• Jade Vande Kamp - VR

Acquiring Field Data and Creating Vibration Tests

Effective vibration testing simulates the field environments a product or package will experience in the real world. Because understanding how a product reacts to the real world is critical, testing labs often use field data to create tests that simulate real-world conditions.


This paper first discusses the proper techniques for collecting field data and then examines three methods for using that data to create tests, Field Data Replication, Random Import and Fatigue Damage Spectrum. The paper ends with comments on test acceleration.


Figure 1 - Sample Data Acquisition Hardware - ObserVR1000

Collecting Field Data


Collecting field data is essential to realistic vibration testing. The data can be used to validate existing testing methods, to create new tests, or even be directly replicated on a shaker. For all of these uses, collecting accurate data is critical.

First, use an accelerometer that will not saturate in the environment being recorded. Most IEPE accelerometers have a ±5V signal. This means that a 100 mV/G accelerometer typically operates up to approximately 50 G’s of acceleration and a 10 mV/G accelerometer up to 500 G’s.

Effective accelerometer mounting requires an appropriate method and location. Beeswax and magnetic mounting are easily implemented but will lower the resonance of the accelerometer due to the poor connection between the accelerometer and the device under test (DUT), reducing the recording accuracy. Mounting with an appropriate adhesive/cement gives better results, while stud mounting is by far the best method.

Figure SEQ Figure \* ARABIC 1 - Sample Data Acquisition Hardware - ObserVR1000 Mounting position is also a significant consideration. The accelerometer should be placed in the axis of the predominate vibration. Then, in the laboratory, the accelerometer should be mounted in the same location and orientation as used for data recorded.

Properly secured cables prevent cable whip and connector strain. Cable whip induces noise, especially on high-impedance signal paths, due to the triboelectric effect. Cable strain near either electrical connectors leads to intermittent or broken connections and data loss.

Other data recording considerations include the number of channels, battery life, and sample rate, all of which can affect the maximum recording time.


Field Data Replication


Field Data Replication (FDR) is an exact 1:1 replication of a field recording. This method is closest to the real world but can be very time consuming. A 1:1 playback means that data is not randomized and typically it is not accelerated, though more intense testing can be achieved by increasing vibration amplitude.

FDR tests are often used to analyze response to an environment. A common application is Buzz, Squeak, and Rattle (BSR) testing, where an engineer listens for anything within the human hearing range that could be excited by a field environment.

There are two FDR control methods. The first is an iterative algorithm, using a pseudo-closed loop control. The waveform is played at a low level, then scaled/incremented, repeating the process until the entire playback falls within the required tolerances based on voltage measurements. This works but while the waveform is “playing” there is not true closed-loop feedback control. Even if a product fails or an accelerometer falls off, the test continues running to the end of the waveform.

The other control method is a predictive algorithm. It requires a controller with significant processing power and dynamic range but results in a test more responsive to changes or failures. Using true closed-loop feedback control, it reacts to failures by shutting down the test, avoiding damage to the DUT, shaker and transducers.


Random Import


Random Import analyzes a field recording and creates a random test profile based on the data using one of two methods, Average or Peak Hold. The Average method combines the FFT while Peak Hold concentrates the energy on the peak acceleration for a section of time. Both methods provide valuable insight and can be used to generate a random test profile. However, some engineering justifications are needed, especially when an environment is non-Gaussian.

With the Average method, high peaks from a non-Gaussian environment are not properly represented in the random PSD calculation, resulting in an undertest. Conversely, the Peak Hold method over-emphasizes these peaks, resulting in an overtest.


Fatigue Damage Spectrum


Analyzing an environment or series of environments using the Fatigue Damage Spectrum (FDS) and then converting the resultant FDSes to create a PSD is an accurate method of determining the appropriate test profile for a single environment or multiple environments. While FDS has assumptions that must be addressed, it is still the most widely used method for generating random tests based on field environments.

While it is possible to generate an FDS from an FFT, this is applicable only if the data being analyzed is Gaussian in nature because the conversion from PSD to FDS assumes a Gaussian output. Via cycle counting and rainflow analysis, FDS can account for any non-Gaussian nature of the field environments, and maintain a time-based relationship between the original field data, the FDS and the resultant PSD. This time base relationship allows for reliable test acceleration. See Figure 2, where the FDS generated PSD falls between the Average and Peak Hold PSDs.

The non-Gaussian nature of many industries creates a much more damaging environment due to the increase in peak acceleration. Properly accounting for the damage created by peaks is essential to creating a representative test. When needed, non-Gaussian content can be re-introduced into a random test so that the peak acceleration of the test is equivalent to the real world peak acceleration.


Figure 2 - Resultant PSD Comparison between Average, Peak Hold and FDS


Accelerate the Test:


Target life and test duration are the final elements required to generate a test profile. The test duration can be changed to accelerate the test. The time-based method used to generate the FDS allows for reliable test acceleration and proper accounting for any non-Gaussian nature in time history files. When a test is accelerated, inaccuracy is added to the calculation. The ratio of target life to test duration should not cause instantaneous shock on the product or apply high cycle fatigue to the product.

One way to determine the maximum acceleration level is the ‘Did it break?’ method, requiring multiple product samples. The first sample runs through the new, accelerated test. If it doesn’t fail, assume that the test is not over-accelerated. If it fails, there are two possible conclusions: either the test is over-accelerated and the failure is not realistic or the product is weak and will fail in the real world. The testing is then repeated, iteratively, with decreased acceleration until the product passes or the life-time of the product is reached. At that point the decision can be made on product viability.


Conclusion:


There are several methods for using field data to create tests. For all the methods, acquiring valid and accurate data is the first priority. Then, when creating a test, the test type needs to fit the testing requirements. For example, creating a lifetime of damage in a Field Data Replication test is simply unrealistic if your product has thousands of hours of lifetime expectancy. Finally, using the appropriate calculation and control methodology for any test profile will result in a test that is valid and allows engineers to draw useful conclusions.

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