Understanding Dynamic Signal Analyzers

Dynamic signal analyzers (DSAs) have been a mainstay of test and measurement for almost 20 years. These versatile instruments, which analyze sub-100-kHz signals in both the time and frequency domain, are suitable for a variety of applications including noise and vibration analysis, modal analysis, electronic design, and acoustic testing.

For many years, DSAs were constrained by limited dynamic range and insufficient real-time bandwidth. But in the last several years, state-of-the-art components such as fast analog-to-digital converters (ADCs) and digital signal processors (DSPs) have dramatically improved the potential of these instruments.

A block diagram of a DSA is shown in Figure 1. DSAs typically have at least two input channels, since two channels are necessary to make the phase-sensitive measurements such as frequency response or cross spectrum that characterize many DSA applications. Input data from these channels is digitized by one or more ADCs.

The digital data stream is divided into a series of time records that are converted to spectral data by a discrete Fourier transform. The basic parameters of the input A/D conversion and Fourier transform directly govern most of the performance specifications of DSAs: The sampling rate corresponds directly to the maximum frequency range of the DSA, the number of bits in the ADC limits the dynamic range, and the number of samples in the time record dictates the frequency resolution of the spectral data.

Almost all modern DSAs use 16-bit ADCs, giving them a nominal dynamic range of 96 dB. However, the actual dynamic range of real instruments is limited not only by the converter resolution, but also by other noise sources and spurious signals, leading most manufacturers to quote spur-free dynamic-range values of about 90 dB.

Since most DSAs are used to analyze acoustic and mechanical signals, extremely high sampling frequencies are not needed. In fact, most DSAs use a sampling frequency between 100 kHz and 250 kHz, giving them a useful frequency range of about 50 to 100 kHz.

Some models multiplex a single ADC among many channels, forcing you to trade number of channels for frequency range, while others digitize all channels simultaneously with multiple ADCs. With multiple ADCs, two-channel measurements can be made using the full frequency range of the instrument rather than half its range, and independent frequency spans can be set on each channel.

Fast sampling rates are no guarantee that an analyzer can process its data quickly enough to keep up with the input data rate. This leads to the concept of real-time bandwidth, traditionally one of the important DSA specifications.

The real-time bandwidth is the bandwidth at which the acquisition time for the time record equals the time it takes to process the time data into a spectrum. Obviously, slower processing means longer time intervals between time records, which translates to slower real-time bandwidths. Advances in DSPs have led to DSAs with faster real-time rates, with some units such as Stanford Research System’s SR785 having a two-channel, real-time bandwidth in excess of 100 kHz.

Most DSAs feature an integrated signal source that can be used as a stimulus for measurements. Typical outputs include low distortion sine waves, pink and white noise, arbitrary waveforms, and a variety of chirps that can be synchronized to the FFT analyzer to produce a stimulus signal having a flat power spectrum.

DSAs are used mostly in the frequency domain, taking spectra and frequency response functions, and almost all modern DSAs have a variety of features that facilitate these measurements. One feature is real-time zoom, which allows you to focus in with increased resolution on some portion of the overall frequency range of the instrument.

Most DSAs have a variety of averaging modes that can reduce overall noise. These include synchronous (vector) averaging and rms averaging that decreases the shot-to-shot variations in the noise. Triggering modes often include pre- and post-event triggering to aid in capturing transient data.

In addition to the standard FFT mode, many analyzers offer a swept-sine analysis mode where frequency response is mapped a single frequency at a time. Gain is adjusted at each point in the frequency response curve to increase the overall dynamic range (often >120 dB) of measurements. The only downside to the swept sine mode is the increased measurement time.

Time-domain features found on DSAs usually include an oscilloscope-type mode for displaying the time-domain waveform, a histogram analysis mode that computes the amplitude distribution of the input signal, and auto- and cross-correlation measurements.

Order Tracking

Order tracking is used to evaluate the behavior of rotating machinery. In Figure 2, the DSA displays measurement data as a function of multiples of the shaft frequency (orders) rather than absolute frequency. Combined with a waterfall plot, the DSA provides a complete history or order map of data as a function of time or rpm. By using known “fingerprints,” you can diagnose problems like bearing failure or shaft alignment.

DSAs also track individual orders vs. rpm so you can profile particular orders of interest. In addition, they perform run-up and run-down measurements and display the results in polar or Bode formats.

Octave Analysis

In octave analysis, the DSA measures spectral power in octaves, with either 1, 3, or 12 bands per octave (1/1, 1/3, 1/12 octave analysis). Figure 3 shows an example of 1/3 octave analysis.

Input data is passed into a bank of parallel digital filters. The output from each filter is rms averaged to compute the spectral power in each band, and the results are displayed as a bar-type graph. Octave analysis measures spectral power in a similar way that people perceive sound and is very common in audio and acoustics applications.

About the Authors

Bob Kochhar is a sales engineer at Stanford Research Systems. He has been with the company for seven years and is a graduate of Purdue University with an M.S.E.E. e-mail: [email protected]
David Ames is the U.S. sales and marketing manager at Stanford Research Systems. Mr. Ames, who received an E.E. from San Jose State University, has been employed at the company for 13 years. e-mail: [email protected]
Stanford Research Systems, 1290-D Reamwood Ave., Sunnyvale, CA 94089, 408-744-9040.

Return to EE Home Page

Published by EE-Evaluation Engineering
All contents © 2001 Nelson Publishing Inc.
No reprint, distribution, or reuse in any medium is permitted
without the express written consent of the publisher.

September 2001

Sponsored Recommendations

What are the Important Considerations when Assessing Cobot Safety?

April 16, 2024
A review of the requirements of ISO/TS 15066 and how they fit in with ISO 10218-1 and 10218-2 a consideration the complexities of collaboration.

Wire & Cable Cutting Digi-Spool® Service

April 16, 2024
Explore DigiKey’s Digi-Spool® professional cutting service for efficient and precise wire and cable management. Custom-cut to your exact specifications for a variety of cable ...

DigiKey Factory Tomorrow Season 3: Sustainable Manufacturing

April 16, 2024
Industry 4.0 is helping manufacturers develop and integrate technologies such as AI, edge computing and connectivity for the factories of tomorrow. Learn more at DigiKey today...

Connectivity – The Backbone of Sustainable Automation

April 16, 2024
Advanced interfaces for signals, data, and electrical power are essential. They help save resources and costs when networking production equipment.


To join the conversation, and become an exclusive member of Electronic Design, create an account today!