In order to recognize the coupling faults of complex industrial processes effectively, this paper proposed an AdaBoost ensemble (AdBE) model based on the reconstruction of local tangent space alignment (RLTSA). First, to obtain the low-dimensional manifold structure embedded in original data space, RLTSA algorithm is designed by constructing the tangent space in the neighborhood of each data point to represent the local geometry and then aligning them to obtain the embedding coordinates. Secondly, to solve the loss of global feature information, an affine matrix is used to inversely map the low-dimensional coordinates to restore the global structure information. Thirdly, based on the above reconstruction of local tangent space alignment, an AdaBoost ensemble (AdBE) classifier is constructed for multiple faults recognition in which the AdaBoost algorithm is used to improve the performance of Decision Tree (DT), and One vs. Rest (OvR) ensemble strategy is introduced to establish the RLTSA-AdBE model. Case studies are conducted using a three-dimensional S_curve data set and the Tennessee Eastman process (TEP) to respectively verify the performance of the RLTSA algorithm and the proposed RLTSA-AdBE model. The simulation results indicate that the proposed method guarantees high diagnosis accuracy and macro_F1 Score of coupling faults recognition.

为了有效识别复杂工业过程的耦合故障，本文提出了一种基于局部切线空间对齐（RLTSA）重构的AdaBoost集成（AdBE）模型。首先，为了获得嵌入原始数据空间的低维流形结构，设计RLTSA算法，通过在每个数据点的邻域构造切线空间来表示局部几何，然后将它们对齐以获得嵌入坐标。其次，为了解决全局特征信息的丢失问题，采用仿射矩阵对低维坐标进行逆映射，还原全局结构信息。第三，基于以上局部切线空间对齐的重构，构建了AdaBoost集成(AdBE)分类器进行多故障识别，其中AdaBoost算法用于提高决策树(DT)的性能，并引入One vs. Rest (OvR)集成策略建立RLTSA-AdBE模型. 使用三维 S_curve 数据集和田纳西伊士曼过程 (TEP) 进行案例研究，分别验证了 RLTSA 算法和提出的 RLTSA-AdBE 模型的性能。仿真结果表明，该方法保证了较高的诊断精度和耦合故障识别的macro_F1 Score。使用三维 S_curve 数据集和田纳西伊士曼过程 (TEP) 进行案例研究，分别验证了 RLTSA 算法和提出的 RLTSA-AdBE 模型的性能。仿真结果表明，该方法保证了较高的诊断精度和耦合故障识别的macro_F1 Score。使用三维 S_curve 数据集和田纳西伊士曼过程 (TEP) 进行案例研究，分别验证了 RLTSA 算法和提出的 RLTSA-AdBE 模型的性能。仿真结果表明，该方法保证了较高的诊断精度和耦合故障识别的macro_F1 Score。