Journal of System Simulation ›› 2022, Vol. 34 ›› Issue (09): 2074-2086.doi: 10.16182/j.issn1004731x.joss.21-0396
• VV&A Technology • Previous Articles Next Articles
Hailong Zhu1(), Ruxia Jia1, Liang Zhang2, Wei He1,3(
)
Received:
2021-05-06
Revised:
2021-08-15
Online:
2022-09-18
Published:
2022-09-23
Contact:
Wei He
E-mail:zhuhailong2018@vip.163.com;he_w_1980@163.com
CLC Number:
Hailong Zhu, Ruxia Jia, Liang Zhang, Wei He. Turbofan Engine Fault Prediction Based on Evidential Reasoning and Belief Rule Base[J]. Journal of System Simulation, 2022, 34(09): 2074-2086.
Table 2
Parameter setting of Er fusion process
名称 | 权重 | 参考值1 | 参考值2 | 参考值3 | 参考值4 | 参考值5 | 参考值6 |
---|---|---|---|---|---|---|---|
低压压气机出口总温度/ºR | 0.10 | 645 | 644 | 643 | 642 | 641 | 640 |
高压压气机出口总温度/ºR | 0.05 | 1 616 | 1 600 | 1 592 | 1 584 | 1 573 | 1 562 |
低压涡轮出口总温度/ºR | 0.10 | 1 442 | 1 433 | 1 421 | 1 409 | 1 397 | 1 377 |
高压压气机出口静压/psia | 0.04 | 571 | 568 | 564 | 559 | 554 | 548 |
物理风扇转速/(rpm) | 0.10 | 2 389.80 | 2 389.40 | 2 389.00 | 2 388.20 | 2 387.00 | 2 386.00 |
高压涡轮冷气流量/(lbm/s) | 0.10 | 9 235 | 9 200 | 9 162 | 9 120 | 9 052 | 9 017 |
低压涡轮冷气流量/(lbm/s) | 0.04 | 48.5 | 48.2 | 47.9 | 47.5 | 47.1 | 46.6 |
风扇换算转速/(rpm) | 0.10 | 538 | 535 | 532 | 527 | 523 | 518 |
核心机换算转速/(rpm) | 0.04 | 2 389.00 | 2 388.50 | 2 388.00 | 2 387.40 | 2 386.60 | 2 386.00 |
需求风扇转速/(rpm) | 0.04 | 8 290 | 8 257 | 8 216 | 8 183 | 8 155 | 8 099 |
需求风扇换算转速/(rpm) | 0.04 | 8.58 | 8.50 | 8.43 | 8.33 | 8.21 | 8.15 |
燃油油量与高压压气机出口静压的比率/(pps/psi) | 0.10 | 400 | 398 | 396 | 394 | 391 | 388 |
涵道比 | 0.10 | 39.90 | 39.80 | 39.60 | 39.40 | 38.80 | 38.10 |
抽气焓 | 0.05 | 24.00 | 23.80 | 23.70 | 23.50 | 23.20 | 22.80 |
Table 4
Initial belief degree of BRB
θl | ϑrϑs | {(S,β1,l),(A,β2,l),(B,β3,l), {(C,β4,l),(D,β5,l),(E,β6,l)} |
---|---|---|
1 | SS | {(S,1),(A,0),(B,0),(C,0),(D,0),(E,0)} |
1 | AS | {(S,0),(A,1),(B,0),(C,0),(D,0),(E,0)} |
1 | BS | {(S,0),(A,0),(B,1),(C,0),(D,0),(E,0)} |
1 | CS | {(S,0),(A,0),(B,0),(C,1),(D,0),(E,0)} |
1 | DS | {(S,0),(A,0),(B,0),(C,0),(D,1),(E,0)} |
1 | ES | {(S,0),(A,0),(B,0),(C,0),(D,0),(E,1)} |
1 | SA | {(S,1),(A,0),(B,0),(C,0),(D,0),(E,0)} |
1 | AA | {(S,0),(A,1),(B,0),(C,0),(D,0),(E,0)} |
1 | BA | {(S,0),(A,0),(B,1),(C,0),(D,0),(E,0)} |
1 | CA | {(S,0),(A,0),(B,0),(C,1),(D,0),(E,0)} |
1 | DA | {(S,0),(A,0),(B,0),(C,0),(D,1),(E,0)} |
1 | EA | {(S,0),(A,0),(B,0),(C,0),(D,0),(E,1)} |
1 | SB | {(S,1),(A,0),(B,0),(C,0),(D,0),(E,0)} |
1 | AB | {(S,0),(A,1),(B,0),(C,0),(D,0),(E,0)} |
