基于故障树和神经网络模型的航天器智能诊断研究

提出基于故障树和神经网络模型的诊断方法，提出面向故障树的基于框架和广义规则的知识表示方法及相应的确定性和可能性推理策略，对于可能性推理的结果，通过基于神经网络模型的学习诊断来进一步确定其状态。在Ｗｉｎｄｏｗｓ环境下，用Ｂｏｒｌａｎｄ Ｃ＋＋实现了一个原型系统。通过对“实践４号”卫星能源系统故障模拟实验台的诊断验证了系统的有效性。

RESEARCH ON THE INTELLIGENT DIAGNOSISFOR SPACECRAFT BASED ON FAULT TREE ANDNEURAL NETWORK

Fault diagnostic system is of great importance in monitoring and controllingspacecraft in the ground control center. The bottleneck problem of knowledge acquisition for spacecraft fault diagnosis is solved by using fault tree knowledge. Thepaper presents a fault diagnostic method based on fault tree and neural networkmodel. Based on the hierarchical model of fault tree, knowledge representationmethod based on frame and generalized rule is presented, and the relevant certainand possible reasoning strategies are described. Learning diagnosis based on neuralnetwork model is used to confirm and verify the results from the possible reasoning. By using Borland C++ under Windows, a fault diagnostic prototype systemis developed, and the validity is also demonstrated by diagnosing a satellite powersystem fault imitation bench.