张建军,张利,穆海芳,刘征宇,徐娟.基于改进粒子群优化BP网络的发动机故障诊断方法[J].农业机械学报,2011,42(1):198-203.
Zhang Jianjun,Zhang Li,Mu Haifang,Liu Zhengyu,Xu Juan.Improved BP-neural Network of the Particle Swarm Optimization in the Research on Engine Fault Diagnosis[J].Transactions of the Chinese Society for Agricultural Machinery,2011,42(1):198-203.
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基于改进粒子群优化BP网络的发动机故障诊断方法   [下载全文]
Improved BP-neural Network of the Particle Swarm Optimization in the Research on Engine Fault Diagnosis   [Download Pdf][in English]
  
DOI:10.3969/j.issn.1000-1298.2011.01.039
中文关键词:  发功机  粗糙集  粒子群优化  BP神经网络  故障诊断  属性约简
基金项目:国家自然科学基金资助项目(60873003);教育部博士点新教师基金资助项目(20080351025);国家电子信息产业发展基金资助项目(2010301)
作者单位
张建军 安全关键工业测控技术教育部工程研究中心 
张利 合肥工业大学 
穆海芳 合肥工业大学 
刘征宇 合肥工业大学 
徐娟 安全关键工业测控技术教育部工程研究中心 
中文摘要:使用BP神经网络进行故障诊断过程中,随着输入变量的增加会造成“维数”灾难,导致训练效率不高,而且易陷入局部极小的问题。基于粗糙集的约简是常用的降低“维数”的方法,但约简是NP问题,随着信息量增多计算量会随之剧增;本文采用基于属性重要度的启发式值约简算法进行属性约简,建立了一种模糊信息知识发现方法结合粒子群优化BP网络的故障诊断方法。通过实验表明此方法不仅能有效获取规则,降低网络的输入维数,还能有效避免陷入局部极小,从而提高故障诊断的效率。
Zhang Jianjun  Zhang Li  Mu Haifang  Liu Zhengyu  Xu Juan
Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education;Hefei University of Technology;Hefei University of Technology;Hefei University of Technology;Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education
Key Words:Engine, Rough set (RS), Particle swarm optimization (PSO), BP-neural network, Fault diagnosis, Attribute reduction
Abstract:In the process of using BP-neural network in fault diagnosis, there will be “dimension tragedy” as the input variable increases, which causes the lower training effective. Besides, traditional BP algorithm tends to fall in local optimization. The reduction based on the rough set (RS) is the conventional “reduce dimension” method, but it is NP-hard problem, whose computing will gradually augment as the information increases. Therefore, a heuristic algorithm was used for attribute reduction based on the importance of attribute value to reduce attribute, a fault diagnosis approach was formed combining the fuzzy information system knowledge method with BP-neural network of the particle swarm optimization (PSO) algorithm to diagnose the fault of engine. The experiments show that comparing with the conventional method, it can not only require fault diagnosis rule, but also reduce net input dimensions effectively, avoid falling in local optimization and increase the efficiency of fault diagnosis.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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