1 | BB | {(S,0),(A,0),(B,1),(C,0),(D,0),(E,0)} |
1 | CB | {(S,0),(A,0),(B,0),(C,1),(D,0),(E,0)} |
1 | DB | {(S,0),(A,0),(B,0),(C,0),(D,1),(E,0)} |
1 | EB | {(S,0),(A,0),(B,0),(C,0),(D,0),(E,1)} |
1 | SC | {(S,1),(A,0),(B,0),(C,0),(D,0),(E,0)} |
1 | AC | {(S,0),(A,1),(B,0),(C,0),(D,0),(E,0)} |
1 | BC | {(S,0),(A,0),(B,1),(C,0),(D,0),(E,0)} |
1 | CC | {(S,0),(A,0),(B,0),(C,1),(D,0),(E,0)} |
1 | DC | {(S,0),(A,0),(B,0),(C,0),(D,1),(E,0)} |
1 | EC | {(S,0),(A,0),(B,0),(C,0),(D,0),(E,1)} |
1 | SD | {(S,1),(A,0),(B,0),(C,0),(D,0),(E,0)} |
1 | AD | {(S,0),(A,1),(B,0),(C,0),(D,0),(E,0)} |
1 | BD | {(S,0),(A,0),(B,1),(C,0),(D,0),(E,0)} |
1 | CD | {(S,0),(A,0),(B,0),(C,1),(D,0),(E,0)} |
1 | DD | {(S,0),(A,0),(B,0),(C,0),(D,1),(E,0)} |
1 | ED | {(S,0),(A,0),(B,0),(C,0),(D,0),(E,1)} |
1 | SE | {(S,1),(A,0),(B,0),(C,0),(D,0),(E,0)} |
1 | AE | {(S,0),(A,1),(B,0),(C,0),(D,0),(E,0)} |
1 | BE | {(S,0),(A,0),(B,1),(C,0),(D,0),(E,0)} |
1 | CE | {(S,0),(A,0),(B,0),(C,1),(D,0),(E,0)} |
Table 5
Rule matching table
规则状态 | 匹配度 | |
---|---|---|
t=334时刻 | t=335时刻 | |
S&S | 0 | 0 |
S&A | 0 | 0.358 4 |
S&B | 0 | 0.641 6 |
S&C | 0 | 0 |
S&D | 0 | 0 |
S&E | 0 | 0 |
A&S | 0.370 4 | 0 |
A&A | 0.370 4 | 0.358 4 |
A&B | 0.370 4 | 0.641 6 |
A&C | 0.370 4 | 0 |
A&D | 0.370 4 | 0 |
A&E | 0.370 4 | 0 |
B&S | 0.629 6 | 0 |
B&A | 0.629 6 | 0.358 4 |
B&B | 0.629 6 | 0.641 6 |
B&C | 0.629 6 | 0 |
B&D | 0.629 6 | 0 |
B&E | 0.629 6 | 0 |
C&S | 0 | 0 |
C&A | 0 | 0.358 4 |
C&B | 0 | 0.641 6 |
C&C | 0 | 0 |
C&D | 0 | 0 |
C&E | 0 | 0 |
D&S | 0 | 0 |
D&A | 0 | 0.358 4 |
D&B | 0 | 0.641 6 |
D&C | 0 | 0 |
D&D | 0 | 0 |
D&E | 0 | 0 |
E&S | 0 | 0 |
E&A | 0 | 0.358 4 |
E&B | 0 | 0.641 6 |
E&C | 0 | 0 |
E&D | 0 | 0 |
E&E | 0 | 0 |
Table 6
Optimized BRB confidence
θl | ϑrϑs | {(S,β1,l),(A,β2,l),(B,β3,l),(C,β4,l),(D,β5,l),(E,β6,l),} |
---|---|---|
1 | SS | {(S,0.160 8),(A, 0.056 9),(B,0.221 7),(C,0.0181),(D,0.378 7),(E,0.163 7)} |
1 | AS | {(S,0.390 8),(A,0.138 7),(B,0.147 8),(C,0.039 9),(D,0.045 0),(E,0.237 6)} |
1 | BS | {(S,0.430 1),(A,0.006 7),(B,0.042 6),(C,0.054 6),(D,0.094 4),(E,0.371 6)} |
1 | CS | {(S,0.092 0),(A,0.276 2),(B,0.108 9),(C,0.189 7),(D,0.083 5),(E,0.249 7)} |
1 | DS | {(S,0.181 3),(A,0.172 1),(B,0.360 2),(C,0.244 8),(D,0.017 9),(E,0.023 6)} |
1 | ES | {(S,0.227 7),(A,0.126 7),(B,0.180 4),(C,0.245 4),(D,0.110 4),(E,0.109 4)} |
1 | SA | {(S,0.232 9),(A,0.094 5),(B,0.3718),(C,0.076 1),(D,0.224 5),(E,0.000 1)} |
