Visually inspecting the data is the best way to detect disturbances such as an abnormal pulse, a temporary sensor failure, or a transmitter failure.

Unexpected events like these can corrupt the raw data samples. These disturbances can result in outliers, clipped saturation, and/or quantization effects that severely distort the resulting model estimation.

In the following figure, you can recognize outliers by visually inspecting the data:

Figure 175. Identifying Outliers in Visual Data
In the previous figure, notice that the data acquired between 85–100 seconds is abnormal. When preprocessing data, you want to remove all outliers in the data set. You must remove the outliers manually.
Note You also can plot the data waveform and the spectral density function of the data to discover periodic disturbances.

Traditionally, you examine data samples either in the time domain or the frequency domain. An effective approach is to display the data in the joint time-frequency domain, which provides a better understanding about the measured signals. You must have the LabVIEW Advanced Signal Processing Toolkit installed to use the joint time-frequency domain.