| 引用本文: | 刘久富,孙燕,于杰,刘文渊,刘海阳.火箭发动机启动过程的部分可观Petri网故障诊断[J].哈尔滨工业大学学报,2017,49(3):15.DOI:10.11918/j.issn.0367-6234.2017.03.002 |
| LIU Jiufu,SUN Yan,YU Jie,LIU Wenyuan,LIU Haiyang.Fault diagnosis of rocket engine start-up process with partially observed Petri nets[J].Journal of Harbin Institute of Technology,2017,49(3):15.DOI:10.11918/j.issn.0367-6234.2017.03.002 |
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| 摘要: |
| 针对液氧/甲烷膨胀循环发动机启动过程中存在的不可观事件和不可观运行状态，现有故障诊断方法仍存在诊断不准确的问题，提出一种基于部分可观Petri网的故障诊断方法.首先，将系统获取的观测序列分解为单位长度的基础观测序列，应用线性矩阵不等式计算与基础观测序列相符的点火序列集；然后，采用向前-向后算法拓展诊断区间、参数K限定故障诊断序列长度，通过分析点火序列集中不可观变迁是否正常点火，判定观测序列是否包含故障；最后，将部分可观Petri网故障诊断算法应用于液氧/甲烷膨胀循环发动机启动过程.结果表明：所提出的算法使计算复杂性缩小为原来的ho-1·eho-K，避免随状态空间复杂性增大而出现的状态空间爆炸问题，同时算法能进行实时跟随、在线诊断，诊断准确性可达到99.134%.
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| 关键词: 液氧/甲烷膨胀循环发动机 故障诊断 部分可观Petri网 整数线性规划 向前向后算法 |
| DOI：10.11918/j.issn.0367-6234.2017.03.002 |
| 分类号:V434;TP277 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(61473144) |
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| Fault diagnosis of rocket engine start-up process with partially observed Petri nets |
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LIU Jiufu1,SUN Yan1,YU Jie1,LIU Wenyuan1,LIU Haiyang2
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(1.College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; 2.College of Electronic Science and Engineering, Southeast University,Nanjing 210096, China)
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| Abstract: |
| For the start-up process of the LOX/CH4 expander cycle engine, containing unobserved events and unobserved states, the existing fault diagnosis methods are still not accurate enough, so we present a diagnosis method with partially observed Petri nets. Firstly, the system observation sequences are decomposed into elementary observation sequence of length 1 and linear matrix inequalities are used to compute the firing sequences consistent with each elementary observation sequence. Then, using the forward-backward algorithm extends the diagnosis range and using the parameter K limits the length of fault diagnosis sequence. Analyzing the unobserved transitions of the fire sequences, fired or not, so as to determine whether the faults are contained among the observed sequence. Finally, the LOX/CH4 expander cycle engine start-up process is diagnosed by the fault diagnosis system of partially observed Petri nets. The experimental results show that the proposed algorithm can reduce the computational complexity as the original ho-1·eho-K. It avoids the state space explosion problem because of the increasing of state space complexity. Meanwhile, it can be real-time tracking and online fault diagnosis which diagnosis accuracy can be reached 99.134%.
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| Key words: LOX/CH4 expander cycle engine fault diagnosis partially observed Petri nets integer linear programming forward-backward algorithm |
