基于综合模糊聚类算法的液体火箭发动机故障诊断

董周杰; 郭迎清

doi:10.13224/j.cnki.jasp.2020.06.023

基于综合模糊聚类算法的液体火箭发动机故障诊断

doi: 10.13224/j.cnki.jasp.2020.06.023

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出版历程
- 收稿日期: 2019-11-09
- 刊出日期: 2020-06-28

Fault diagnosis of liquid rocket engine based on comprehensive fuzzy clustering algorithm

School of Power and Energy,Northwestern Polytechnical University,Xi’an 710072,China

摘要

摘要: 基于液体火箭发动机正常及故障状况数据的完备程度和数据质量的不断提高,提出一种基于数据驱动的综合模糊聚类算法用于故障诊断。采用模糊c均值(FCM)算法对已知正常样本数据进行聚类得到最优的聚类中心,将所得到的聚类中心作为先验样本数据用于传递闭包法最优分类结果的选择从而得到故障检测结果,该算法只需要少量的正常先验样本数据就能快速、准确的检测出故障;随后采用FCM算法进行故障分类,可以根据现有的故障数据库进行聚类得到对应的故障类型,并且可以给出故障幅值范围。模型仿真结果表明:该算法对故障的检测率可达968%,故障隔离率达到94%。某型液体火箭发动机实际试车数据结果表明:该故障诊断算法能够准确及时的检测并隔离出故障。
- 液体火箭发动机 /
- 故障诊断 /
- 模糊聚类 /
- 模糊c均值(FCM) /
- 模糊传递闭包法
Abstract: Based on the completeness of the normal and fault condition data of liquid rocket engine and the improvement of data quality, a data-driven comprehensive fuzzy clustering algorithm was proposed for fault diagnosis. The fuzzy c-means (FCM) algorithm was used to cluster the known normal sample data to obtain the optimal clustering center, and the obtained cluster center was used as a-priori sample data to select the optimal classification result of the closure method to obtain the fault detection result. Only a small amount of normal prior sample data were required to quickly and accurately detect the fault; then the FCM algorithm was used to classify the fault, and the corresponding fault type can be clustered according to the existing fault database, and the range of fault amplitudes can be given. The simulation results showed that the detection rate of the algorithm was up to 968% and the fault isolation rate was 94%. The actual test data of a liquid rocket engine show that the fault diagnosis algorithm can detect and isolate faults accurately and timely.
- liquid rocket engine /
- fault diagnosis /
- fuzzy clustering /
- ,fuzzy c-means(FCM) /
- fuzzy transfer closure

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