Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression
By Abdenour Soualhi, Kamal Medjaher, Noureddine Zerhouni. IEEE Transactions on Instrumentation and Measurement, 2015
601 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/36bf1136efa89d135b60b636fbb7f6f9f59b4443
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In Control and Systems Engineering
In Mechanical Engineering
Citations in the corpus
- Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain (2016, methodology): “For instance, in [28], a single fault in bearings is diagnosed and detected using HHT and Support Vector Machines (SVM) applied to the vibration signal.”
- Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning (2018, background): “Correspondingly, numerous studies have focused on how to extract effective features from measured time-domain signals based on various signal processing techniques, such as time-domain statistical analysis and spectral analysis [2], [3], time–frequency and other transform domain analysis [4]–[7], ad”
- Direct Remaining Useful Life Estimation Based on Support Vector Regression (2017, methodology): “In [23], the RUL is estimated as the smallest value given by the three determined models.”
- Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges (2017, methodology): “Several applications of SVM classification in big data analytics include text classification [49], pattern matching [50], health diagnostics [51], and commerce.”
- Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “The Hilbert-Huang transform was utilized in [72] to extract health indicators from a vibration signal; an SVM model was then used as an effective solution to assess the degradation states,”
- Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis (2017, methodology): “[6] used Hilbert–Huang transform, support vector machine (SVM), and regression to monitor the bearing health conditions.”
- A comprehensive review on convolutional neural network in machine fault diagnosis (2020, background): “2) Although two-direction vibration signals are collected, the vertical vibration signals provide less useful information than the horizontal ones for tracking the bearing degradations according to the related literature [28, 29].”
- Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks (2018, methodology): “[10] applied the Hilbert–Huang transform (HHT) to extract health indicator from vibration signals and utilized SVM to achieve fault classification of bearings.”
- A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings (2020, methodology): “, artificial neural networks (ANNs) [10], [11], support vector machine (SVM) [12], [13], and relevance vector machine (RVM) [14], [15], to train a prediction model and then utilize the trained model to estimate the RUL of a bearing.”
- Artificial intelligence for fault diagnosis of rotating machinery: A review (2018, methodology): “Soualhi et al. [66] use the HHT to extract health indicators from vibration signals to tack the degradation of critical components in bearings; then, the degradation states are detected by SVM.”
- Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network (2017, methodology): “Xu et al. [12] utilized the PCA to fuse timedomain and frequency-domain features of flame oscillation signal, and established the joint probability density arbiter and neural networks/SVM models to identify the fuel type.”