Hello everyone! I am working with RapidMiner for a week now and I
cannot figure out how to solve my problem or to be more specific: I need
some inspiration for the work with RapidMiner.
Here is my starting point:
have a csv-file which contains several examples of data from sensors of
a fictional production machine. The first row will be a timestamp which contains the time when the sensor collected data. The second one will be the name of the event which happened. Attached you will find some data example as I cannot upload it here.
you can see, from time to time an error has accurred (yellow mark)
which I want to analyse why it happened. The assumption is that events
which happened in a short time before
"error occurred" have a higher possibility to cause this problem. Events
which happened a long time before the error occurred have a lesser
- After doing the tutorial and reading some
questions from the community I decided to try an agglomerative cluster
to cluster all the events which occurred in the time before the event
- That is why I want to take the event
"error occurred" as my zero and measure the time distances between zero
and the events happened before in order to determine which failure of a
sensor will probably lend into the the event "error occurred".
thought was to maybe split the data at a first step after each "error
occurred" into smaller sub-files and try to apply the agglomerative
Could you guys please give me an inspiration to
solve my problem or could you please tell me if this is possible like I
presented my ideas?
Thanks in advance and have a nice week!