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针对具有动态特性的质量相关的故障检测问题,提出了一种基于自回归移动平均模型(auto-regressive moving average exogenous,ARMAX)的动态全主成分回归(dynamic total principal component regression,DT-PCR)方法.该方法基于输入的时滞值,形成增广输入矩阵;然后将形成的增广矩阵分解成质量无关和质量相关两个正交部分,并根据这两个部分对应子空间的统计量设计出一个更简单的故障诊断策略.该方法对于输出的预测精度也优于以往的方法.最后,通过一个数值例子和田纳西—伊斯曼过程将DT-PCR与全潜结构投影模型(total partial least squares,TPLS)进行对比,验证了DT-PCR的输出预测性能及与质量相关的故障检测性能.“,”On the basis of the structure of auto-regressive moving average exogenous (ARMAX),we propose a dynamic total principal component regression (DT-PCR) method for dynamic performance of quality-related fault detection.We form the input augmented matrix in the method based on the delay value of the input.The augmented matrix is divided into two orthogonal parts,namely,quality-related and quality-unrelated.We design a simple fault detection strategy based on statistics in two subspaces that correspond to the two parts.The output prediction accuracy of DT-PCR is better than that of former methods.The prediction and fault detection performance of the proposed approach are proved by a numerical example and the Tennessee Eastman process through a comparison by using total partial least squares (TPLS).