12-29-2016 01:55 PM
New to Forum and I wonder why I didn't use this before as I have been using RapidMiner for educational purposes since 2011.
Anyways, I am in need of use cases if RapidMiner for big data/ Industry 4.0 setting .Smart factories and such. I know there is a sample process describing predictive maintenance but I wonder whther it is possible to analyze stream data with RapidMiner (for pattern recognition, fault diagnosis, pattern recognition etc.) and if so what is the process like? Also if sample data sets exists that would be great for test.
12-29-2016 02:06 PM
We have actually quite a lot of customer engagements in the manufacturing industry - in fact, this is one of the three largest industries we serve. You can find a few case studies here: https://rapidminer.com/resources/?type=case-studie
It typically comes down to scenarios like the following:
Sensor data is typically involved but that does not necessarily require stream handling in the modeling phase (only in application). In 99% of all use cases we see, the models are trained offline on batches of data and then the model is applied on data streams in an optimized architecture. RapidMiner perfectly supports this scenario.
Sorry, I can publicly share more insights into what our clients are doing (not even talking about data or processes of course!) but if this is for a commercial project please feel free to reach out to our sales reps to discuss your situation and we might be able to give you more ideas.
12-29-2016 03:55 PM
Thanks for fast reply. Case studies, demos and webinars are all good references. But Predictive Maintenance process built in Studio is a very helpful example and similar ones I am looking for.
Can you direct me to similar examples for:
This is mainly for training purposes but can lead to a commercial project as well.
12-29-2016 04:43 PM
All the processes and data I have are customer-related and hence I cannot share, sorry
Maybe somebody else has something readily available...
However, for those three actually only the Root Cause Analysis can be somewhat special (since it really depends a lot on the data and problem types you are tackling). Yield optimization and quality assurance can typically be mapped on some form of basic classification or regression problems:
Hope this helps a bit,
12-29-2016 05:40 PM
funny i wrote a quick blog article und prespriptive SPC last week. Have a look here: https://www.linkedin.com/pulse/overcome-spc-prescr