Fault Diagnosis Method for Wind Turbine Gearbox Based on Ensemble-Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy
Abstract
:1. Introduction
2. Basic Principles
2.1. ICEEMDAN–SWT
2.1.1. Rime Optimization Algorithm
2.1.2. Improved Complete Ensemble EMD
2.1.3. Permutation Entropy
2.1.4. Stationary Wavelet Transform
2.2. ERCMFRDE
2.2.1. Fluctuation-Based Reverse Dispersion Entropy
2.2.2. Ensemble-Refined Composite Multiscale FRDE
2.2.3. Recursive Feature Elimination
2.3. Least Squares Support Vector Machine
3. The Failure Diagnostic Flowchart
4. Experimental Verification
4.1. Signal Acquisition and Noise Reduction
4.2. Feature Extraction
4.3. Feature Selection
4.4. Intelligent Diagnosis
4.5. Noise Resistance Test
4.6. Comparison Experiment
4.6.1. Comparison of Different Entropy Algorithms
4.6.2. Comparison of Different Diagnostic Methods
5. Conclusions
- A comparison of the ERCMFRDE-based feature extraction method with other feature extraction methods revealed that the ERCMFRDE method had the highest accuracy rate, proving to be significantly better than the entropy algorithm with a single coarse-grained processing and the traditional kinds of entropy algorithms. This evidence serves to illustrate the superiority of the proposed ensemble entropy method for feature extraction and fault identification.
- In a noisy environment, the fault diagnosis method based on ICEEMDAN–SWT and ERCMFRDE continued to perform well, indicating that the method is effective in eliminating interference components unrelated to fault characteristics. This indicates that the method displays both excellent generalization capabilities and robust performance.
- The efficacy and reliability of the fault diagnosis methodology based on ERCMFRDE were verified using two gearbox datasets from different sources. The method could accurately identify the type of faults in gearboxes and has more significant advantages over the other three classic fault diagnosis methods.
- The fault diagnosis method based on ERCMFRDE proposed in this paper is a practical approach for extracting the fault features of gearboxes and accurately identifying different types, with an accuracy of 98.88%. This demonstrates that the proposed intelligent diagnostic method offers a significant advantage in gearbox fault diagnosis.
- The methods and models investigated in this paper are mainly data-driven and lack consideration of fault mechanisms and knowledge of the unit operating mechanisms. In future research, the complex operating mechanisms of wind turbine gearboxes will be considered and embedded in the data-driven model to guide feature and model learning.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
DE | Dispersion Entropy |
RDE | Reverse Dispersion Entropy |
FDE | Fluctuation Dispersion Entropy |
FRDE | Fluctuation-based Reverse Dispersion Entropy |
ERCMDE | Ensemble Refined Composite Multiscale DE |
ERCMRDE | Ensemble Refined Composite Multiscale RDE |
ERCMFDE | Ensemble Refined Composite Multiscale FDE |
RCMFRDE | Refined Composite Multiscale Reverse Dispersion Entropy |
ERCMFRDE | Ensemble RCMFRDE |
EMD | Empirical Mode Decomposition |
EEMD | Ensemble EMD |
CEEMD | Complementary EEMD |
ICEEMDAN | Improved CEEMD with Adaptive Noise |
WT | Wavelet Transform |
SWT | Stationary Wavelet Transform |
PE | Permutation Entropy |
IMF | Intrinsic Modal Function |
SVM | Support Vector Machine |
LSSVM | Least Squares Support Vector Machine |
RIME | Rime Optimization Algorithm |
Nsd | Noise standard deviation |
NR | Number of Realizations |
NOR | Normal State |
MTF | Missing Tooth Fault |
BTF | Broken Tooth Fault |
SWF | Surface Wear Fault |
PSO | Particle Swarm Optimization |
rms | Root Mean Square |
MFS | Mechanical Failure Simulation |
SNR | Signal-to-Noise Ratio |
DTL | Deep Transfer Learning |
VMD | Variational Mode Decomposition |
WPD | Wavelet Packet Decomposition |
CNN | Convolutional Neural Network |
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Fault Types | Motor Speed (r/min) | Number of Training Samples | Number of Testing Samples | Class Label |
---|---|---|---|---|
Normal state | 1750 | 60 | 40 | 1 |
Broken tooth | 1750 | 60 | 40 | 2 |
Missing tooth | 1750 | 60 | 40 | 3 |
Surface wear | 1750 | 60 | 40 | 4 |
Entropy Value | Moment Processing | Coarse Graining | Size |
---|---|---|---|
RCMFRDE_mean | First-order | Mean | 400 × 20 |
RCMFRDE_max | First-order | Maximum | 400 × 20 |
RCMFRDE_min | First-order | Minimum | 400 × 20 |
RCMFRDE_var | Second-order | Variance | 400 × 19 |
RCMFRDE_rms | Second-order | Root mean square | 400 × 19 |
RCMFRDE_skewness | Third-order | Skewness | 400 × 18 |
Ensemble Entropy Set | Entropy Value | Moment Processing | Coarse Graining | Size |
---|---|---|---|---|
ERCMFRDE_1 | RCMFRDE_mean | First-order | Mean | 400 × 60 |
RCMFRDE_max | Maximum | |||
RCMFRDE_min | Maximum | |||
ERCMFRDE_2 | RCMFRDE_var | Second-order | Variance | 400 × 38 |
RCMFRDE_rms | Root mean square | |||
ERCMFRDE_3 | RCMFRDE_skewness | Third-order | Skewness | 400 × 18 |
ERCMFRDE | RCMFRDE_mean | First-order | Mean | 400 × 116 |
RCMFRDE_max | Maximum | |||
RCMFRDE_min | Maximum | |||
RCMFRDE_var | Second-order | Variance | ||
RCMFRDE_rms | Root mean square | |||
RCMFRDE_skewness | Third-order | Skewness |
Entropy Value | Accuracy (%) | ||
---|---|---|---|
Maximum | Minimum | Average | |
RCMFRDE_mean | 96.88 | 93.13 | 95.34 |
RCMFRDE_max | 96.88 | 91.88 | 95.22 |
RCMFRDE_min | 93.13 | 87.50 | 89.59 |
RCMFRDE_var | 95.63 | 90.63 | 93.50 |
RCMFRDE_rms | 95.63 | 86.25 | 89.72 |
RCMFRDE_skewness | 96.25 | 89.38 | 92.34 |
ERCMFRDE_1 | 100.00 | 96.25 | 97.75 |
ERCMFRDE_2 | 96.88 | 93.75 | 95.25 |
ERCMFRDE | 100.00 | 96.88 | 98.88 |
Entropy Value | Accuracy (%) | ||
---|---|---|---|
Maximum | Minimum | Average | |
ERCMDE | 94.38 | 90.00 | 92.28 |
ERCMRDE | 98.13 | 94.38 | 96.03 |
ERCMFDE | 100.00 | 95.63 | 97.47 |
ERCMFRDE | 100.00 | 96.88 | 98.88 |
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Wang, X.; Du, Y. Fault Diagnosis Method for Wind Turbine Gearbox Based on Ensemble-Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy. Entropy 2024, 26, 705. https://doi.org/10.3390/e26080705
Wang X, Du Y. Fault Diagnosis Method for Wind Turbine Gearbox Based on Ensemble-Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy. Entropy. 2024; 26(8):705. https://doi.org/10.3390/e26080705
Chicago/Turabian StyleWang, Xiang, and Yang Du. 2024. "Fault Diagnosis Method for Wind Turbine Gearbox Based on Ensemble-Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy" Entropy 26, no. 8: 705. https://doi.org/10.3390/e26080705