Bayesian Online Change Detection of Multimode Processes With Application to Wind Turbines

Online change detection of multimode processes is important for process monitoring and control, which aims to timely and accurately detect two types of changes: 1) mode changes and 2) parameter changes. However, the existing online methods mainly focus on one type of change and, thus, have difficulty capturing the complex change structure. Motivated by this, we propose a novel Bayesian online change detection method for multimode processes (BCD-MMP). Specifically, the complex change structure is characterized by three state variables (the latest change point (LCP), the mode of the current segment, and the indicator variable of parameter changes). When a new data point arrives, we infer the posterior distribution of the state variables. To make the inference process tractable, we develop a sampling-based inference algorithm, and a pruning strategy is also provided to improve the computation efficiency. Simulation and real case studies of wind turbine torque control process indicate that the proposed method can achieve better online change detection performance than the state-of-the-art methods.