中国空间科学技术

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基于神经网络模型的学习诊断

纪常伟,荣吉利,黄文虎   

  1. 北京工业大学,哈尔滨工业大学
  • 发布日期:1997-10-25

LEARNING DIAGNOSIS METHOD BASED ON NEURAL NETWORK MODEL

Ji Changwei (Beijing Polytechnic University,Beijing 100022) Rong Jili Huang Wenhu (Harbin Institute of Technology,Harbin 150001)   

  • Published:1997-10-25

摘要： 基于故障树模型的诊断把故障树的底事件分成三部分：必然的故障源集（ＣＦＳ）、正常底事件集（ＮＥＳ）和可能的故障源集（ＰＦＳ），及如何进一步确定ＰＦＳ中各元素的状态（正常或异常）。在存在大量训练样本的情况下，可采用基于神经网络模型的学习诊断方法来确定ＰＦＳ中各元素的状态，并通过对某卫星能源系统故障模拟原理性试验台的故障诊断验证了该方法的有效性。

关键词: 卫星, 故障诊断, 人工神经元网络, 失效模式, 算法

Abstract: Fault tree model based diagnosis divides the bottom events of the fault tree into three parts: certain fault sources(CFS)、normal event sources(NES) and possible fault sources(PFS), but it is not clear how to determine the states of the elements in PFS (normal or abnormal). With lots of training examples, learning diagnosis method based on neural network is applied to determine the states of the elements in PFS, and its effectiveness is demonstrated by diagnosing a principle fault simulation testbed of any satellite power system.