论文标题
基于递归Dempster-Shafer组合规则的强大数据驱动的故障诊断计划 *
A Robust Data-Driven Fault Diagnosis scheme based on Recursive Dempster-Shafer Combination Rule *
论文作者
论文摘要
在本研究中,已经考虑了飞机内传感器故障诊断和通过Dempster-Shafer(DS)理论的残留信号的递归组合。特别是,出于在线强大的传感器诊断目的,提出了一个新型的基于证据的残余误差与可靠性措施的函数的函数。提出的信息融合机制分为三个步骤。第一步,应用了经典的DS概率质量组合规则。然后,计算了先前的后质量与当前与故障事件相关的先验质量之间的差异。最后,故障事件的后质量的增加是根据可靠性系数的函数加权的,该可靠性系数取决于控制活动规范。已经制定了基于提议的组合规则的传感器故障隔离方案,并将其与众所周知的最先进的递归组合规则进行了比较。已经进行了定量分析,以利用p92 tecnam飞机的多飞行数据。拟议的方法显示有效,特别是在降低错误警报率时。
In-flight sensor fault diagnosis and recursive combination of residual signals via the Dempster-Shafer (DS) theory have been considered in this study. In particular, a novel evidence-based combination rule of residual errors as a function of a reliability measure derived from streaming data is proposed for the purpose of online robust sensors fault diagnosis. The proposed information fusion mechanism is divided into three steps. In the first step, the classic DS probability mass combination rule is applied; then, the difference between the previous posterior mass and the current prior mass associated with fault events is computed. Finally, the increment of the posterior mass of a fault event is weighted as a function of a reliability coefficient that depends on the norm of control activity. A Sensor Fault Isolation scheme based on the proposed combination rule has been worked out and compared with well-known state-of-the-art recursive combination rules. A quantitative analysis has been performed exploiting multi-flight data of a P92 Tecnam aircraft. The proposed approach showed to be effective, particularly in reducing the false alarms rate.