Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO
Abstract
:1. Introduction
2. SVR Prediction Model
3. Improved PSO Algorithm
3.1. Guided Mutation Strategy
3.2. PSO Performance Verification
4. Aeroengine Wear Prediction Case
4.1. Multi-Step Prediction
4.2. Single-Step Prediction
4.3. Comparison with Other Methods
5. Conclusions
- (1)
- The guided mutation strategy can increase the diversity of particles and improve the global optimal performance. Therefore, GMPSO is basically better than other PSO variants.
- (2)
- The structure parameters and the embedding dimension have an obvious impact on the prediction accuracy; it is necessary to optimize them to improve the prediction accuracy of SVR.
- (3)
- Multi-step prediction has relatively lower accuracy than that of single-step prediction due to the error cumulative effect, but the time costs of the two forms exhibit the opposite trend.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Test Function | Search Range | Dimension | Optimal Solution | Optimal Extremum |
---|---|---|---|---|
Sphere: | [−100, 100] | 50 | (0, 0,…,0)50 | 0 |
Schaffer: | [−100, 100] | 50 | (0, 0,…,0)50 | 0 |
Griewank: | [−100, 100] | 50 | (0, 0,…,0)50 | 0 |
Ackley: | [−100, 100] | 50 | (0, 0,…,0)50 | 0 |
Rastrigin: | [−100, 100] | 50 | (0, 0,…,0)50 | 0 |
Rosenbrock: | [−100, 100] | 50 | (1, 1,…,1)50 | 0 |
SDPF: | [−100, 100] | 50 | (0, 0,…,0)50 | 0 |
RHEF: | [−100, 100] | 50 | (0, 0,…,0)50 | 0 |
Function | SRPSO | ALCPSO | SLPSO | IWPSO | SFPSO | DNPSO | SAPSO | MAPSO | GMPSO |
---|---|---|---|---|---|---|---|---|---|
fSph | 0.00025 | 8.4617 | 4174.53 | 474.278 | 1956.71 | 0.1176 | 132.214 | 0.0042 | 3.98 × 10−68 |
fSch | 0.4481 | 0.4548 | 0.4928 | 0.4451 | 0.4886 | 0.4995 | 0.4455 | 0.3816 | 6.67 × 10−11 |
fGri | 0.0055 | 0.3550 | 2.0718 | 1.1099 | 1.4751 | 0.0105 | 0.9869 | 0.0041 | 0 |
fAck | 21.1626 | 19.2621 | 18.6374 | 19.7884 | 20.6655 | 20.0057 | 19.4486 | 18.8497 | 0 |
fRas | 193.353 | 950.846 | 4564.84 | 1124.34 | 2613.14 | 471.213 | 1176.23 | 204.646 | 0 |
fRos | 155.083 | 5219.32 | 8819.17 | 5891.12 | 2059.34 | 168.554 | 2634.42 | 148.853 | 47.3102 |
fSDPF | 3.15 × 108 | 1.22 × 1024 | 6.12 × 1052 | 1.48 × 1038 | 1.43 × 1039 | 0.1566 | 9.66 × 1035 | 7.84 × 107 | 1.77 × 10−101 |
fRHEF | 917.416 | 1033.42 | 9612.11 | 10436.4 | 6855.24 | 675.486 | 2506.73 | 1258.76 | 4.37 × 10−56 |
True Values | GMPSO-SVR | PSO-SVR | ||||||
---|---|---|---|---|---|---|---|---|
Exp.No1 | Exp.No2 | Exp.No1 | Exp.No2 | |||||
Prediction | RE | Prediction | RE | Prediction | RE | Prediction | RE | |
5.98 | 5.9842 | 0.07% | 5.9842 | 0.07% | 6.1838 | 3.41% | 6.1387 | 2.65% |
6.02 | 6.0338 | 0.23% | 6.0338 | 0.23% | 5.9301 | 1.49% | 6.0604 | 0.67% |
5.75 | 5.9962 | 4.28% | 5.9962 | 4.28% | 5.7172 | 0.57% | 5.9905 | 4.18% |
6.23 | 5.8659 | 5.85% | 5.8659 | 5.85% | 5.6559 | 9.21% | 5.8062 | 6.8% |
5.88 | 5.7425 | 2.34% | 5.7425 | 2.34% | 5.4062 | 8.06% | 5.7914 | 0.15% |
(C, γ, ε, D)best | (0.1, 13.5887, 0.01, 13) | (0.1, 13.5887, 0.01, 13) | (100, 0.01, 0.01, 11) | (0.1, 11.8511, 0.01, 11) | ||||
Fitness value | 0.049% | 0.049% | 0.053% | 0.051% | ||||
Average RE | 2.55% | 2.55% | 4.55% | 2.89% |
True Values | Multi-Step GMPSO-SVR | Single-Step GMPSO-SVR | ||||
---|---|---|---|---|---|---|
Prediction | RE | (C, γ, ε, D)best | Fitness Value | Prediction | RE | |
5.98 | 5.9842 | 0.07% | (0.1, 13.5887, 0.01, 13) | 0.049% | 5.9842 | 0.07% |
6.02 | 6.0338 | 0.23% | (0.1, 11.6259, 0.01, 13) | 0.052% | 6.0213 | 0.022% |
5.75 | 5.9962 | 4.28% | (0.1, 4.9941, 0.01, 16) | 0.056% | 5.7554 | 0.094% |
6.23 | 5.8659 | 5.85% | (0.1, 54.7909, 0.01, 26) | 0.0489% | 6.2236 | 0.103% |
5.88 | 5.7425 | 2.34% | (0.1, 16.4312, 0.01, 3) | 0.0442% | 5.8819 | 0.032% |
Average RE | 2.55% | 0.064% | ||||
Elapsed time | 70.32 s | 358.35 s |
True Values | Multi-Step GMPSO-SVR | Single-Step GMPSO-SVR | BP Network | ARMA | ||||
---|---|---|---|---|---|---|---|---|
Prediction | RE | Prediction | RE | Prediction | RE | Prediction | RE | |
5.98 | 5.9842 | 0.07% | 5.9842 | 0.07% | 5.6074 | 6.23% | 5.5091 | 7.87% |
6.02 | 6.0338 | 0.23% | 6.0213 | 0.022% | 6.3361 | 5.25% | 5.5980 | 7.01% |
5.75 | 5.9962 | 4.28% | 5.7554 | 0.094% | 5.8454 | 1.66% | 6.2631 | 8.92% |
6.23 | 5.8659 | 5.85% | 6.2236 | 0.103% | 5.9702 | 4.17% | 4.7603 | 23.59% |
5.88 | 5.7425 | 2.34% | 5.8819 | 0.032% | 5.8382 | 0.71% | 3.8466 | 34.58% |
Average RE | 2.55% | 0.064% | 3.60% | 16.39% |
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Zheng, B.; Gao, F.; Ma, X.; Zhang, X. Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO. Appl. Sci. 2021, 11, 10592. https://doi.org/10.3390/app112210592
Zheng B, Gao F, Ma X, Zhang X. Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO. Applied Sciences. 2021; 11(22):10592. https://doi.org/10.3390/app112210592
Chicago/Turabian StyleZheng, Bo, Feng Gao, Xin Ma, and Xiaoqiang Zhang. 2021. "Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO" Applied Sciences 11, no. 22: 10592. https://doi.org/10.3390/app112210592
APA StyleZheng, B., Gao, F., Ma, X., & Zhang, X. (2021). Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO. Applied Sciences, 11(22), 10592. https://doi.org/10.3390/app112210592