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# Time Frequency Analysis Application Areas (Advanced Signal Processing Toolkit)

LabVIEW 2013 Advanced Signal Processing Toolkit Help

Edition Date: June 2013

Part Number: 372656B-01

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In general, you can categorize time-frequency analysis methods into two classes: linear methods and quadratic methods. You usually use quadratic methods to analyze, classify, and detect latent features in a signal, and you usually use linear methods to reduce noise and extract signal components.

One major benefit of applying a time-frequency transform to a signal is discovering the pattern of frequency changes, which often clarifies the nature of the signal. Once you identify a pattern, you can analyze and classify the pattern. For example, a pattern of harmonic drift associated with rotating machinery can indicate the working condition of a system, and a pattern of frequency changes in medical signals can indicate a patient's health condition.

Another important use of time-frequency analysis is to reduce random noise in noise-corrupted signals. For example, random noise might spread evenly across the entire time-frequency domain. The useful information, however, usually is concentrated in a relatively small region in the time-frequency domain. If you convert such a noise-corrupted signal to the time-frequency domain using a linear time-frequency transform, you might be able to extract those components in the time-frequency domain and then reconstruct the time-domain signal, which has a higher signal-to-noise ratio.

You also can use time-frequency analysis to determine if a signal has distinct time-frequency components and isolate those components for further analysis. In the time domain, you can separate the components of signals that do not overlap, such as musical notes. You cannot use the Fourier transform to separate signal components that overlap in the time domain. In the frequency domain, you can use the fast Fourier transform (FFT) to separate signals, such as vibration harmonics caused by a steady-state shaft imbalance. However, the different components can overlap in the frequency domain if the spectral content varies over time. With such overlapping signal components, you cannot distinguish the components in either the time domain or in the frequency domain alone. In this situation, you usually can use a linear time-frequency method to distinguish the overlapping signal components.

Many real-world signals contain time-varying spectra, which means that the potential application areas of time-frequency analysis are numerous. The following list highlights a few successful application areas of time-frequency analysis.

• Order analysis, such as for rotating machinery analysis
 Note  You can use the NI Sound and Vibration Measurement Suite to examine dynamic signals mechanical systems generate, including rotating or reciprocating components, and to create order analysis applications for order tracking, order extraction, and tachometer signal processing.
• Machine condition monitoring for systems associated with rotational machinery, such as power-generator systems
• Audio equipment testing and characterization, such as for speakers
• Speech processing, such as speech enhancement and speech recognition
• Radar and sonar image enhancement
• Biomedical signal processing, such as signal feature extraction
• Seismological signal processing, such as detection of soil liquefaction