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首页> 外文会议>ASME(American Society of Mechanical Engineers) Turbo Expo vol.1; 20070514-17; Montreal(CA) >A SENSOR-FAULT-TOLERANT DIAGNOSIS TOOL BASED ON A QUADRATIC PROGRAMMING APPROACH
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A SENSOR-FAULT-TOLERANT DIAGNOSIS TOOL BASED ON A QUADRATIC PROGRAMMING APPROACH

机译：基于二次规划方法的传感器容错诊断工具

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摘要

Kalman filters are widely used in the turbine engine community for health monitoring purpose. This algorithm gives a good estimate of the engine condition provided that the discrepancies between the model prediction and the measurements are zero-mean, white random variables. However, this assumption is not verified when instrumentation (sensor) faults occur. As a result, the identified health parameters tend to diverge from their actual values which strongly deteriorates the diagnosis. The purpose of this contribution is to blend robustness against sensor faults into a tool for performance monitoring of jet engines. To this end, a robust estimation approach is considered and a Sensor Fault Detection and Isolation module is derived. It relies on a quadratic program to estimate the sensor faults and is integrated easily with the original diagnosis tool. The improvements brought by this robust estimation approach are highlighted through a series of typical test-cases that may be encountered on current turbine engines.
机译：卡尔曼过滤器广泛用于涡轮发动机社区，以进行健康监测。如果模型预测和测量值之间的差异为零均值，白色随机变量，则该算法可以很好地估计发动机状况。但是，当发生仪器（传感器）故障时，无法验证此假设。结果，所识别的健康参数趋于偏离其实际值，这严重地使诊断恶化。该贡献的目的是将针对传感器故障的鲁棒性融合到喷气发动机性能监控工具中。为此，考虑了鲁棒的估计方法，并导出了传感器故障检测和隔离模块。它依靠二次程序来估计传感器故障，并且可以轻松地与原始诊断工具集成。通过当前涡轮发动机可能会遇到的一系列典型测试案例，强调了这种可靠的估算方法带来的改进。

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