2020 Trade Shows are cancelled so let DYNOmite bring them to you...

Trade Show Update

10/21/2020

Trade Shows have been cancelled for 2020 and we are disappointed as you are. But in anticipation of this reality, we have been working hard to create a new-to-the-world virtual booth for our devoted Customers. You will hear more about this in the next weeks, but look for:

  1. Live streaming content with industry experts
  2. Demonstrations with service techs of our equipment
  3. Live chat with Sales and Service staff to discuss industry needs and innovations
  4. Watch as some of the most popular SuperFlow and DYNOMite products are being built
  5. the coolest opportunity the industry has ever experienced – YOUR chance to interact with our Innovation team to finalize versions of software and controls

DYNOmite views this year as an opportunity to engage with you in ways we haven't been able to in the past. Please visit our show page to register for additional information as it becomes available.

Dampening Data


Read about when to average, dampen, or otherwise filter the display of dynamometer test data.

Courses | 07.28.20

How can I smooth my data?

Average, dampen, or filter? Computers can record engine information at an incredible pace, but humans still absorb it slowly. To maximize a test’s value, raw data can be mathematically processed (in DYNO-MAX) by one or more of these methods.

Averaging: Takes anywhere from two to thousands of consecutive data points and combines them into a single data point. It is analogous to driving cross county, taking MPH readings every second, then reporting this data as a single average speed for the entire trip (or alternatively as a daily MPH average).

Dampening: Unlike simple averaging which reduces the quantity of data points, dampening does not condense data. Rather, it reduces the significance of any single data point by averaging it with one or more sets of adjacent points on either side of the center reading. This is equivalent to using an oil filled pressure gauge in order to get a steady reading from a pulsating source.

Spike Filtering: Occasionally large transient pulses (either real or artificially induced) creep into data. Such “noise” needles in a graph or data listing are distracting, especially to novice operators. Filters use algorithms to “intelligently” strip the high frequency pulses out of otherwise meaningful data.

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