基于FTA与BAM神经网络融合的飞机故障诊断方法

飞机由大量彼此关联的组件组合而成,其大规模特性使得基于故障树(FTA)和基于神经网络的故障诊断方法在应用于其故障诊断时分别存在空间爆炸问题和训练样本整理困难问题.本文融合故障树和BAM神经网络,由故障树归纳出系统所有的故障模式,整理出BAM神经网络所需的具有规范性、独立性、正交性的训练样本,然后用BAM神经网络实现飞机故障的快速和准确诊断.实验评估结果表明,融合方法有良好的可扩展性,而且故障判别率提升了20%.

An Aircraft Fault Diagnosis Scheme Based on Integration of FTA With BAM Neural Networks

When the existing fault diagnosis methods are applied to an aircraft with a large number of components associated with each other, there appear space explosion problems in those diagnosis methods based on fault tree analysis (FTA), and the difficulty in sorting the training samples in methods based on neural network. This paper proposed a scheme that integrates the fault tree with BAM neural networks, in which all the failure modes of a system are summarized with the fault tree, sorting out the necessary training samples for the BAM neural networks. On this basis, fast and accurate aircraft fault diagnosis can be achieved by applying BAM neural networks. The experiment evaluations show that the proposed method has better scalability, and the average fault-judging rate is improved by 20%.