Hierarchical entropy weight performance evaluation method of aero piston engine
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摘要:
针对航空活塞发动机多目标、多准则的性能评价问题，采用层次分析法和熵权法将其转化为单目标、多层次的评价，建立不同工况下的性能指标、权重分配和评价体系。用遗传算法BP（back propagation）神经网络（GA-BP）模型对其性能衰退状态进行计算判定，并结合实验与仿真，验证了该性能评价体系的正确性。以喷油孔异常导致发动机性能异常衰退为案例，从燃烧的角度探究其性能衰退出现的机理。结果表明：当发动机零部件出现异常时，层次熵权性能评价方法能较好地反映发动机当前的性能状态，可对其安全性做出准确判断；且GA-BP计算模型的平均绝对百分比误差比BP、RBF（radial basis function）、Elman模型分别减小了3.5208%、0.7027%、3.7854%，具有较高的精确性。
Abstract:In response to multi-objective and multi-criteria performance evaluation problem for aero piston engine, the analytic hierarchy process (AHP) and entropy weight method (EWM) were used to transform it into single-objective and multi-level evaluation, and establish performance indicators, weight allocation, and evaluation systems under different operating conditions. Genetic algorithm-back propagation (GA-BP) neural network was employed to calculate and estimate its performance degradation state, the correctness of the performance evaluation system was verified by the joint simulation experiment. For the abnormal fuel injection holes leading to abnormal engine performance degradation as an example, this study explored the mechanism of its performance degradation from the perspective of combustion. The results showed that, when there were abnormal components of engine, the hierarchical entropy weight performance evaluation was a better method to reflect current performance status of the engine and make accurate judgments on its safety. The GA-BP computational model had a high accuracy, whose mean absolute percent error (MAPE) decreased by 3.5208%, 0.7027% and 3.7854%, respectively, compared with BP, radial basis function (RBF) and Elman models.
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图 2 压燃式航空活塞发动机 AMESim 模型
Figure 2. AMESim model of compression-ignition aero piston engine
图 3 不同起飞海拔下各工况主观权重占比
Figure 3. Subjective weight ratio of each condition at different take-off altitudes
图 4 不同工况下各个指标主观权重占比
Figure 4. Subjective weight ratio of each indicator under different conditions
图 5 不同工况下各个指标客观权重占比
Figure 5. Objective weight ratio of each indicator under different conditions
图 6 不同工况下各个指标复合权重占比
Figure 6. Composite weight ratio of each indicator under different conditions
图 8 不同起飞海拔下各工况复合权重占比
Figure 8. Composite weight ratio of each condition at different take-off altitudes
图 9 遗传算法进化过程适应度变化曲线
Figure 9. Fitness change curve of genetic algorithm evolution process
图 10 不同神经网络模型的评估值与实际值对比
Figure 10. Comparison of estimation values and actual values for different neural network models
图 11 不同神经网络模型的评估值误差对比
Figure 11. Comparison of estimation values errors for different neural network models
图 12 不同起飞海拔下航空活塞发动机的综合性能值
Figure 12. Comprehensive performance values of aero piston engine at different take-off altitudes
图 13 1900 m起飞工况综合性能衰退对缸压的影响（n=2300 r/min）
Figure 13. Effect of general performance degradation on cylinder pressure at 1900 m take-off operating condition （n=2300 r/min）
图 14 2500 m额定工况下综合性能衰退对缸压的影响（n=2180 r/min）
Figure 14. Effect of comprehensive performance degradation on cylinder pressure at 2500 m rated operating condition （n=2180 r/min）
图 15 起飞工况下综合性能衰退对功率的影响（n=2300 r/min）
Figure 15. Effect of comprehensive performance degradation for power at take-off operating condition （n=2300 r/min）
图 16 额定工况下综合性能衰退对功率的影响（n=2180 r/min）
Figure 16. Effect of comprehensive performance degradation for power at rated operating condition （n=2180 r/min）
表 1 航空活塞发动机综合性能评估等级
Table 1. Comprehensive performance evaluation levels of aero piston engine
评估等级 $ \delta $范围 等级描述 合格 $ 0 < \delta \leqslant 0.1 $ 可正常运行 轻度衰退 $ 0.1 < \delta \leqslant 0.2 $ 略有衰退 中度衰退 $ 0.2 < \delta \leqslant 0.3 $ 衰退明显，有危险 严重衰退 $ 0.3 < \delta \leqslant 0.4 $ 故障的可能性很大 故障 $ 0.4 < \delta \leqslant 1 $ 出现故障 
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表 2 航空活塞发动机主要参数
Table 2. Main parameters of aero piston engine
参数 数值或说明 缸径/mm 83 活塞行程/mm 92 排量/L 1.991 压缩比 18.0 最大转速/（r/min） 3887 最大连续功率/kW 99 进气方式 废气涡轮增压+中冷 燃油供给 高压共轨直喷燃油系统
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表 3 神经网络模型评估值的误差对比
Table 3. Error comparison of neural network model estimation values
神经
网络模型方均
根误差平均
绝对误差平均绝对
百分比误差/%GA-BP 0.0054 0.0050 0.7666 BP 0.0320 0.0280 4.2874 RBF 0.0123 0.0095 1.4693 Elman 0.0367 0.0295 4.552
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