摘要
传感器状态的好坏很大程度上影响暖通空调(HVAC)系统的运行,对其展开故障诊断十分必要。核主成分分析(KPCA)方法通过集成算子与非线性核函数计算高维特性空间的主元成分,有效捕捉过程变量中的非线性关系,将其用于传感器常见4种故障的诊断,先用Q统计量进行故障监测,再用T2贡献量百分比变化来识别故障。实验结果表明:KPCA方法具有很好的故障监测与诊断能力。
Fault detection and diagnosis for sensor is necessary, which affects the performance of the HVAC system seriously. The kernel principal component analysis （KPCA） effectively captures the nonlinear relationship of the process variables, which computes principal-component in high-dimensional feature space by means of integral operators and nonlinear kernel functions. The KPCA method is used in diagnosing for four familiar sensor faults. At first its fault is detected by Q statistic, at second its fault is identified by T^2 contribution percent change. The experiment result shows the KPCA method has good performance in fault detection and diagnosis.
出处
《传感器与微系统》
CSCD
北大核心
2008年第5期37-39,42,共4页
Transducer and Microsystem Technologies
关键词
核主成分分析
暖通空调
传感器
故障监测诊断
kernel principal component analysis（KPCA）
HVAC
sensor
fault detection and diagnosis
