Efficient online cross-covariance monitoring with incremental SVD: An approach for the detection of emerging dependency patterns in IoT systems

Abstract: The development of the manufacturing systems has made it increasingly necessary to monitor the data generated by multiple interconnected subsystems with rapid incoming of samples. Based on incremental Singular Value Decomposition (ISVD), we develop a general online monitoring approach for the relationship of data generated from two interconnected subsystems, where each subsystem produces big data streams with several variation patterns in normal working condition. When special situation happens and new associations occur, a very small amount of computation is sufficient to update the system status and compute the control statistics by using this approach. The proposed method reduces computational overhead and retains only a small number of pairs of possible dependent patterns at each step. The validation of the method through simulation studies and a case study on semiconductor manufacturing processes further supports its effectiveness.