adjustments of parameters. This adjustment method is easily affected by prior knowledge, easily falls into the local optimal solution, cannot obtain the global optimal solution, and requires a lot of resources. Therefore, this paper proposes a new method for bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by a simulated annealing algorithm. Firstly, the original bearing vibration signal is extracted by wavelet packet transform to obtain the spectrogram, and then the obtained spectrogram is sent to the convolutional neural network for parameter adjustment, and finally the simulated annealing algorithm is used to adjust the parameters. To verify the effectiveness of the method, the bearing database of Case Western Reserve University is used for testing, and the traditional intelligent bearing fault diagnosis methods are compared. The results show that the new method for bearing fault diagnosis proposed in this paper has a better and more reliable diagnosis effect than the existing machine learning and deep learning methods.
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