该研究关注未知模式变化和退化行为下混合系统的早期故障预测。利用增强全局分析冗余关系(AGARRs)来处理参数性和非参数性的早期故障。在故障被检测到之前，模式信息由基于模式变化签名矩阵(MCSM)的模式跟踪器提供。一旦检测到故障，基于模型的模式跟踪器就不再适用。提出了一种参数化模式变化的技术，并讨论了未知退化行为的故障模型选择机制。在故障隔离后建立故障假设集，采用混合差分进化算法进行故障预测，能够同时处理实数和二进制未知变量。模拟结果验证了所提方法的有效性。
摘要：
This work concerns incipient fault prognosis for hybrid systems with unknown mode change and degradation behavior.This method utilizes the newly developed concept of Augmented Global Analytical Redundancy Relations(AGARRs) to consider incipient fault prognosis of parametric and nonparametric nature.The incipient fault could develop at all modes(detectable mode/non-detectable mode).Before a fault is detected,the mode information is provided by a mode tracker,which is based on Mode-change Signature Matrix (MCSM) and is only activated when an inconsistency between monitored system and its nominal model is detected.Once a fault is detected,model based mode tracker is not useful anymore.A technique to parameterize the mode change is utilized.The degradation behavior of incipient fault is unknown in advanced,and a degradation model selection mechanism is discussed.The fault hypothesis set, including suspected faults and suspected mode change,is established after fault isolation.A mixed differential evolution(MDE) algorithm is proposed for fault prognosis,which is able to simultaneously handle real and binary unknown variables.Simulation results of different fault scenarios show the effectiveness of the proposed method.
展开
扫一扫
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
