Research on the Fault Diagnosis Method of Rotating Machinery Based on Improved Variational Modal Decomposition and Probabilistic Neural Network Algorithm
Abstract
:1. Introduction
2. Vibration Signal Processing Method Based on VMD
2.1. IPSO-VMD Algorithm Design
- Adaptive inertia weight strategy
- and are the preset minimum and maximum values of inertial weight , taking , ;
- is the average value calculated by the fitness of the whole population at k-th iteration;
- is the minimum fitness of the whole population at iteration.
- 2.
- Compressibility factor method
- 3.
- Elite Learning Strategies
- 4.
- Fitness Distance Ratio Optimization Strategy
2.2. Fault Simulation Experiment
3. Feature Extraction and Recognition Based on IPSO-VMD
3.1. Feature Extraction Based on VMD
- Correlation Coefficient
- 2.
- IMF Energy Ratio
3.2. Multi-Dimension Sensitive Feature Optimization
- Time-Domain Parameters
- 2.
- Frequency-Domain Parameters
- 3.
- Other Parameters
3.3. Probabilistic Neural Networks
4. Experimental Verification
4.1. Case 1
4.2. Case 2
5. Conclusions and Future Work
- In view of the difficulty in selecting the two key parameters and in VMD, the adaptive inertia weight strategy and compression factor method were used to improve the PSO algorithm, and the adaptive optimization of parameters was carried out by combining the fitness function, so as to avoid the instability of manual value taking and cumbersome operation.
- In the extracted multi-dimensional feature vectors, the sensitivity of different fault feature parameters varies, and some parameters may be irrelevant or redundant. Laplace score was used to select sensitive feature parameters from multi-domain feature parameters, and the fault sensitive feature vector was constructed and input into the PNN model to realize fault diagnosis.
- Through the validation of simulated and real signals, the proposed IPSO-VMD-PNN framework shows excellent robustness and adaptability, demonstrating its potential for application in practical engineering.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Acquisition System | Processing Board | Sampling Frequency | Sampling Time | Speed |
---|---|---|---|---|
dSPACE | DS1006 | 20 kHz | 10 s | 2820 rpm |
Label | Gears 1 | Gears 2 | Gears 3 | Gears 4 |
---|---|---|---|---|
1 | Normal | Normal | Normal | Normal |
2 | Flake | Normal | Normal | Normal |
3 | Pit | Normal | Normal | Normal |
Label 1 | Label 2 | Label 3 |
---|---|---|
Gears Status | Feature Extraction Algorithm | Classification Algorithm | Number of Training Sets | Number of Test Sets | Right | Misjudgment | Accuracy Rates |
---|---|---|---|---|---|---|---|
Label 1 | IPSO-VMD | PNN | 70 | 30 | 29 | 1 | 96.67% |
PSO-VMD | PNN | 70 | 30 | 28 | 2 | 93.33% | |
PSO-VMD | ELM | 70 | 30 | 26 | 4 | 86.67% | |
PSO-VMD | SVM | 70 | 30 | 27 | 3 | 90.00% | |
EMD | PNN | 70 | 30 | 24 | 6 | 80.00% | |
Label 2 | IPSO-VMD | PNN | 70 | 30 | 29 | 1 | 96.67% |
