ArticleSensors (Basel, Switzerland)2022
Rolling Bearing Fault Diagnosis Based on Markov Transition Field and Residual Network.
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.
- Personalized Classification of Scenario-Derived Operational Driver-State Classes from Non-Intrusive Wearable Signals in Real-World SAE Level 2 Automated Driving.Sensors (Basel, Switzerland) · 2026Article
- GMRVGG: A Bearing Fault Diagnosis Method Based on Tri-Modal Image Feature Fusion.Sensors (Basel, Switzerland) · 2026Article
- An Explainable Intelligent Fault Diagnosis for Rotating Machinery via Multi-Source Information Fusion Under Noisy Environments and Small Sample Conditions.Sensors (Basel, Switzerland) · 2026Article
- A Fault Identification Method for Micro-Motors Using an Optimized CNN-Based JMD-GRM Approach.Micromachines · 2026Article
- A novel approach to smart-assisted schizophrenia screening based on Raman spectroscopy and deep learning.Scientific reports · 2025Article
- Research on variable-length control chart pattern recognition based on sliding window method and SECNN-BiLSTM.Scientific reports · 2025Article
- A Novel Method for Rolling Bearing Fault Diagnosis Based on Gramian Angular Field and CNN-ViT.Sensors (Basel, Switzerland) · 2024Article
- Intelligent Fault Diagnosis of Rolling Bearing Based on Gramian Angular Difference Field and Improved Dual Attention Residual Network.Sensors (Basel, Switzerland) · 2024Article
- Rolling bearing fault diagnosis based on Gramian angular difference field and improved channel attention model.PeerJ. Computer science · 2024Article
- Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning.Sensors (Basel, Switzerland) · 2023Article
- Method for Diagnosing Bearing Faults in Electromechanical Equipment Based on Improved Prototypical Networks.Sensors (Basel, Switzerland) · 2023Article
- A Deep Learning-Based Unbalanced Force Identification of the Hypergravity Centrifuge.Sensors (Basel, Switzerland) · 2023Article
- Editorial for Special Issue: Machine Health Monitoring and Fault Diagnosis Techniques.Sensors (Basel, Switzerland) · 2023Article
- Two-Stage Multi-Channel Fault Detection and Remaining Useful Life Prediction Model of Internal Gear Pumps Based on Robust-ResNet.Sensors (Basel, Switzerland) · 2023Article
- A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors.Sensors (Basel, Switzerland) · 2022Review
- A wireless sensor network node fault diagnosis model based on belief rule base with power set.Heliyon · 2022Article
- End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2022Article
- Time Series Forecasting of Motor Bearing Vibration Based on Informer.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning models, and their multilayer nonlinear mapping capability can improve the accuracy of intelligent fault diagnosis. However, problems such as gradient disappearance occur as the number of network layers increases. Moreover, directly taking the raw vibration signals of rolling bearings as the network input results in incomplete feature extraction. In order to efficiently represent the state characteristics of vibration signals in image form and improve the feature learning capability of the network, this paper proposes fault diagnosis model MTF-ResNet based on a Markov transition field and deep residual network. First, the data of raw vibration signals are augmented by using a sliding window. Then, vibration signal samples are converted into two-dimensional images by MTF, which retains the time dependence and frequency structure of time-series signals, and a deep residual neural network is established to perform feature extraction, and identify the severity and location of the bearing faults through image classification. Lastly, experiments were conducted on a bearing dataset to verify the effectiveness and superiority of the MTF-ResNet model. Features learned by the model are visualized by t-SNE, and experimental results indicate that MTF-ResNet showed better average accuracy compared with several widely used diagnostic methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.