What’s faster, MATLAB or Python?  There are a lot of forums out there comparing the two programming languages; but none seemed to give actual computation times for real analysis.  This post will.


As an engineer I've always been intrigued by the Tacoma Narrows bridge collapse. Just the thought of how wind can excite the bridge's structural resonance to a point where it collapses is pretty incredible.


My last post laid out the basics of the Fourier transforms, but what happens when you try to apply this knowledge in the field? In Fourier transforms, one of the big issues engineers see is that the frequencies that appear in their data do not line up well with the analysis frequency, so the signal starts to “leak” into the surrounding frequencies.

Signals rarely ever line up perfectly, but these issues can be cleared up by taking more data and applying a technique called windowing. Before we get to the solution, let’s look at the problem in more detail.

I was chatting with my colleague Steve Hanly about his recent post on the Fourier transform and power spectral density, and we thought it might help to go a bit more into the math and guts of the Fourier transform. As we know, the Fourier transform is a common and useful engineering tool for analyzing signals and vibrations, but sometimes it can produce some hard to interpret results.

My aim for this post is to start things off with a refresher on the basics of the math behind the Fourier transformation, and lay the foundation for future posts that will go into more detail on how the Fourier transform should be used and interpreted.


I recently spoke with Steve Taylor, senior researcher from Cannon Instruments, who is using piezoelectric fan technology to solve some of his thermal management challenges. Steve has given permission for me to share his experience.

The Challenges 

Steve's two challenges were reliably operating a fan at high temperature, and in a confined space. Cannon is developing a product for the oil and gas industry that measures the properties of fluids.  

His new product needed to increase evaporation at the surface of the fluid samples. When Steve reached out to me he said he needed; "a way to circulate air in a narrow space at temperatures up to 140 C." He went on to say that,

FFT, PSD and spectrograms don't need to be so complicated. Once you understand the basics they can really help with your vibration analysis.

In this post I'll try to provide the right mix of theory and practical information, with examples, so that you can hopefully take your vibration analysis to the next level!

In order to effectively do your job of vibration analysis, you may be more interested in some practical information; but it is important to understand a bit of the theory behind FFTs, PSDs and spectrograms. I'll provide an overview of the math behind the FFT, PSD and spectrogram (for more detail, check out our blog on Fourier Transforms); but I'll use plots to make my point instead of only equations and text.

Let's go through the important differences between an FFT, PSD, and spectrogram and I'll try to illustrate when it is appropriate to use each type of vibration analysis tool.  You'll be doing your vibration analysis in the real world so we'll look at real world examples, and analyze data captured from an actual accelerometer.

All data presented and the MATLAB scripts used to perform the vibration analysis will be available to download at the bottom of the page so you can do your own vibration analysis.  

I get this question quite a lot... unfortunately the short answer is no.

However, there is hope (see below), just not from vibration energy harvesting.



If you have ever shot a firearm, you are familiar with the ‘kick’ that occurs at every shot.  This imparts forces and stresses on the firearm, which are transferred into the shooter's shoulder and to any equipment that is attached to the firearm.

Why, why, why are accelerometer datasheets so confusing?  There's a reason. Accelerometer companies, understandably, try to position their products in the best light possible; and they often do so by using complicated terminology and units for the accelerometer's specifications.

I frequently talk with customers who don't fully understand the different specifications used on an accelerometer's datasheet.  And this makes shopping for an accelerometer an even more difficult task than it already is!

In this post I will provide brief descriptions of 10 specifications often listed on accelerometer datasheets that you can use as a reference for whenever you are shopping around for accelerometers - the sensor that tells us how the world moves! I've also included a one page cheat sheet for quick reference - the link is at the bottom of the page.

You know you need an accelerometer; but how do you select the right type for your application?  You’re not alone if you don’t know much about the different types of accelerometers and how they can impact your results.  Most engineers I talk to are surprised to learn just how important accelerometer selection is; and some didn’t even know that there were different types to begin with!

There are three main sensing technologies or types (capacitive MEMS, piezoresistive, and piezoelectric).  But before you can even start considering what accelerometer type works best, it's important to first identify exactly what you are looking to measure.

Posted by:

Comments Section