Preprocessing Data from a System (Advanced Signal Processing Toolkit or Control Design and Simulation Module)

After acquiring the data, you must preprocess the raw data samples. Preprocessing involves steps such as removing trends, filtering noise, and so on. You can use the Data Preprocessing VIs to analyze the raw data and determine whether that data accurately reflects the response of the system you want to identify. To identify a system model in the frequency domain by using time-domain data, you must preprocess the time-domain data by estimating the frequency response function (FRF). You can use the SI Estimate FRF VI to estimate the FRF from time-domain data.

You can use a number of preprocessing techniques to ensure that the incoming data samples are free from external noise, scaling problems, outliers, and other corruptions. These preprocessing techniques include the following methods:

• Visually inspecting data
• Removing offsets and trends
• Filtering and downsampling
• Data Scaling

Note  To identify a system model in the frequency domain by using time-domain data, you must preprocess the time-domain data by estimating the frequency response function (FRF).

Validating the quality of the data at each step in the preprocessing procedure is important in ensuring that you accurately identify a model in the later steps of the system identification process.