Change Point Detection of Multimode Processes Considering Both Mode Transitions and Parameter Changes

AbstractMultimode processes are common in modern industry and refer to processes that work in multiple operating modes. Motivated by the torque control process of a wind turbine, we determine that there exist two types of changes in multimode processes: (1) mode transitions and (2) parameter changes. Detecting both types of changes is an important issue in practice, but existing methods mainly consider one type of change and thus do not work well. To address this issue, we propose a novel modeling framework for the offline change point detection problem of multimode processes, which simultaneously considers mode transitions and parameter changes. We characterize each mode with a parametric cost function and formulate the problem as an optimization model. In the model, two penalty terms penalize the number of change points, and a series of constraints specify the multimode characteristics. With certain assumptions, the asymptotic property ensures the accuracy of the model solution. To solve the model, we propose an iterative algorithm and develop a multimode-pruned exact linear time (multi-PELT) method for initialization. The simulation study and the real case study demonstrate the effectiveness of our method against the state-of-the-art methods in terms of the accuracy of change point detection, mode identification, and parameter estimation.Keywords: Change point detectionconstrained optimization modelmultimode processeswind turbine torque controlDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsJun XuJun Xu is currently a Ph.D. student in Department of Industrial Engineering, Tsinghua University. He received his B.Eng. degree in Industrial Engineering from Tsinghua University in 2019. His research interests include modeling, monitoring, change detection and diagnosis of complex systems.Jie ZhouJie Zhou is a senior engineer in Goldwind Science & Technology Co.,Ltd, Beijing, China. He is focusing on wind turbine diagnosis and safety control. He is also currently working towards the D.Eng. degree in Industrial Engineering with Tsinghua University, Beijing, China. He received his B.S. and M.S. degrees in Electrical Engineering from Dalian University of Technology, Dalian, China.Xiaofang HuangXiaofang Huang is a senior engineer. She received her master's degree from Xidian University, Xi'an, China in 2006. She is currently a department lead of the R&D Center of Goldwind Science & Technology Co.,Ltd, mainly engaged in the development and localization of wind turbine main control system software, as well as the development of intelligent control and protection technology of wind turbines and wind farms.Kaibo WangKaibo Wang is a professor in Department of Industrial Engineering, Tsinghua University and the vice dean of Vanke School of Public Health, Tsinghua University. He received his B.Eng., and M.S. degrees in Mechatronics and Mechanical Engineering from Xi’an Jiaotong University, Xi’an, China, in 1999 and 2002 respectively, and Ph.D. in Industrial Engineering and Engineering Management from Hong Kong University of Science and Technology, Hong Kong, in 2006. His research focuses on statistical quality control and data-driven system modeling, monitoring, diagnosis and control, with a special emphasis on the integration of engineering knowledge and statistical theories for solving problems from the real industry.