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1、1.Fault diagnosis for civil aviation aircraft based on rough-neural network 基于粗神经网络的民用航空故障诊断基于粗神经网络的民用航空故障诊断 Liu, Yongjian1; Zhu, Jianying1; Xia, Hongshan1 Source: Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics, v 35, n 8, p 1005-1008, August 200
2、9 Language: Chinese ISSN: 10015965 CODEN: BHHDE8 Publisher: Beijing University of Aeronautics and Astronautics (BUAA)Author affiliation: 1 College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China Abstract: To solve the defects of traditional fault diagnosi
3、s neural network, such as long training time, complex structure and single-valued input, a fault diagnosis system for civil aircraft based on rough-neural network was proposed. Rough set theory was applied to the front-end neural network to reduce the data of civil aircraft fault sample so as to rem
4、ove the disturbance of redundant attributes, and overcome the impaction of unrelated data that imposed on the performance of network learning, simplify network structure. Secondly, by using the rough neurons instead of the traditional neurons, the performance of network was improved, and the scope o
5、f the application of network was expanded. The effectiveness of this method was verified by Airbus A320 aircraft fault diagnosis test. (7 refs.)摘要：本文主要为解决诸如结构复杂、单值输入等传统故障诊断神经网络的缺陷这一类的问题，而采用一个基于粗神经网络的民航飞机故障诊断系统的方法。粗糙集这一理论应用到前端神经网络，以减少民用航空器故障样本的数据，以消除多余属性的干扰，克服网络学习性能的无关数据影响，简化网络结构。其次，通过使用粗神经而不是传统的神经元，
6、来提高网络性能，扩大网络的应用范围。该方法的有效性，得到了空中客车 A320 型飞机故障诊断测试的验证。 Controlled terms: Aircraft - Auxiliary power systems - Civil aviation - Electric power transmission networks - Failure analysis - Fuzzy sets - Neural networks - Neurons - Power transmissionUncontrolled terms: Airbus A320 - Civil aircrafts - Comple
7、x structure - Fault diagnosis - Fault diagnosis systems - Fault sample - Network learning - Network structures - Rough neuron - Rough-neural network - Training time2.Civil aviation aircraft fault diagnosis research based rough sets and BP net 基于粗糙集和基于粗糙集和 BP 网络的民用飞机故障诊断研究网络的民用飞机故障诊断研究 Zhang, Peng; C
8、ui, Wenli Source: Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, v 28, n SUPPL. 5, p 101-104, August 2007 Language: Chinese ISSN: 02543087 CODEN: YYXUDY Publisher: Science PressAbstract: Because of the highly requirement on safety and reliability of civil aviation, fault diagnosis o
9、f civil aviation aircraft is very important. At present most of the fault diagnosis depends on experiences. It often fails in intractable trouble. Now artificial intelligence is applied, but it still cant meet all the needs of formidably complex fault. The combination of different methods achieves i
10、mprovement and becomes a tendency of research. This paper presents a method of combining rough sets and BP net based on each characters. It shows practicability and efficiency through example in civil aviation aircraft fault diagnosis. (5 refs.)摘要：由于对安全和民用航空的可靠性要求高，民用飞机故障诊断显得非常重要。目前，上述故障诊断最依赖于经验。它往往
11、由于一些棘手的问题而失败。现在人工智能的应用仍不能满足所有的艰巨复杂故障的需要。不同方式的组合实现了改进，并成为研究的趋势。本文提出了一种结合粗糙集和 BP 网的每个字符的方法。它表明通过例如，在民用飞机故障诊断中的实用性和效率。Database: Compendex 3.The research of aircraft fault diagnosis based on adaptive FPN 基于基于自适应自适应 Petri 网的飞机故障诊断研究网的飞机故障诊断研究 Peng, Zhang1; Shiwei, Zhao1; Yake, Wang1 Source: 2009 Chinese
12、Control and Decision Conference, CCDC 2009, p 5252-5255, 2009, 2009 Chinese Control and Decision Conference, CCDC 2009 Language: English Conference: 2009 Chinese Control and Decision Conference, CCDC 2009, June 17, 2009 - June 19, 2009 Publisher: IEEE Computer SocietyAuthor affiliation: 1 Engineerin
13、g Techniques Training Center, Civil Aviation University of China, Tianjin 300300, China Abstract: For aircraft equipment being more and more complex, it makes fault diagnosis more and more difficult. In this paper a measure based on adaptive fuzzy Petri nets (FPN) for fault diagnosis is proposed. Fi
14、rst, deal with repair factorys statistical data and expert experience data; second, establish a fault diagnosis model through fuzzy production rules, models output is the location of fault cause, which provides the suggestion for maintenance decision-making; and finally amend model parameters accord
15、ing to actual maintenance result. Through the simulation on maintenance data, it is shown that the model can be quickly and efficiently identify the Causes for failure to improve the efficiency of maintenance. The method is clear and intuitive, easy-to-achieve on the computer for the establishment o
16、f a maintenance decision support system provides a new way. 2009 IEEE. (5 refs.) 摘要：由于飞机使用的设备变得越来越复杂，它使故障诊断变得越来越困难。在此提出了 一个对自适应模糊 Petri 网的故障诊断（模糊 Petri 网）的措施。首先，修理厂的统计数据 和专家经验，数据处理;第二，通过建立模糊产生式规则的故障诊断模型，模型的输出是故 障原因，提供了维修地点的建议作出决定的决策，最后，根据模型参数修正实际维修的结果。通过对维修数值模拟，结果表明，该模型可以快速，有效地找出故障，提高维修效率 的原因。该方法清晰直观，易于实现的用于维修决策支持系统建立的计算机提供了一种新 的途径。 Controlled terms: Aircraft parts and equipment - Artificial intelligence
