MSc Robotics Final Project by Johanna Amunyela

Observation while building the model  I noticed that the data I am working with is labeled data, with features and a label outcome. This means I have to look at it from a prediction perspective. prediction can be a classification or a regression problem. In my case, the outcome has only two class labels. 0 or 1 meaning the drill bit is down for mining or up to move to the next mining ground. Hence binary classification. Overall there are obvious relationships and some that are not. Regression will work best in advancing to the output label being a fluctuating value between an upper threshold and lower threshold value. For the data, I initially loaded the data and use scatter matrix and histogram functions from pandas and matplotlib libraries to try and see if there are more relations or something new. To teach the model the relationship between the feature and the outcome, I used several algorithms and compared them to see which one performed best in this scenario. see code below:...

I have drafted a flow chart of the old drilling process.  I am currently finalizing the proposed drilling process and state machine flow chat to illustrate how the model drill bit will mine below:

Random Probable simulation options i found interesting to watch 

Error Log  SQL errors encountered  Challenges  The data I need sits on a server that is on a vessel out at sea x using line of site and the vessel is about +-9000 km from me . The connection is lagging causing the response time to be slow. To over come this I started working to refine the sql query with picking a smaller range for the data  With a 1 hour  and the top 1000 records filter. Once I was satisfied with the query I remove the records filter and started adjusting the time filter.  For now I am going to try and work on a day worth of data and see if I can find new relationships and or anomaly.  The days worth of data posed another challenge I couldn't copy it of the server into the excel sheet as I did with the smaller data set , so I exported the file as a csv file and push the file of the server. Conversion failed when converting date and/or time from character string. solution : I removed the 00 to match the data format  The conversion ...

 

  Observable variables (inputs ) Name                                    Range                           Availability           potential importance  Airflow                      2000 - 2600,  preferred airflow is at 2200 CFM/KPA  air pressure            7 to 10 bars          water depth              shallow 70-90 meters   7- 8                                           optimum 90-130  9 compressors               ...

  Moving the vessel into position The role of the navigator is to observe the snail trail of the vessel and simultaneously move the vessel into a position that is in the vicinity of the targeted mining holes. He or She does this guided by a theoretical hole image displayed on the mining system.  The navigator while moving the vessel into the desired position should avoid overshooting the drill's placement. 

  Drillers mining parameter The ML model would focus on automating some if not most of the driller's functions performed to achieve timely drill bit composition.  Functions - the abovementioned function is the manual setting of the following mining parameter. Gravel -is the material extracted from the sea flow and pumped up onto the vessel for further processing. Mining airflow:  we need to take note that if the air pressure is too low, it will not be able to lift the gravel. Similarly, it's too high it will result in an overflow of gravel.  one needs to achieve a correct range else the incorrect values will cause blockages.      air pressure             The ideal air pressure should be within 7 to 10 bars, and it is mostly prepared at 8 bars.        airflow           The airflow ranges from 200 - 2600, but the prepared air flow is at 2200 (need to reconfirm these...