Online vibration analysis #Post5
Dear OpenAdaptronik blog readers,
In this post I am gonna talk about changes my master thesis scope, which I encountered while conducting user story interviews as well as by reading the official project description of openadaptronik. I will also mention the user story format that I used and the outcome from it.
Let’s get started with the target audience.
It is extremely vital in the process of requirement engineering to figure out and understand the audience the system/ knowledge platform is going to cater to.
As I already mentioned on my previous posts that a big part of my thesis work involves requirement engineering. As a part of that process, I did run some user stories with the potential Customer, in my case, they are the my supervisors. With these new user stories, I found a very strong pattern on where this platform will fit the best. As mentioned earlier, in order to give a platform it’s value and meaning, it is very important for me to know the target audience. My thesis is a part of the bigger project ‘Openadaptronik’ however the knowledge platform is one of the most crucial parts in that project. As could be read from this description, which also clarifies the group of people the platform is meant for; the target audience.
As contrary to my previous understanding, it is a platform for everyone, who needs vibration analysis, the project ’openadaptronik’ mainly intended for specific vibration problem in photonic technologies and meant for ordinary citizen or SMEs to find solution to problem or providing them with an analysis platform. It is going to be a platform for curious individuals (neugieriger/interessierter Mensch), who are equipped with certain degree of knowledge on the field of mechanical vibration and open to learn and experiment with their gathered data.
It is still, of course, going to be open to everyone who is willing to use it,. However, the new found knowledge from user stories gives a much better and concrete idea about our target audience.
User Story and outcome:
My user story format followed a simple and classic pattern of ‘who’, ‘what’ and ‘why’.
As a <user/ who>, i want <something/ what>, so that <it benefits…/why>. After examining about more than 10 user stories it was very clear to me that it is meant for curious individual who are interested to find the reason or get an insight for a vibration related problem, in a word, ‘Makers’.
More or less all the stories involved the problem of Noise/Loudness causing from vibration of some mechanical part that is available at our household. For example, a microwave, a vacuum cleaner, a fan, an air condition, etc.
The second type of users are those interested individual who owns small to medium sized firm which produces electrical or mechanical parts/components and also uses sensors to measure different data, they are interested to get an insight into their data, they can upload their data analyse it. The last, yet the most important type of individuals are the makers (the university students, engineers, researchers, the readers of the blog,you.. etc…) who likes to work on handicraft work (Bastelarbeit) and can use a platform for their data as well as running analysis on those data. Essentially, this is the optimization category, which I spoke about and explained on this post.
Scope of the platform:
Along with the right target group identification, it is also very important to know the assumption about the user knowledge or the skill in order to define the scope of the platform. In the following info-graphic, I tried to explain in a complete chain of the problem solving, where does the platform fit. Under yellow line is the part where our platform scope is and at the top of the purple line is the part that is assumed knowledge or skills of the target audience.
Take away from the changes:
Although it changes the system design perspective, since I progressed by keeping a different audience category and different use cases in mind, it is still going to be one of its first kind, a big data repository for mechanical vibration data. Where anyone, who has collected vibration measurement data using vibration measurement sensors (e.g. accelerometer, etc ) can upload data and eventually analyse their data.
The ultimate goal of the platform is to use all that measurement data in the future to run big data analysis to find unknown and useful facts about vibration. In order to create training dataset for machine learning, first, we need historical data. Only after that we can collect data from live machine/components in order to do predictive maintenance. However, before and above all, this platform gives opportunity to the whole community to upload data and analyse it. Also the goal is to be able to do vibration analysis without having to pay for expensive software and services. The data is always available as well as the possibility to analyse the data is always available to a user who has access to a computer with internet; a cloud based solution.
In my next post I will talk about the nitty-gritty of the platform and the my experience with pyuff and plotly.