Identification of LTI models from concatenated data sets

Research output: Unpublished contribution to conferenceUnpublished abstract


For some industrial applications, experimental data is available in the form of several data sets corresponding to the operation of the plant under the same conditions. An example of such an application is the condition monitoring of a wind turbine based on SCADA data. Here, one is interested in the identification of a turbine’ subsystem for a specific wind condition. However, long records of a given operating condition might be difficult to obtain. Hence, one needs to
select multiple short data-records from the operational data to identify the system. In this case, identification approaches where missing data are treated as unknown parameters [1, 2] are not feasible due to the large amount of lost data. Then, the best option is to concatenate the data sets, and introduce additional parameters to handle the transient effects [3]. Our aim is to verify the consistency of the estimates when considering this last approach. To this end, we performed a
Montecarlo simulation to prove consistency when dealing with AR and ARX model structures.
Original languageEnglish
Number of pages1
Publication statusPublished - 31 Mar 2017
Event37th Benelux meeting on systems and control - Soesterberg, The Netherlands, Soesterberg, Netherlands
Duration: 27 Mar 201829 Mar 2018


Workshop37th Benelux meeting on systems and control
Internet address


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