基于非线性模型的间冷回热涡扇发动机气路故障诊断方法研究

王义楠; 丰国龙; 陈毓智; 缑林峰

doi:10.13224/j.cnki.jasp.20250053

基于非线性模型的间冷回热涡扇发动机气路故障诊断方法研究

doi: 10.13224/j.cnki.jasp.20250053

王义楠^1,,
丰国龙¹,
陈毓智^{1, 3,*, ,},
缑林峰^{1, 2, 3}

1.
西北工业大学动力与能源学院，西安 710129
2.
西北工业大学国家卓越工程师学院，西安 710072
3.
中国航发四川燃气涡轮研究院高空模拟技术重点实验室，四川绵阳 621000

基金项目: 国家自然科学基金（52402520)

详细信息

作者简介:
王义楠（2003-），男，航空发动机智能控制与健康监测。E-mail：2021301846@mail.nwpu.edu.cn

通讯作者:
陈毓智（1989-），男，副教授，博士，燃气涡轮发动机总体性能与健康监测。E-mail：yuzhi.chen@nwpu.edu.cn

中图分类号: V235.13
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出版历程
- 收稿日期: 2025-01-28
- 网络出版日期: 2026-08-10

Research on gas path fault diagnosis method of intercooled recuperated turbofan engine based on nonlinear model

Wang Yinan^1
,,
Feng Guolong¹,
Chen Yuzhi^{1, 3,*
, ,},
Gou Linfeng^{1, 2, 3}

1.
School of Power and Energy，Northwestern Polytechnical University，Xi’an 710129，China
2.
National Elite Institute of Engineering，Northwestern Polytechnical University，Xi’an 710072，China
3.
Science and Technology on Altitude Simulation Laboratory，Sichuan Gas Turbine Establishment，Aero Engine Corporation of China，Mianyang Sichuan 621000，China

摘要

摘要:
间冷回热涡扇发动机相对普通三轴涡扇发动机的健康参数更多，导致故障诊断计算量变大。为提高间冷回热涡扇发动机多部件退化下气路故障诊断速度，开发了一种适用于间冷回热涡扇发动机的快速气路故障诊断方法。该方法在传统嵌套迭代诊断架构的基础上设计了一种非嵌套迭代架构，考虑了包括6个旋转部件以及2个换热器部件的退化情况，通过分割部件使发动机部件匹配和故障诊断可以在同一个牛顿-拉夫逊迭代算法中完成，并开展了传感器选择优化以提升测量参数对于不同部件退化的辨识能力。在对发动机全寿命周期内的所有工况进行仿真测试的结果显示，该方法的最大诊断误差为0.0036%，平均误差为0.0008%，平均计算时间小于0.063 s。与传统诊断架构相比，这种非嵌套的架构可以在保证诊断精度的前提下显著降低了子部件调用次数和故障诊断时间，为克服多部件故障高精度诊断与快速诊断需求相互冲突提供了方法和理论支撑。
- 间冷回热涡扇发动机 /
- 非线性模型 /
- 气路故障诊断 /
- 牛顿-拉夫逊算法 /
- 传感器选择优化
Abstract:
The intercooled recuperated turbofan engine has more health parameters than the ordinary three-shaft turbofan engine, which leads to a larger calculation amount of fault diagnosis. To enhance the speed of gas path fault diagnosis of intercooled recuperated turbofan engine under multi-component degradation, a fast gas path fault diagnosis method for intercooled recuperated turbofan engine was developed. Based on the traditional nested iterative diagnosis architecture, a non-nested iterative architecture was designed, which considered the degradation of 6 rotating components and 2 heat exchanger components. By dividing the components, the engine component matching and fault diagnosis could be completed in the same Newton-Raphson iterative algorithm. The sensor selection optimization was carried out to improve the ability of measuring parameters to identify the degradation of different components. The results of simulation test on all conditions of engine life cycle showed that the maximum diagnostic error of this method was 0.0036%, the average error was 0.0008%, and the average calculation time was less than 0.063 seconds. Compared with the traditional diagnostic architecture, this non-nested architecture can significantly reduce the number of sub-component calls and fault diagnosis time while ensuring the diagnostic accuracy. It provides the method and theoretical support for overcoming the conflict between high-precision fault diagnosis and rapid diagnosis of multi-component faults.
- intercooled recuperated turbofan engine /
- nonlinear model /
- gas path fault diagnosis /
- Newton-Raphson algorithm /
- sensor selection optimization

HTML

图 1 三轴间冷回热涡扇发动机结构示意图

Figure 1. Schematic diagram of the three-shaft intercooled recuperated turbofan engine

