This thesis tries to obtain information about how works the Support Vector Machines
method for statistical learning applied to a pump within a hydraulic installation. Support
Vector Machines, known as SVM, is a method that teaches a learning machine
how to classify the data of a system. The system under study, in this case the pump, is
tested in the loop and measured for a later classification. This method has been used
in many applications due to its predicting function. Support Vector Machines involves a mode for turning a machine (the computer)
able to make difference between the values of a parameter. So, the pumping sytem can
be monitored and measured and then, recording the vibrations that the pump produces,
the machine is trained for recognizing the vibrations and associate them with a value
of the properties of the pumping system. In this way, the learning machine predict the
values of the desired magnitude:
ow rate or rotation speed among other.
The machine has in advance the measures and learn from them and their values,
that is why is called supervised learning. The accuracy that the learning machine develops
is obtained comparing the predicted values with the actuals, and it is represented
using a visual tool called confusion matrix.
For the vibration is used a sensor coupled to the pump that takes the signal and
provides it in order to be recorded with the values of the rest of the parameters measured. This project has been assisted by two different computer programs. First, LabView
was used for the recording as an interface and then, Python applied the SVM method
to the data. The sensors fixed in the workbench measure the properties of the pumping system
and parallel, the accelerometer take the signal of the vibrations produced in the pump.
In the LabView program, loops of 100 seconds are recorded with a established
flow
rate, rotation speed and sensor position. The measures are recorded varying the values
of the mentioned magnitudes in order to make different groups of classes.
After that, the data achieved feed the Python program. The classifications are done
attending to different criteria and are analyzed in order to extract as much information
and conclusions as possible.Ingeniería Industria
