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In the interesting field of IoT a lot of buzz is made around the predictive maintenance use cases. What is predictive maintenance?
The main promise of predictive maintenance is to allow convenient scheduling of corrective maintenance, and to prevent unexpected equipment failures.
The key is “the right information in the right time”. By knowing which equipment needs maintenance, maintenance work can be better planned (spare parts, people, etc.) and what would have been “unplanned stops” are transformed to shorter and fewer “planned stops”, thus increasing plant availability. Other potential advantages include increased equipment lifetime, increased plant safety, fewer accidents with negative impact on environment, and optimized spare parts handling.
So in simpler terms: If you can predict that something will break you can repair it before it breaks. This improvse reliability and save costs, even though you repaired something that did not yet need repairs. At least you would be able to reduce inconveniences by repairing/maintaining when it still is easy to be done rather than under stress.
You would probably agree with me that these are a very industry-specific use cases. It’s easy to understand when it is tied to an actual case that happened.
Let me tell you a case that happened here last week. It happened to Leela – a 10 year old white British short hair lady cat with gorgeous blue eyes:
Ever since her sister had developed a severe kidney issue we started to unobtrusively monitor their behavior and vital signs. Simple things like weight, food intake, water intake, movement, regularities (how often x/y/z).
When Leela now visits her litter box she is automatically weighed and it’s taken note that she actually used it.
A lot of data is aggregated on this and a lot of things are being done to that data to generate indications of issues and alerts.
This alerted us last weekend that there could be an issue with Leelas health as she was suddenly visiting the litter box a lot more often across the day.
We did not notice anything with Leela. She behaved as she would everyday, but the monitoring did detect something was not right.
What had happened?
On the morning of March 9th Leela already had been to the litter box above average. So much above average that it tripped the alerting system. You can see the faded read area in the top of the graph above showing the alert threshold. The red vertical line was drawn in by me because this was when we got alerted.
Now what? She behaved totally normal just that she went a lot more to the litter box. We where concerned as it matched her sisters behavior so we went through all the checklists with her on what the issue could be.
We monitored her closely and increased the water supplied as well as changed her food so she could fight a potential bladder infection (or worse).
By Monday she did still not behave different to a degree that anyone would have been suspicious. Nevertheless my wife took her to the vet. And of course a bladder infection was diagnosed after all tests run.
She got antibiotics and around Wednesday (13th March) she actually started to behave much like a sick cat would. By then she already was on day 3 of antibiotics and after just one day of presumable pain she was back to fully normal.
Interestingly all of this can be followed up with the monitoring. Even that she must have felt worse on the 13th.
With everything back to normal now it seems that this monitoring has really lead us to a case of “predictive cat maintenance”. We hopefully could prevent a lot of pain with acting quick. Which only was possible through the monitoring in place.
Health is a huge topic for the future of devices and gadgets. Everyone will casually start to have more and more devices in their daily lifes. Unfortunately most of those won’t be under your own control if you do not insist on being in control.
You do not have to build stuff yourself like I did. You only need to make the right purchase decisions according to things important to you. And one of these things on that checklist should be: “am I in full control of the data flow and data storage”.
If you are not. Do not buy!
By coincidence the idea of having the owner of the data in full control of the data itself is central to my current job at MindSphere. With all the buzz and whistles around the Industry IoT platform it all breaks down to keep the actual owner of the data in control and in charge. A story for another post!
Cascading Style Sheets or CSS in short are a very powerful tool to control how content is being displayed.
CSS is designed to enable the separation of presentation and content, including layout, colors, and fonts. This separation can improve content accessibility, provide more flexibility and control in the specification of presentation characteristics, enable multiple web pages to share formatting by specifying the relevant CSS in a separate .css file, and reduce complexity and repetition in the structural content. Separation of formatting and content also makes it feasible to present the same markup page in different styles for different rendering methods, such as on-screen, in print, by voice (via speech-based browser or screen reader), and on Braille-based tactile devices. CSS also has rules for alternate formatting if the content is accessed on a mobile device.
