In fault diagnosis, partial least squares (PLS) is a popular data-driven method to identify abnormal key performance indicators (KPI). However, there are two problems in fault diagnosis when using PLS, including inaccurate fault subspace extraction and unidentified false alarms. In the first problem, the improved PLS (IPLS) model is adopted to obtain a precise subspace through orthogonal decomposition. In addition, to eliminate the normal value in fault data, a quality-related fault subspace (QRFS) extraction method is proposed, which can extract a purer quality-related fault subspace. In the second problem, to provide feedback for false alarms, a modified IPLS (M-IPLS) model is proposed to extract the quality-unrelated fault subspace. Based on the proposed fault subspace extraction methods, the fault can be reconstructed by a lower dimensional fault subspace and false alarms with feedback can improve the efficiency of diagnosis. Finally, two examples, including a numerical simulation and the Tennessee Eastman process (TEP), are used to show the effectiveness of the proposed method.

在故障诊断中，偏最小二乘法 (PLS) 是一种流行的数据驱动方法，用于识别异常关键性能指标 (KPI)。但是，使用PLS进行故障诊断存在两个问题，即故障子空间提取不准确和误报不明。第一个问题采用改进的PLS（IPLS）模型，通过正交分解得到精确的子空间。此外，为了消除故障数据中的正态值，提出了一种质量相关故障子空间(QRFS)提取方法，可以提取出更纯的质量相关故障子空间。在第二个问题中，为了对误报提供反馈，提出了一种改进的IPLS（M-IPLS）模型来提取与质量无关的故障子空间。基于提出的故障子空间提取方法，故障可以通过较低维的故障子空间进行重构，带有反馈的虚警可以提高诊断效率。最后，两个例子，包括数值模拟和田纳西伊士曼过程（TEP），被用来证明所提出方法的有效性。