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基于支持向量数据描述的火炮自动机故障状态监测技术研究 被引量:5

Research on Fault State Monitoring Technology of Artillery Automaton Based on Support Vector Data Description
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摘要 针对火炮自动机故障状态监测问题,提出了一种基于支持向量数据描述的故障状态监测模型。利用搭建的自动机振动测试平台获取自动机振动信号,采用变模态分解方法将振动信号分解为多个本征模态分量,计算各个分量的样本熵值作为故障特征,并以正常状态下的自动机故障特征为训练样本进行SVDD模型的训练,训练过程中根据模型特点找到合适的模型参数,完成自动机状态监测模型的构建。在自动机测试平台上设置多种预制零件故障进行模型的验证,结果表明所建立的状态监测模型对异常状态的发生有很强的敏感性,具有较高的检测准确率;同时设计了关重件模拟性能退化试验,试验结果验证了所提出的模型具有良好的早期故障检测能力,可较为准确地反映自动机故障性能退化过程,可为火炮自动机故障状态监测提供一定的借鉴和指导。 In order to detect the occurrence of fault conditions and monitor the fault status effectively on artillery automata,a support vector data description(SVDD)based automaton fault status monitoring model was proposed.The established vibration test platform to obtain theautomaton vibration signal was used,then the vibration signal was decomposed to multiple intrinsic mode components by variational mode decomposition.The sample entropy values of the components were calculated as the fault characteristic value.The automaton fault characteristics in the normal state were used as training samples to train the SVDD model.In the training process,the best model parameters were found according to the characteristics of the model to complete the construction of the automaton state monitoring model.Finally,several prefabricated failures of automaton parts were set on the test platform for model testing,the test results showed that the established condition monitoring model is very sensitive to the occurrence of abnormal conditions and has a high detection accuracy rate.At the same time,a simulated performance degradation test of key components was designed.The test results also confirmed that the proposed model has a good early fault detection ability and can accurately reflect the automaton fault performance degradation process.The research in this paper can provide reference and guidance for the fault state monitoring of artillery automata.
作者 王斐 房立清 陈敬文 WANG Fei;FANG Liqing;CHEN Jingwen(Department of Ordnance Engineering,Sergeant Academy of PAP,Hangzhou 310023,Zhejiang, China;Department of Artillery Engineering, Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, Hebei, China;The Second Military Office in Chongqing, Chongqing 400000, China)
出处 《火炮发射与控制学报》 北大核心 2022年第1期29-35,共7页 Journal of Gun Launch & Control
基金 河北省自然科学基金资助项目(E2016506003)。
关键词 火炮 自动机 故障 状态监测 支持向量数据描述 artillery automaton fault state monitoring SVDD
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