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图 2 气路故障诊断流程图

Figure 2. Schematic diagram of gas fault diagnosis

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图 3 非嵌套迭代算法中子部件计算流程

Figure 3. Non-nested iterative algorithm subcomponent calculation schedule

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图 4 敏感性分析

Figure 4. Sensitivity analysis

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图 5 相似故障的相关性分析

Figure 5. Correlation analysis of similar faults

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图 6 测量参数组合条件数排序

Figure 6. Measurement parameters combination condition number order

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图 7 不同测量参数选择方案的诊断效果

Figure 7. Diagnostic effect of different measurement parameter selection schemes

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图 8 嵌套与非嵌套迭代算法诊断结果对比

Figure 8. Comparison of nested and non-nested iterative algorithm diagnosis results

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表 1 健康参数参考值^[25]

Table 1. Reference values of health parameters^[25]

编号	健康参数	健康状态1	健康状态2
1	$ {X}_{\text{fan,e}} $	0.9858	0.9715
2	$ {X}_{\text{fan,f}} $	0.9818	0.9635
3	$ {X}_{\text{IPC,e}} $	0.9870	0.9739
4	$ {X}_{\text{IPC,f}} $	0.9800	0.9600
5	$ {X}_{\text{HPC,e}} $	0.9530	0.9060
6	$ {X}_{\text{HPC,f}} $	0.9297	0.8594
7	$ {X}_{\text{HPT,e}} $	0.9810	0.9619
8	$ {X}_{\text{HPT,f}} $	1.0129	1.0257
9	$ {X}_{\text{IPT,e}} $	0.9878	0.9756
10	$ {X}_{\text{IPT,f}} $	1.0075	1.0150
11	$ {X}_{\text{LPT,e}} $	0.9946	0.9892
12	$ {X}_{\text{LPT,f}} $	1.0021	1.0042
13	$ {X}_{\text{IC,e}} $	0.9910	0.9820
14	$ {X}_{\text{RC,e}} $	1.0006	1.0012

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表 2 备选测量参数

Table 2. Alternative measurement parameter

序号	参数	定义
1	$ {T}_{13} $	风扇外涵出口温度
2	$ {T}_{21} $	风扇内涵出口温度
3	$ {T}_{24} $	间冷器热侧进口温度
4	$ {T}_{25} $	间冷器热侧出口温度
5	$ {T}_{43} $	高压涡轮出口温度
6	$ {T}_{47} $	中压涡轮出口温度
7	$ {T}_{5} $	低压涡轮出口温度
8	$ {T}_{7} $	回热器热侧出口温度
9	$ {T}_{16} $	外涵尾喷管进口温度
10	$ {p}_{25} $	间冷器热侧出口压力
11	$ {p}_{7} $	回热器热侧出口压力
12	$ {p}_{16} $	外涵尾喷管进口压力

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表 3 健康参数偏差对测量参数影响矩阵

Table 3. Influence matrix of health parameter deviation on measurement parameter

参数	$ {X}_{\text{fan,e}} $	$ {X}_{\text{fan,f}} $	$ {X}_{\text{IPC,e}} $	$ {X}_{\text{IPC,f}} $	$ {X}_{\text{HPC,e}} $	$ {X}_{\text{HPC,f}} $	$ {X}_{\text{HPT,e}} $	$ {X}_{\text{HPT,f}} $	$ {X}_{\text{IPT,e}} $	$ {X}_{\text{IPT,f}} $	$ {X}_{\text{LPT,e}} $	$ {X}_{\text{LPT,f}} $	$ {X}_{\text{IC,e}} $	$ {X}_{\text{RE,e}} $
$ {T}_{13} $	0.029	0.142	−0.023	−0.012	−0.002	−0.015	0.041	0.076	0.004	−0.049	−0.179	−0.121	−0.002	−0.125
$ {T}_{21} $	0.008	0.062	−0.012	−0.006	−0.001	−0.008	0.022	0.041	0.003	−0.025	−0.095	−0.065	−0.001	−0.066
$ {T}_{24} $	0.064	0.043	0.084	0.162	0.085	−0.004	0.151	0.189	−0.390	−0.868	0.032	0.821	0.011	−0.154
$ {T}_{25} $	0.045	0.095	0.028	0.070	0.039	−0.010	0.093	0.129	−0.181	−0.433	−0.079	0.326	0.249	−0.139
$ {T}_{43} $	0.107	0.210	0.543	0.186	0.426	0.036	0.599	0.812	0.588	0.564	0.261	−1.154	0.060	−0.315
$ {T}_{47} $	0.103	0.227	0.567	0.165	0.441	0.038	0.516	0.340	0.517	0.589	0.259	−1.441	0.062	−0.314
$ {T}_{5} $	0.173	0.131	0.622	0.184	0.470	0.054	0.619	0.828	0.765	0.893	0.778	−1.250	0.072	−0.224
$ {T}_{7} $	0.132	0.139	0.381	0.143	0.272	0.117	0.110	0.032	0.308	0.968	0.361	−0.362	0.123	0.307
$ {T}_{16} $	0.039	0.138	−0.029	0.001	−0.005	−0.016	0.040	0.072	−0.052	−0.152	−0.148	0.017	−0.037	−0.131
$ {p}_{25} $	−0.078	−0.327	−0.897	−0.168	1.184	0.209	1.459	1.717	−1.052	−3.337	0.013	1.797	0.142	−0.298
$ {p}_{7} $	−0.007	−0.270	−0.409	−0.152	−0.298	−0.046	−0.358	−0.458	−0.378	−0.417	0.387	1.006	0.023	0.053
$ {p}_{16} $	−0.354	−0.535	−0.002	−0.005	0.025	−0.022	0.105	0.174	0.037	−0.057	−0.344	−0.291	−0.009	−0.222