I frequently come across content I want to read. And almost as frequently I do not have time for a longer read when I come across interesting content.
My workflow for this is: keeping some to-be-read backlog of PDF files I have printed from websites. These PDF files are automatically synced to various devices and I can read them at a later stage.
What often is frustrating to see: bad the print results of website layouts as these websites have not even thought of the remote option of being printed.
With this blog I want to support any workflow and first and foremost my own. Therefore printing this blog adds some print-audience specifics.
For example the links I am using in the articles are usually inline when you are using a browser. When you’re printing the article those links get converted and are being written out with the text. So you can have them in your print-outs without loosing information.
And the changes you need to apply to any webpage to instantly enable this are very simple as well! Just add this to your page stylesheet:
I had redone the header of this blog a while ago but since I was trying around some things on the template I wanted something more dynamic but without any additional dependencies.
So I searched and found:
Tim Holman did a very nice implementation of this “worm generator” with only using the HTML5 canvas tag and some math. I made some very slight changes and integrated it into the header graphic. It will react to your mouse movement and resets if you click anywhere. Give it a go!
We are looking at our screens more and more time of the day and most of that time we are reading or writing text. Text needs to look pretty for our eyes not to get sore – apart from the obvious “being able to tell what letter that is” there is a big portion of personal taste and preference when it comes to the choice of the font.
“Ever notice how people texting at night have that eerie blue glow?
Or wake up ready to write down the Next Great Idea, and get blinded by your computer screen?
During the day, computer screens look good—they’re designed to look like the sun. But, at 9PM, 10PM, or 3AM, you probably shouldn’t be looking at the sun.
f.lux fixes this: it makes the color of your computer’s display adapt to the time of day, warm at night and like sunlight during the day.
It’s even possible that you’re staying up too late because of your computer. You could use f.lux because it makes you sleep better, or you could just use it just because it makes your computer look better.”
“The Data Visualisation Catalogue is currently an on-going project developed by Severino Ribecca.
Originally, this project was a way for me to develop my own knowledge of data visualisation and create a reference tool for me to use in the future for my own work. However, I thought it would also be useful tool to not only other designers, but also anyone in a field that requires the use of data visualisation regularly (economists, scientists, statisticians etc).
Although there have been a few attempts in the past to catalogue some of the established data visualisation methods, there is no website that is really comprehensive, detailed or helps you decide the right method for your needs.
I will be adding in new visualisation methods, bit-by-bit, as I research each method to find the best way to explain how it works and what it is best suited for.”
Since my wife started working as a photographer on a daily basis the daily routine of getting all the pictures off the camera after a long day filled with photo shootings got her bored quickly.
Since we got some RaspberryPis to spare I gave it a try and created a small script which when the Pi gets powered on automatically copies all contents of the attached SD card to the houses storage server. Easy as Pi(e) – so to speak.
So this is now an automated process for a couple of weeks – she comes home, get’s all batteries to their chargers, drops the sd cards into the reader and poweres on the Pi. After it copied everything successfully the Pi sends an eMail with a summary report of what has been done. So far so good – everything is on our backuped storage server then.
Now the problem was that she often does not immediately starts working on the pictures. But she wants to take a closer look without the need to sit in front of a big monitor – like taking a look at her iPad in the kitchen while drinking coffee.
So what we need was a tool that does this:
take a folder (the automated import folder) and get all images in there, order them by day
display an overview per day of all pictures taken
allow to see the fullsized picture if necessary
work on any mobile or stationary device in the household – preferably html5 responsive design gallery
it should be fast because commonly over 200 pictures are done per day
it should be opensource because – well opensource is great – and probably we would need to tweak things a bit
It’s pretty fast because it’s not actively resizing the images – instead it’s taking the thumbnail picture from the original jpg file which the camera placed there during storing the picture. It’s got some caching and can be run on any operating system where mono / .net is available – which is probably anything – even the RaspberryPi.