活性污泥法是我国最常用的污水处理工艺，然而，污泥膨胀的发生是活性污泥工艺稳定可靠运行难以回避和亟待解决的问题。为此，文中提出了一种新的全生命周期故障诊断方法，用于监测污泥膨胀，并在准确地预警故障后提供合理的决策支持。为了充分挖掘污膨胀数据的隐含信息，文中利用典型相关分析 (CCA) 和绝对平均振幅值 (AMAV) 提取相关特征并用于故障检测，通过重排历史观测样本的方法改进贡献图并用于故障分离; 根据故障预警结果，提出了基于 AMAV 的特征提取和多元格兰杰因果 (MVGC) 分析的故障传播定位方法。使用在污水厂采集的现场数据进行实验，结果表明，所提方法能及时有效地检测、分离和分析污泥膨胀的发生。

As a typical multivariate statistical analysis method，the partial least squares (PLS) has been widelyapplied in the anomaly monitoring of key performance indicators． However，the data in complex industrial processes may show dynamic or nonlinear characteristics，or both，so the linear model based on PLS is not suitable for thisprocess and may increase the false alarm rate． Therefore，a multi-feature extraction algorithm based on PLS was proposed． Firstly，the dynamic features were extracted based on the dynamic internal model of PLS to obtain thequality-related dynamic subspace and dynamic residual subspace． The PLS regression modeling was carried out forthe dynamic residual subspace to further separate the quality related features of static information． Then，the residualsubspace was projected to a high dimension to construct a nonlinear PLS model to extract the nonlinear characteristics of variables． Finally，the statistics was constructed in each feature space，and a complete multi-feature hybrisystem process monitoring strategy was designed． The example analysis results of Tennessee-Eastman process show
that the proposed method has faster fault detection speed and can achieve better fault detection effect．