1 | AA | {(S,0.192 1),(A,0.1381),(B,0.436 3),(C,0.036 1),(D,0.135 2),(E,0.062 2)} |
1 | BA | {(S,0.161 9),(A,0.034 0),(B,0.054 6),(C,0.214 9),(D,0.084 5),(E,0.450 1)} |
1 | CA | {(S,0.5281),(A,0.0431),(B,0.114 8),(C,0.192 1),(D,0.021 4),(E,0.100 4)} |
1 | DA | {(S,0.081 0),(A,0.064 4),(B,0.425 5),(C,0.082 6),(D,0.306 2),(E,0.040 3)} |
1 | EA | {(S,0.1051),(A,0.292 4),(B,0.050 1),(C,0.087 0),(D,0.383 9),(E,0.081 5)} |
1 | SB | {(S,0.377 6),(A,0.105 6),(B,0.241 0),(C,0.064 2),(D,0.181 9),(E,0.029 8)} |
1 | AB | {(S,0.119 9),(A,0.171 0),(B,0.037 5),(C,0.1951),(D,0.115 7),(E,0.360 7)} |
1 | BB | {(S,0.046 2),(A,0.215 6),(B,0.097 4),(C,0.442 7),(D,0.137 0),(E,0.0611)} |
1 | CB | {(S,0.037 4),(A,0.104 6),(B,0.069 7),(C,0.074 9),(D,0.127 6),(E,0.585 9)} |
1 | DB | {(S,0.323 4),(A,0.036 4),(B,0.134 4),(C,0.081 7),(D,0.177 0),(E,0.247 1)} |
1 | EB | {(S,0.147 5),(A,0.103 6),(B,0.040 5),(C,0.315 8),(D,0.100 0),(E,0.292 5)} |
1 | SC | {(S,0.131 4),(A,0.278 3),(B,0.315 9),(C,0.157 4),(D,0.089 5),(E,0.027 5)} |
1 | AC | {(S,0.236 3),(A,0.051 7),(B,0.121 6),(C,0.231 0),(D,0.344 8),(E,0.014 6)} |
1 | BC | {(S,0.019 1),(A,0.076 3),(B,0.029 8),(C,0.072 0),(D,0.053 8),(E,0.749 0)} |
1 | CC | {(S,0.227 8),(A,0.0418),(B,0.009 3),(C,0.021 5),(D,0.000 0),(E,0.699 5)} |
1 | DC | {(S,0),(A,0.005 4),(B,0.016 8),(C,0),(D,0.000 4),(E,0.979 2)} |
1 | EC | {(S,0.303 6),(A,0.142 7),(B,0.087 6),(C,0.023 9),(D,0.088 6),(E,0.353 6)} |
1 | SD | {(S,0.2470),(A,0.1759),(B, 0.2034),(C,0.2412),(D,0.0949),(E,0.0375)} |
[1] | 韦祥, 李本威, 杨欣毅, 等. 某型涡扇发动机燃调故障联合仿真[J]. 系统仿真学报, 2018, 30(10): 3923-3932. |
Wei Xiang, Li Benwei, Yang Xinyi, et al. Fault Co-simulation of Fuel Regulator in a Certain Type of Turbofan Engine[J]. Journal of System Simulation, 2018, 30(10): 3923-3932. | |
[2] | 蔡景, 胡维, 陈曦. 全修复策略下FADEC系统多故障TLD仿真分析[J]. 航空动力学报, 2020, 35(4): 823-831. |
Cai Jing, Hu Wei, Chen Xi. TLD Simulation Analysis with Multiple Faults for FADEC System under Full Repair Policy[J]. Journal of Aerospace Power, 2020, 35(4): 823-831. | |
[3] | 年夫顺. 关于故障预测与健康管理技术的几点认识[J].仪器仪表学报, 2018, 39(8): 1-14. |
Nian Fushun. Viewpoints About the Prognostic and Health Management[J]. Chinese Journal of Scientific Instrument, 2018, 39(8): 1-14. | |
[4] | Samir Khan, Takehisa Yairi. A Review on the Application of Deep Learning in System Health Management[J].Mechanical System and Signal Processing (S0888-3270), 2018, 107: 241-265. |