PSO-VMD | PNN | 70 | 30 | 27 | 3 | 90.00% | |
PSO-VMD | ELM | 70 | 30 | 24 | 6 | 80.00% | |
PSO-VMD | SVM | 70 | 30 | 26 | 4 | 86.67% | |
EMD | PNN | 70 | 30 | 24 | 6 | 80.00% | |
Label 3 | IPSO-VMD | PNN | 70 | 30 | 30 | 0 | 100% |
PSO-VMD | PNN | 70 | 30 | 28 | 2 | 93.33% | |
PSO-VMD | ELM | 70 | 30 | 26 | 4 | 86.67% | |
PSO-VMD | SVM | 70 | 30 | 28 | 2 | 93.33% | |
EMD | PNN | 70 | 30 | 25 | 5 | 83.33% |
Bearing Status | Fault Diameter (mm) | Rotational Speed (rpm) | Data Labels | Number of Samples | Failure Label |
---|---|---|---|---|---|
Normal | 0 | 1750 | Normal_2 | 200 | 1 |
Rolling element Failure | 0.3556 | 1750 | B014_2 | 200 | 2 |
Inner ring Failure | 0.3556 | 1750 | IR014_2 | 200 | 3 |
Outer ring Failure | 0.3556 | 1750 | OR014@6_2 | 200 | 4 |
Normal | Ball Failure | Inner Failure | Outer Failure |
---|---|---|---|
Gears Status | Feature Extraction Algorithm | Classification Algorithm | Number of Training Sets | Number of Test Sets | Right | Misjudgment | Accuracy Rate |
---|---|---|---|---|---|---|---|
Normal | IPSO-VMD | PNN | 140 | 60 | 59 | 1 | 98.33% |
PSO-VMD | PNN | 140 | 60 | 57 | 3 | 95.00% | |
PSO-VMD | ELM | 140 | 60 | 55 | 5 | 91.67% | |
PSO-VMD | SVM | 140 | 60 | 58 | 2 | 96.67% | |
EMD | PNN | 140 | 60 | 54 | 6 | 90.00% | |
Rolling element failure | IPSO-VMD | PNN | 140 | 60 | 58 | 2 | 96.67% |
PSO-VMD | PNN | 140 | 60 | 56 | 4 | 93.33% | |
PSO-VMD | ELM | 140 | 60 | 54 | 6 | 90.00% | |
PSO-VMD | SVM | 140 | 60 | 57 | 3 | 95.00% | |
EMD | PNN | 140 | 60 | 53 | 7 | 88.33% | |
Inner ring Failure | IPSO-VMD | PNN | 140 | 60 | 60 | 0 | 100% |
PSO-VMD | PNN | 140 | 60 | 59 | 1 | 98.33% | |
PSO-VMD | ELM | 140 | 60 | 58 | 2 | 96.67% | |
PSO-VMD | SVM | 140 | 60 | 58 | 2 | 96.67% | |
EMD | PNN | 140 | 60 | 56 | 4 | 93.33% | |
Outer ring Failure | IPSO-VMD | PNN | 140 | 60 | 59 | 1 | 98.33% |
PSO-VMD | PNN | 140 | 60 | 58 | 2 | 96.67% | |
PSO-VMD | ELM | 140 | 60 | 57 | 3 | 95.00% | |
PSO-VMD | SVM | 140 | 60 | 58 | 2 | 96.67% | |
EMD | PNN | 140 | 60 | 55 | 5 | 91.67% |
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Li, Z.; Zou, C.; Chen, Z.; Lu, H.; Xie, S.; Zhang, W.; He, J. Research on the Fault Diagnosis Method of Rotating Machinery Based on Improved Variational Modal Decomposition and Probabilistic Neural Network Algorithm. Appl. Sci. 2024, 14, 7380. https://doi.org/10.3390/app14167380
Li Z, Zou C, Chen Z, Lu H, Xie S, Zhang W, He J. Research on the Fault Diagnosis Method of Rotating Machinery Based on Improved Variational Modal Decomposition and Probabilistic Neural Network Algorithm. Applied Sciences. 2024; 14(16):7380. https://doi.org/10.3390/app14167380
Chicago/Turabian StyleLi, Zhangjie, Chao Zou, Zhimin Chen, Hong Lu, Shiwen Xie, Wei Zhang, and Jiaqi He. 2024. "Research on the Fault Diagnosis Method of Rotating Machinery Based on Improved Variational Modal Decomposition and Probabilistic Neural Network Algorithm" Applied Sciences 14, no. 16: 7380. https://doi.org/10.3390/app14167380