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表 4 各健康参数间相关性系数值

Table 4. Correlation coefficient values among various health parameters

参数	$ {X}_{\text{fan,e}} $	$ {X}_{\text{fan,f}} $	$ {X}_{\text{IPC,e}} $	$ {X}_{\text{IPC,f}} $	$ {X}_{\text{HPC,e}} $	$ {X}_{\text{HPC,f}} $	$ {X}_{\text{HPT,e}} $	$ {X}_{\text{HPT,f}} $	$ {X}_{\text{IPT,e}} $	$ {X}_{\text{IPT,f}} $	$ {X}_{\text{LPT,e}} $	$ {X}_{\text{LPT,f}} $	$ {X}_{\text{IC,e}} $	$ {X}_{\text{RE,e}} $
$ {X}_{\text{fan,e}} $	1
$ {X}_{\text{fan,f}} $	0.88	1
$ {X}_{\text{IPC,e}} $	0.52	0.67	1
$ {X}_{\text{IPC,f}} $	0.56	0.68	0.93	1
$ {X}_{\text{HPC,e}} $	0.10	−0.04	−0.13	0.03	1
$ {X}_{\text{HPC,f}} $	0.14	−0.06	−0.18	−0.05	0.90	1
$ {X}_{\text{HPT,e}} $	0.06	−0.05	−0.14	0.03	0.99	0.84	1
$ {X}_{\text{HPT,f}} $	0.06	−0.02	−0.09	0.07	0.97	0.78	0.99	1
$ {X}_{\text{IPT,e}} $	0.45	0.59	0.95	0.78	−0.10	−0.16	−0.12	−0.06	1
$ {X}_{\text{IPT,f}} $	0.39	0.55	0.90	0.69	−0.45	−0.43	−0.47	−0.43	0.93	1
$ {X}_{\text{LPT,e}} $	0.67	0.34	0.47	0.40	0.23	0.30	0.17	0.17	0.50	0.39	1
$ {X}_{\text{LPT,f}} $	−0.37	−0.57	−0.93	−0.77	0.06	0.14	0.05	−0.01	−0.99	−0.89	−0.38	1
$ {X}_{\text{IC,e}} $	0.24	0.10	−0.03	0.10	0.41	0.48	0.36	0.33	−0.09	−0.20	0.23	0.11	1
$ {X}_{\text{RE,e}} $	0.21	0.10	0.12	0.02	−0.30	0.10	−0.46	−0.53	0.11	0.29	0.27	−0.02	0.12	1

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表 5 不同组合的测量参数及条件数

Table 5. Different combinations of measurement parameters and conditions

序号	$ {T}_{24} $	$ {T}_{25} $	$ {T}_{47} $	$ {T}_{7} $	$ {p}_{7} $	$ {p}_{16} $	$ \mathrm{\lg }K $
1	√	−	−	√	√	√	0.61
2	−	√	−	√	√	√	0.65
3	√	−	√	√	−	√	0.68
4	−	√	√	−	√	√	0.70
5	√	−	√	√	√	√	0.74
6	−	√	√	√	√	√	0.81

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表 6 嵌套与非嵌套迭代算法诊断效果对比

Table 6. Comparison of diagnostic effect between nested and non-nested iterative algorithms

诊断性能对比	嵌套迭代	非嵌套迭代
最大相对误差/%	0.0043	0.0036
平均相对误差/%	0.0011	0.0008
平均调用子部件次数	14112	112
平均计算时间/s	1.213	0.063

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