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1. 发明授权

US07243048B2 Fault detection system and method using multiway principal component analysis 有权
标题翻译：故障检测系统和使用多路主成分分析的方法
公开(公告)号：US07243048B2
公开(公告)日：2007-07-10
申请号：US11288818
申请日：2005-11-28
申请人： Wendy K. Foslien , Satya Varaprasad Allumallu
发明人： Wendy K. Foslien , Satya Varaprasad Allumallu
IPC分类号： G06F17/18
CPC分类号： G05B23/024
摘要： A fault detection system and method is provided that facilitates detection of faults that are manifest over a plurality of different operational phases. The fault detection system and method use multiway principal component analysis (MPCA) to detect fault from turbine engine sensor data. Specifically, the fault detection system uses a plurality of load vectors, each of the plurality of load vectors representing a principal component in the turbine engine sensor data from the multiple operational phases. The load vectors are preferably developed using sets of historical sensor data. When developed using historical data covering multiple operational phases, the load vectors can be used to detect likely faults in turbine engines. Specifically, new sensor data from the multiple operational phases is projected on to the load vectors, generating a plurality of statistical measures that can be classified to determine if a fault is manifest in the new sensor data.
摘要翻译：提供了一种故障检测系统和方法，其有助于检测在多个不同操作阶段上显现的故障。故障检测系统和方法使用多路主成分分析（MPCA）来检测涡轮发动机传感器数据的故障。具体地，故障检测系统使用多个负载向量，多个负载矢量中的每一个表示来自多个操作阶段的涡轮发动机传感器数据中的主要分量。优选利用历史传感器数据集开发负载矢量。当使用涵盖多个操作阶段的历史数据开发时，可以使用负载向量来检测涡轮发动机中的可能故障。具体地说，来自多个操作阶段的新的传感器数据被投影到负载向量上，产生多个统计测量值，这些统计测量值可被分类以确定新传感器数据中是否存在故障。

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