Data-manifold-based monitoring and anomaly diagnosis for manufacturing process

Aiming to solve the problems of the inaccurate dimension reduction of high-dimensional data and insufficient information utilization in traditional manufacturing process monitoring methods—in which mostly only the distance information of pairwise points is used as the similarity index for the data dimension reduction—this paper proposes a data-manifold-based monitoring method that combines the distance information and angle information of pairwise points. First, the intrinsic dimension of manufacturing process data is estimated by combining multiple geometric features on the data manifold. Second, considering the angle information and distance information in the neighborhood of process data, the method to construct local features of the data manifold is given; further, a fusion method of local features and global–local features of manifold data based on the eigendimension is proposed, which constructs the data dimension-reduction mapping matrix to improve the integrity of data information extraction. Then, the data index of process monitoring is given and employed to monitor the manufacturing process. Finally, Tennessee Eastman (TE) process simulation data were used to verify the effectiveness of the proposed method. The results show that compared with other methods, the anomaly detection rate of the proposed method was increased by more than 50%, while the false alarm rate was decreased by 21.4%, which proves that the method can significantly improve the efficiency of manufacturing process anomaly monitoring.