基于TSMD-SVM-HMM的机内测试智能降虚警方法

机内测试虚警问题是影响系统完好性和使用保障费用的重要因素。针对环境因素导致的虚警问题,设计了时间环境应力测量装置,应用支持向量机的小样本学习优点,建立虚警与环境因素的关联关系,应用隐马尔可夫模型的连续动态信号处理能力,描述系统长期工作历程中虚警的发生规律,提出了基于时间环境应力测量装置-支持向量机-隐马尔可夫模型的机内测试智能降虚警方法。最后,在某型直升机航向姿态系统上进行了应用与验证,试验结果表明:该方法有效识别出了机内测试的虚警。

Intelligent Method of Reducing False Alarm in Built-in Test Based on Time Stress Measurement Device-support Vector Machine-hidden Markov Model

False alarm of Built-in Test(BIT) has important influence on system readiness and usage cost.Aimed at solving the problem of false alarm caused by environmental factors,a Time Stress Measurement Device(TSMD) was designed to sample the temperature and vibration of the system.The relationship between false alarm and environmental factors was built based on Support Vector Machine(SVM) which has the advantage of small sample learning.The occurring rule of false alarm was described by using Hidden Markov Model(HMM) which can process dynamic sequential signal well.Then,an intelligent method of reducing false alarm was proposed in terms of TSMD-SVM-HMM.Finally,the method was applied and validated on a helicopter heading attitude system.The experimental results show that the method can effectively recognize and reduce false alarms caused by environmental factors.