Hackaday Prize Entry: Open Source FFT Spectrum Analyzer

Every machine has its own way of communicating with its operator. Some send status emails, some illuminate, but most of them vibrate and make noise. If it hums happily, that’s usually a good sign, but if it complains loudly, maintenance is overdue. [Ariel Quezada] wants to make sense of machine vibrations and draw conclusions about their overall mechanical condition from them. With his project, a 3-axis Open Source FFT Spectrum Analyzer he is not only entering the Hackaday Prize 2016 but also the highly contested field of acoustic defect recognition.

open_fft_machineFor the hardware side of the spectrum analyzer, [Ariel] equipped an Arduino Nano with an ADXL335 accelerometer, which is able to pick up vibrations within a frequency range of 0 to 1600 Hz on the X and Y axis. A film container, equipped with a strong magnet for easy installation, serves as an enclosure for the sensor. The firmware [Ariel] wrote is an efficient piece of code that samples the analog signals from the accelerometer in a free running loop at about 5000 Hz. It streams the digitized waveforms to a host computer over the serial port, where they are captured and stored by a Python script for further processing.

From there, another Python script filters the captured waveform, applies a window function, calculates the Fourier transform and plots the spectrum into a graph. With the analyzer up and running, [Ariel] went on testing the device on a large bearing of an arbitrary rotating machine he had access to. A series of tests that involved adding eccentric weights to the rotating shaft shows that the analyzer already makes it possible to discriminate between different grades of imbalance.

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Taming Robot Arm Jump with Accelerometers

Last fall, I grabbed a robot arm from Robot Geeks when they were on sale at Thanksgiving. The arm uses servos to rotate the base and move the joints and gripper. These work well enough but I found one aspect of the arm frustrating. When you apply power, the software commands the servos to move to home position. The movement is sufficiently violent it can cause the entire arm to jump.

This jump occurs because there is no position feedback to the Arduino controller leaving it unable to know the positions of the arm’s servos and move them slowly to home. I pondered how to add this feedback using sensors, imposing the limitation that they couldn’t be large or require replacing existing parts. I decided to try adding accelerometers on each arm section.

Accelerometers, being affected by gravity when on a planet, provide an absolute reference because they always report the direction of down. With an accelerometer I can calculate the angle of an arm section with respect to the direction of gravitational acceleration.

Before discussing the accelerometers, take a look at the picture of the arm. An accelerometer would be added to each section of the arm between the controlling servos.

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Master’s UAV Project Takes Flight

Pushing the maker envelope all the way to the Master level, [Przemyslaw Brudny], [Marek Ulita], and [Maciej Olejnik] from the Politechnika Wroclawska in Poland packed a UAV full of custom sensor boards for their thesis project.

The Skywalker X-8 FPV drone underwent extensive modifications to accommodate the embedded systems as well as upgrading the chassis with carbon glass to withstand the high load and speeds they would need to perform their tests. The ailerons were customized for finer control of the drone. But for our money, it’s all the board design that supports those sensors which is really fun to delve into.

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Small And Inexpensive MEMS Gravimeter

A gravimeter, as the name suggests, measures gravity. These specialized accelerometers can find underground resources and measure volcanic activity. Unfortunately, traditional instruments are relatively large and expensive (nearly 20 pounds and $100,000). Of course, MEMS accelerometers are old hat, but none of them have been stable enough to be called gravimeters. Until now.

In a recent edition of Nature (pdf), researchers at the University of Glasgow have built a MEMS device that has the stability to work as a gravimeter. To demonstrate this, they used it to measure the tides over six days.

The device functions as a relative gravimeter. Essentially a tiny weight hangs from a tiny spring, and the device measures the pull of gravity on the spring. The design of the Glasgow device has a low resonate frequency (2.3 Hz).

Small and inexpensive devices could monitor volcanoes or fly on drones to find tunnels or buried oil and gas (a job currently done by low altitude aircraft). We’ve covered MEMS accelerometers before, although not at this stability level.  We’ve even seen an explanation from the Engineer Guy.

