The following Matlab project contains the source code and Matlab examples used for power spectral density integration. This program calcultes the overall GRMS for a PSD function.

(Acquired Nov 04, 1995) Accession Number: 67B10160 Subject Category: ELECTRONIC COMPONENTS AND CIRCUITS Report/Patent Number: NPO-10126 Document Type: NASA Tech Brief Publisher Information: United States Financial Sponsor: NASA; United States Description: In English Distribution Limits: Unclassified; Publicly available; Unlimited Rights: No Copyright NASA Terms: ACCELERATION (PHYSICS); COMPUTER PROGRAMS; DATA PROCESSING; FLUX DENSITY; FORTRAN; IBM 7090 COMPUTER; IBM 7094 COMPUTER; NUMERICAL ANALYSIS; POWER SPECTRA; RANDOM NOISE; RANDOM VIBRATION; SUBROUTINES; VIBRATION TESTS.

Power spectral density function This page explains what the power spectral density function is and how the customer can use it. This page describs a part of the data analysis services we offer at CRI. Please click 'Data Analysis' button above to see other types of data analysis we offer.

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For more information please click. What is power spectral density function? Power spectral density function (PSD) shows the strength of the variations(energy) as a function of frequency. In other words, it shows at which frequencies variations are strong and at which frequencies variations are weak. The unit of PSD is energy () per frequency(width) and you can obtain energy within a specific frequency range by integrating PSD within that frequency range.

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What can you do with power spectral density function? PSD is a very useful tool if you want to know frequencies and amplitudes of oscillatory signals in your time series data. For example, let assume you are operating a factory with many machines and some of them have motors inside. You detect unwanted vibrations from somewhere. You might be able to get a clue to locate offending machines by looking at PSD which would give you frequencies of vibrations.

PSD is still useful even if data do not contain any purely oscillatory signals. For example, if you have sales data from an ice-cream parlor, you can get rough estimate of summer sales peak by looking at PSD of your data. We quite often compute and plot PSD to get a 'feel' of data at an early stage of time series analysis. Looking at PSD is like looking at simple time series plot except that we look at time series as a function of frequency instead of a function of time.

Frequency is a transformation of time and looking at variations in frequency domain is just another way to look at variations of time series data. PSD tells us at which frequency ranges variations are strong and that might be quite useful for further analysis. What does power spectral density function of actual data look like? We picked up oceanographic data as an example. Figure 1a shows power spectral density function of east-west(zonal) current on the equator. Data were obtained from the web site of National Oceanic and Atmospheric Administration, U.S.A.