[5] | Farzaneh Ahmadzadeh, Jan Lundberg. Remaining Useful Life Estimation: Review[J]. International Journal of System Assurance Engineering and Management (S0975-6809), 2014, 5(4): 461-474. |
[6] | 焦李成, 杨淑媛, 刘芳, 等. 神经网络七十年:回顾与展望[J]. 计算机学报, 2016, 39(8): 1697-1716. |
Jiao Licheng, Yang Shuyuan, Liu Fang, et al. Seventy Years beyond Neural Networks: Retrospect and Prospect[J]. Chinese Journal of Computers, 2016, 39(8): 1697-1716. | |
[7] | Li Guofa, Wang Yanbo, He Jialong, et al. Tool Wear State Recognition Based on Gradient Boosting Decision Tree and Hybrid Classification RBM[J]. International Journal of Advanced Manufacturing Technology (S0268-3768), 2020, 110(18): 511-522. |
[8] | 余嘉熹, 李奇, 陈维荣, 等. 基于随机森林算法的大功率质子交换膜燃料电池系统故障分类方法[J]. 中国电机工程学报, 2020, 40(17): 5591-5599. |
Yu Jiaxi, Li Qi, Chen Weirong, et al. A Fault Classification Method of High-power Proton Exchange Membrane Fuel Cell Systems Based on the Random Forest[J]. CSEE, 2020, 40 (17): 5591-5599. | |
[9] | 郑玉巧, 魏剑峰, 朱凯, 等. 风力机主轴承故障监测方法[J]. 振动.测试与诊断, 2021, 41(2): 341-347,415. |
Zheng Yuqiao, Wei Jianfeng, Zhu Kai, et al. Fault Monitoring Method of Wind Turbine Main Bearing[J].Journal of Vibration, Measurement & Diagnosis, 2021, 41(2): 341-347,415. | |
[10] | 胡爱军, 南冰. 基于自适应概率主成分分析的滚动轴承故障特征增强方法[J]. 振动与冲击, 2017, 36(19): 145-150. |
Hu Aijun, Nan Bing. Fault Feature Enhancement Method for Rolling Bearing Based on Adaptive Probabilistic Principal Component Analysis[J]. Journal of Vibration and Shock, 2017, 36(19): 145-150. | |
[11] | David Siegel, Jay Lee. An Auto-associative Residual Processing and K-means Clustering Approach for Anemometer Health Assessment[J]. International Journal of Prognostics and Health Management (S2153-2648), 2011, 2(2): 50-61. |
[12] | 陈志强, 陈旭东, José Valente de Olivira, 等. 深度学习在设备故障预测与健康管理中的应用[J]. 仪器仪表学报, 2019, 40(9): 206-226. |
Chen Zhiqiang, Chen Xudong, Valente de Olivira José, et al. Application of Deep Learning in Equipment Prognostics and Health Management[J]. Chinese Journal of Scientific Instrument, 2019, 40(9): 206-226. | |
[13] | 闫理跃, 王厚军, 刘震. 一种结合噪声辅助技术和现场数据的模拟电路实时可靠度预测方法[J]. 兵工学报, 2019, 40(2): 326-333. |
Yan Liyue, Wang Houjun, Liu Zhen. A Real-time Reliability Prediction Approach for Analog Circuits Based on Noise-Assisted Technique and On-site Data Update[J]. Acta Armamentarii, 2019, 40(2): 326-333. | |
[14] | 郭济鸣, 齐金平, 李兴运. 基于模糊动态故障树的动车制动系统可靠性分析[J]. 中国机械工程, 2019, 30(13): 1585-1589,1599. |
Guo Jiming, Qi Jinping, Li Xingyun. Reliability Analysis of EMUs Braking Systems with Fuzzy Dynamic Fault Tree[J]. China Mechanical Engineering, 2019, 30(13): 1585-1589,1599. | |
