Continuous Monitoring of Industrial Processes through Cross-Correlation Techniques

Abstract

AbstractThe systems in process industries are typically very large and complex. There are hundreds or thousands of I/O variables and the subprocesses are typically linked with strong interactions. Therefore, monitoring such processes, and ensuring their desired operation may become difficult and challenging. Even more challenging is continuous monitoring where a system is kept normally operating while measuring and evaluating its dynamics. This paper proposes techniques for continuous monitoring of industrial processes using non parametric identification methods. Maximum-length-based binary sequences are applied as excitation signals, and the system-characterizing models are estimated through cross-correlation technique. The proposed methods are verified by experimental data from a physical process emulating the traditional headbox of a paper machine.