Open Sesame, from a Galaxy far, far away.

[TVMiller]’s description of his project is epic enough to deserve a literal copy-paste (something our readers often praise us about). In his own words,  “Having discovered several spare Midichlorians in my liquor cabinet, I trained and applied them to opening a large cumbersome gate. The FORCE motion travels through my inner what-nots and is translated by the Pebble Classic accelerometer toggling a command sent to the (Particle) Cloud (City) which returns to the Particle Photon triggering a TIP120 to fire a button on an existing RF transceiver. May the ridiculous hand gestures be with you, always.” Thus was born the Gate Jedi , and you’ll need exactly 47 Midichlorians, and some other trivial parts, to build one.

The Pebble watch hooks up to his android smart phone. A Pebble (android) app sends the accelerometer data to the Particle (previously called Spark) cloud service. From there, the data is pushed to the Photon IoT board which runs a few lines of code. Output from the Photon turns on a TIP120 power transistor, which in turn triggers the existing RF trans receiver that opens the Gate.

This looks way cooler than the Light Sabre hacks. Check out the video of him summoning the Force. And if you’d like to do more, try integrating gesture controls with this Pebble Watch hack that turns it into a home automation controller.

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RaspiDrums Uses Expensive Sensors

Piezoelectric sensors are great for monitoring mechanical impacts with a microcontroller. Whether you’re monitoring knocks on a door or watching a heartbeat, they are a cheap way to get the job done. They do have their downsides, though, so when [Jeremy] wanted to build an electronic drum set, he decided to use more expensive accelerometers to measure the percussive impacts instead.

Even though piezo sensors are cheap, they require a lot of work to get them working properly. The ADXL377 3-axis accelerometer that [Jeremy] found requires much less work, plus provides more reliable data due to a 1kHz low-pass filter at the output. In his setup, a Raspberry Pi handles all of the heavy lifting. An ADC on each drum sends data about each impact of the drum, and the Raspberry Pi outputs sound via the native Alsa driver and a USB sound card.

This project goes a long way to show how much simpler a project like this is once you find the right hardware for the job. [Jeremy]’s new electronic drums are very well documented as well if you are curious about using accelerometers on your newest project rather than piezo sensors. And, if you’re into drums be sure to see how you can have drums anywhere, or how you can build your own logic drums.

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Hackaday Prize Entry: Project Dekoboko 凸凹 Maps Bumpy Roads On A Bike

If you live in New England (like me) you know that the roads take a pounding in the winter. Combine this with haphazard maintenance and you get a recipe for biking disaster: bumpy, potholed roads that can send you flying over the handlebars. Project Dekoboko 凸凹 aims to help a little with this, by helping you map and avoid the bumpiest roads and could be a godsend in this area.

The 2015 Hackaday Prize entry from [Benjamin Shih], [Daniel Rojas], and [Maxim Lapis] is a device that clips onto your bike and maps how bumpy the ride is as you pedal around. It does this by measuring the vibration of the bike frame with an accelerometer. Combine this with a GPS log and you get a map of the quality of the roads that helps you plan a smooth ride, or which could help the city figure out which roads need fixing the most.

bike-measures-bumpy-roads-thumbThe project is currently on its  third version, built around an Arduino, Adafruit Ultimate GPS Logger shield, and a protoboard that holds the accelerometer (an Analog ADXL345). The team has also set up a first version of their web site, which contains live data from a few trips around Berlin. This does show one of the issues they will need to figure out, though: the GPS data has them widely veering off the road, which means that the data was slightly off, or they were cycling through buildings on the Prinzenstrasse, including a house music club. I’ll assume that it was the GPS being inaccurate and not them stopping for a rave, but they will need to figure out ways to tie this data down to a specific street before they can start really analyzing it. Google Maps does offer a way to do this, but it is not always accurate, especially on city streets. Still, the project has made good progress and could be useful for those who are looking for a smooth ride around town.

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