[15] | 陈玉昆, 高崎, 陈健, 等. 初始保障期内装备维修器材保障决策方法[J].火力与指挥控制, 2020, 45(2): 23-27. |
Chen Yukun, Gao Qi, Chen Jian, et al. Support Decision Method for Maintenance Supply in Initial Equipment Support Period[J]. Fire Control & Command Control, 2020, 45(2): 23-27. | |
[16] | 谷梦瑶, 陈友玲, 王新龙. 多退化变量下基于实时健康度的相似性寿命预测方法[J]. 计算机集成制造系统, 2017, 23(2): 362-372. |
Gu Mengyao, Chen Youling, Wang Xinlong. Multi-index Modeling for Similarity-Based Residual Life Estimation Based on Real-time Health Degree[J]. Computer Integrated Manufacturing Systems. 2017, 23(2): 362-372. | |
[17] | 黄金泉, 王启航, 鲁峰. 航空发动机气路故障诊断研究现状与展望[J]. 南京航空航天大学学报, 2020, 52(4): 507-522. |
Huang Jinquan, Wang Qihang, Lu Feng. Research Status and Prospect of Gas Path Fault Diagnosis for Aeroengine[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2020, 52(4): 507-522. | |
[18] | Yang Jianbo, Liu Jun, Wang Jin, et al. Belief Rule-Base Inference Methodology Using the Evidential Reasoning Approach-RIMER[J]. IEEE Transactions on Systems, Man & Cybernetics (S2168-2267): Part A, 2006, 36(2): 266-285. |
[19] | Yang Jianbo, Xu Dongling. Evidential Reasoning Rule for Evidence Combination[J]. Artificial Intelligence (S0004-3702), 2013, 205: 1-29. |
[20] | He Wei, Yu Chuanqiang, Zhou Guohui, et al. A New Belief-Rule-Based Method for Fault Diagnosis of Wireless Sensor Network[J]. IEEE Access (S2169-3536), 2018, 6: 9404-9419. |
[21] | Li Gailing, Zhou Zhijie, Hu Changhua, et al. A New Safety Assessment Model for Complex System Based on the Conditional Generalized Minimum Variance and the Belief Rule Base[J]. Safety Science (S0925-7535), 2017, 93: 108-120. |
[22] | Sonmez M, Holt G, Yang J, et al. Applying Evidential Reasoning to Prequalifying Construction Contractors[J].Journal of Management in Engineering (S0742-597X), 2002, 18(3): 111-119. |
[23] | Kong Guilan. Belief Rule-Based Inference for Predicting Trauma Outcome[J]. Knowledge-Based Systems (S0950-7051), 2016, 95: 35-44. |
[24] | Abhinav Saxena, Kai Goebel, Don Simon, et al. Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation[C]// 2008 International Conference on Prognostics and Health Management. Denver, CO, USA:IEEE, 2008: 508. |
[1] | Meng Xiaofan, Song Hua. Fault Prediction of Satellite Attitude Control System Based on Neural Network [J]. Journal of System Simulation, 2019, 31(11): 2499-2508. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||