BIG DATA FOR SMART MANUFACTURING

ING-INF/05 - 6 CFU - 1° Semester

Teaching Staff

SALVATORE CAVALIERI
Email: salvatore.cavalieri@unict.it
Office: Città Universitaria (CU), Edificio 3 Polifunzionale, V piano
Phone: 0957382362
Office Hours: Lunedì 9-13 e Venerdì 9-13


Learning Objectives

Knowledge and understanding. On completion of the course, the student shall 1) know the basic principles of the fourth industrial revolution “Industry 4.0” and the basic principles of predictive maintenance, and 2) understand methodologies and techniques used in industries to realise the “Failure Prediction and Predictive Maintenance” .

Applying knowledge and understanding. On completion of the course, the student will be able to select the appropriate technological solutions in predictive maintenance.

Making judgements. On completion of the course, the student will be able to choose a suitable data science model for each of the subjects treated inside the course.

Communication skills. On completion of the course, the student can communicate his conclusions and recommendations about data science applications in smart manufacturing with the argumentation of the knowledge and rationale underpinning these, to both specialist and non-specialist audiences clearly and unambiguously.

Learning skills (Capacità di apprendimento). On completion, the student will be able to continue to study in a manner that may be largely selfdirected or autonomous.


Course Structure

The aim of the course is to provide an in-depth introduction to i) the basic principles of the fourth industrial revolution “Industry 4.0”; ii) basic principles on management problems of a production plants, with particular enphasis on failure management and predictive maintenance; iii) the most important methodologies and techniques used in industries to realise the “Failure Prediction and Predictive Maintenance”



Detailed Course Content

Smart Manufacturing in Industry 4.0. Introduction on the fourth industrial revolution. Main problems in the management of a production plant. Data Science applications to smart manufacturing. Basic principles on failure detection and predictive maintenance.

Failure Detection and Predictive Maintenance. Data Acquisition. Data Processing: signal processing, feature extraction, and feature selection. Data labelling techniques. Machine learning techniques for failure type detection and predictive maintenance. Performance Evaluation. Real case studies using R language.



Textbook Information

[1] - Patrick Jahnke, "Machine Learning Approaches for Failure Type Detection and Predictive Maintenance", Master Thesis, June 19, 2015. Available online.

[2] - Handouts distributed through the Studium platform.




Open in PDF format Versione in italiano