提出了空調(diào)系統(tǒng)傳感器故障檢測、故障識(shí)別、故障重構(gòu)的主成分分析方法。主成分分析法將測量空間分為主成分子空間和殘差子空間。SPE 指數(shù)和SVI 指數(shù)分別用來檢測和識(shí)別故障。沿著故障方向,測量數(shù)據(jù)逐步逼近主成分子空間可以實(shí)現(xiàn)數(shù)據(jù)的重構(gòu)。通過對空調(diào)監(jiān)測系統(tǒng)的傳感器故障檢測與診斷結(jié)果展示出PCA 方法具有良好的故障檢測、識(shí)別和重構(gòu)能力。
關(guān)鍵詞:主成分分析法;傳感器故障;故障檢測與診斷;空調(diào)系統(tǒng)
Abstract :The principal component analysis(PCA) approach for sensor fault detection ,identification and reconstruction in HVAC system is presented. The PCA approach partitions the measurement space into principal component subspace (PCS) and residual subspace (RS) . The SPE index and the SVI index is used for fault detection and fault identification , respectively. Fault can be reconstructed by sliding the mea2 sure to principal component subspace along the direction of fault. It shows that the PCA approach has good ability of fault detection , fault identification and fault reconstruction
though employing in HVAC monitoring and controlling sys2
tem.
Key words : principal component analysis ( PCA) ; sensor fault ;fault detection and diagnosis ;HVAC sys2 tem