ArticleSensors (Basel, Switzerland)2018
A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Fault Diagnosis of Planetary Gearbox Based on Hierarchical Refined Composite Multiscale Fuzzy Entropy and Optimized LSSVM.Entropy (Basel, Switzerland) · 2025Article
- A Comparative Study of Causality Detection Methods in Root Cause Diagnosis: From Industrial Processes to Brain Networks.Sensors (Basel, Switzerland) · 2024Article
- Tsallis Entropy-Based Complexity-IPE Casualty Plane: A Novel Method for Complex Time Series Analysis.Entropy (Basel, Switzerland) · 2024Article
- Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and ISSA-LSTM.Entropy (Basel, Switzerland) · 2023Article
- Advancing Fault Detection in HVAC Systems: Unifying Gramian Angular Field and 2D Deep Convolutional Neural Networks for Enhanced Performance.Sensors (Basel, Switzerland) · 2023Article
- Ensemble Improved Permutation Entropy: A New Approach for Time Series Analysis.Entropy (Basel, Switzerland) · 2023Article
- Dynamic Evaluation of the Degradation Process of Vibration Performance for Machine Tool Spindle Bearings.Sensors (Basel, Switzerland) · 2023Article
- Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve.Entropy (Basel, Switzerland) · 2022Article
- An Improved Incipient Fault Diagnosis Method of Bearing Damage Based on Hierarchical Multi-Scale Reverse Dispersion Entropy.Entropy (Basel, Switzerland) · 2022Article
- Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN.Sensors (Basel, Switzerland) · 2021Article
- Refined Composite Multi-Scale Reverse Weighted Permutation Entropy and Its Applications in Ship-Radiated Noise.Entropy (Basel, Switzerland) · 2021Article
- Deep Learning-Based Adaptive Neural-Fuzzy Structure Scheme for Bearing Fault Pattern Recognition and Crack Size Identification.Sensors (Basel, Switzerland) · 2021Article
- A Novel End-To-End Feature Selection and Diagnosis Method for Rotating Machinery.Sensors (Basel, Switzerland) · 2021Article
- An Optimization Algorithm for Computer-Aided Diagnosis of Breast Cancer Based on Support Vector Machine.Frontiers in bioengineering and biotechnology · 2021Article
- A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise.Entropy (Basel, Switzerland) · 2020Article
- Reverse Dispersion Entropy: A New Complexity Measure for Sensor Signal.Sensors (Basel, Switzerland) · 2019Article
- Feature Extraction of Ship-Radiated Noise Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD, Mutual Information, and Differential Symbolic Entropy.Entropy (Basel, Switzerland) · 2019Article
- Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine.Entropy (Basel, Switzerland) · 2019Article
- Rolling Element Bearing Fault Diagnosis by Combining Adaptive Local Iterative Filtering, Modified Fuzzy Entropy and Support Vector Machine.Entropy (Basel, Switzerland) · 2018Article
- A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery.Sensors (Basel, Switzerland) · 2018Article
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5 authors.
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Abstract
Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.
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