基于多项式混沌理论的多/全电飞机传感器故障诊断方法研究

The More Electric Aircraft (MEA, or All Electric Aircraft, AEA) concept is a major trend in aircraft electrical power system (EPS) engineering due to recent advances in solid-state power electronics and the advanced controls for high speed electrical machines. The MEA may dramatically reduce or eliminate the need for centralized aircraft hydraulic power systems and replace them with an electrically-based power system with greatly improved maintainability, and supportability as well as the potential for significant performance improvements in terms of weight, volume, and system complexity. However, it also proposes higher requirements of the system reliability and safety, as well as the self-healing capability under damage. ..Sensors are widely used in many applications onboard of MEA since they provide measurements required to implement control, supervision, coordination and management, etc. The reliability of the sensors is considered fundamental to improve the reliability and safety of the whole system. The failure of sensors may lead to a waste of the effort in design, or even damage the whole system...This proposal proposes a new analytical approach for sensor failure diagnosis under uncertainty, based on the mathematics of Polynomial Chaos Theory (PCT). The objective is to improve the self-healing capability of MEA under damage, to achieve a higher level system reliability and safety. The sensor failure diagnosis solutions are developed using polynomial chaos mathematical procedures to create a general technique for bounding the dynamic behavior of sensors. Bad data are handled with the resulting algorithms to reject them from control unit. The developed algorithms will be validated through co-simulations,and finally realized with DSP. This makes a migration in the field of sensor failure diagnosis towards on-line operation by countering for parameter uncertainties. Furthermore, the proposed method can not only be used for the purpose of sensor failure diagnosis, but also for system monitoring.

多/全电飞机是未来飞机发展的趋势，它对系统的可靠性和安全性提出了更高的要求。而传感器作为测量控制系统中诸参量的关键部件，比系统中的其他部分更容易发生故障。因此，开展对多/全电飞机传感器故障诊断的研究具有重要的实际意义。而传统的传感器故障诊断算法未考虑系统不确定性因素的影响。因此，本研究课题基于多项式混沌理论(PCT)，提出了一种考虑多/全电飞机系统不确定性因素的传感器故障诊断新方法，该方法支持实时在线运行。具体的研究内容包括：建立多/全电飞机各关键部分的数学模型，确定系统的不确定性来源及其概率分布密度函数；建立基于PCT的故障检测与诊断算法；搭建基于联合仿真的软件测试平台，对提出的故障诊断算法进行验证，并进行硬件实现；最后探讨该算法对提高多/全电飞机系统控制与保护性能的影响。本项目的成功实施，将提高多/全电飞机的系统自愈能力，提高系统的运行可靠性及安全性。

多/全电飞机是未来飞机发展的趋势，它对系统的可靠性和安全性提出了更高的要求。而传感器作为测量控制系统中诸参量的关键部件，比系统中的其他部分更容易发生故障。因此，开展对多/全电飞机传感器故障诊断的研究具有重要的实际意义。而传统的传感器故障诊断算法未考虑系统不确定性因素的影响。因此，本研究课题基于多项式混沌理论(PCT)，提出了一种考虑多/全电飞机系统不确定性因素的传感器故障诊断新方法，该方法支持实时在线运行。..项目研究了多项式混沌理论以及其在不确定性分析中的应用，理清了基于PCT的模型扩展方法。在得到的系统PCT模型的基础上，建立了被观测系统的状态观测器，得到在考虑系统不确定性因素的情况下，传感器的输出阈值；最后，基于得到的阈值，建立起传感器故障诊断（包括故障检测，及故障信号的重建）的新方案。..项目建立了多/全电飞机主要组成部分的数学模型，包括三级式发电机、变压整流器、静止变流器、及感应电机类负载。并在Dymola软件及Modelica语言环境中，进行了建模仿真，对所提出的传感器故障诊断算法进行验证。..通过MATLAB的代码生成功能，产生能在DSP微处理器运行的代码，实现了硬件在线运行。结合飞机三级式发电机调压器，探讨了所提出的传感器故障诊断算法对多/全电飞机系统控制与保护性能的影响，并进行了试验验证。..本项目的成功实施，将提高多/全电飞机的系统自愈能力，提高系统的运行可靠性及安全性。