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## Abstract

This dissertation deals with the study, development and implementation of condition monitoring algorithms for automatic machines. These are implemented in the machine's PLC.
First, data are collected from the machine. Each motion group has been studied to sample the most relevant points (10 points in total) in its trajectory.
Second, data have been analysed. In particular, normality of data has been checked.
Third, the condition monitoring task is fulfilled with a statistical analysis. In this context two tests are presented: the scalar test and the vector test.
The scalar test consists in a simple and immediate test. This must be evaluated for all the 10 sampled points.
The vector test takes into account the relationship between all the points. The outcome is just a single number.
The vector test can be used to check the state of all different axes and, if showing some criticalities, the scalar test can be used to check why a particular axis is not performing as expected.
A crucial issue is to find the healthy values (reference) for each motion group. This, in fact, is not constant but varies with temperature. A solution to obtain a robust reference value is presented in this thesis.
Lastly, the condition monitoring task has been fulfilled with some machine learning techniques as well. A classifier is trained to estimate the time elapsed from last maintenance of a motion group. This can be then compared with the actual time from last maintenance to detect if the component is ageing worse than it should.
To build the classifier, data have been acquired and elaborated. The final classifier is a voting classifier that shows some interesting robustness properties. To decide, it makes three sub-classifiers vote and chooses the majority.
Also, a graphical user interface has been created with the machine learning approach. This can be added to the human-machine interface panel.
Finally, a thorough comparison between the two approaches is presented.

Abstract

This dissertation deals with the study, development and implementation of condition monitoring algorithms for automatic machines. These are implemented in the machine's PLC.
First, data are collected from the machine. Each motion group has been studied to sample the most relevant points (10 points in total) in its trajectory.
Second, data have been analysed. In particular, normality of data has been checked.
Third, the condition monitoring task is fulfilled with a statistical analysis. In this context two tests are presented: the scalar test and the vector test.
The scalar test consists in a simple and immediate test. This must be evaluated for all the 10 sampled points.
The vector test takes into account the relationship between all the points. The outcome is just a single number.
The vector test can be used to check the state of all different axes and, if showing some criticalities, the scalar test can be used to check why a particular axis is not performing as expected.
A crucial issue is to find the healthy values (reference) for each motion group. This, in fact, is not constant but varies with temperature. A solution to obtain a robust reference value is presented in this thesis.
Lastly, the condition monitoring task has been fulfilled with some machine learning techniques as well. A classifier is trained to estimate the time elapsed from last maintenance of a motion group. This can be then compared with the actual time from last maintenance to detect if the component is ageing worse than it should.
To build the classifier, data have been acquired and elaborated. The final classifier is a voting classifier that shows some interesting robustness properties. To decide, it makes three sub-classifiers vote and chooses the majority.
Also, a graphical user interface has been created with the machine learning approach. This can be added to the human-machine interface panel.
Finally, a thorough comparison between the two approaches is presented.

Tipologia del documento

Tesi di laurea
(Laurea magistrale)

Autore della tesi

D'Antuono, Damiano

Relatore della tesi

Correlatore della tesi

Scuola

Corso di studio

Ordinamento Cds

DM270

Parole chiave

condition monitoring,torque,automatic machine,machine learning,data analysis,statistical analysis,PLC,maintenance,diagnosis,control,data science,python

Data di discussione della Tesi

19 Dicembre 2019

URI

## Altri metadati

Tipologia del documento

Tesi di laurea
(NON SPECIFICATO)

Autore della tesi

D'Antuono, Damiano

Relatore della tesi

Correlatore della tesi

Scuola

Corso di studio

Ordinamento Cds

DM270

Parole chiave

condition monitoring,torque,automatic machine,machine learning,data analysis,statistical analysis,PLC,maintenance,diagnosis,control,data science,python

Data di discussione della Tesi

19 Dicembre 2019

URI

Gestione del documento: