System identification and tracking using a statistical model and a Kalman filter. (1st November 2019)

• Record Type: Journal Article
• Authors: Soal, K.; Govers, Y.; Bienert, J.; Bekker, A.
• Is Part Of: Mechanical systems and signal processing. Volume 133(2019)
• Publication Date: 2019-11-01
• Subjects: System identification -- Operational Modal Analysis -- Ship structures -- Multivariate statistics -- Kalman filter -- Automated modal parameter selection; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.05.011
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Development of a system identification and tracking method to improve the accuracy of modal estimates using a statistical model and a Kalman filter. A key objective was to make observed data maximally informative using additional system inputs such as environmental and operational parameters. The Kalman filter is used as an optimal adaptive filter to combine data driven model predictions with actual measurements. The method can also be implemented as an automatic modal parameter selection technique. Abstract: The sensitivity of system identification estimates to changing environmental and operational parameters is important for structural health monitoring and inverse force estimation. Damage to a structure can be misidentified or masked by modal shifts as a result of environmental parameters. In this paper a novel approach to reduce the uncertainties and improve the sensitivity of system identification and tracking is presented based on a data driven statistical model and a Kalman filter. A key objective is to make experimental data maximally informative by using additional system inputs. The method is first demonstrated on numerical data where it was found to improve accuracy and identify underlying trends. Investigations were then conducted on full scale data from the research vessel Polarstern. Model training led to the development of a sliding predictive model using an optimized linear regression method. The model was found to accurately re-create the training data set and was used to make predictions based on future system inputs. Since both the model prediction and the system identification estimates contain different uncertainties the Kalman filter was used to combine both estimates in an optimal way. The Kalman filter estimates were observed to produce balanced and consistent results. The Kalman estimates were also not overly or consistently biased by the SSI estimates or the model predictions.