DATA SCIENCE IN THE FACTORY OF THE FUTURE

Knowledge and Understanding. On completion of the course, the student shall 1) know the basic principles of the smart manufacturing according to the novel Information & Communication Technologies (ICT) adopted in the modern industry, 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 based on Machine Learning and Big Data.

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. On completion, the student will be able to continue to study in a manner that may be largely selfdirected or autonomous.

The course is essentially based on lectures, which include the development of exercises by the teacher on a computer. 

Basics of statistics, machine learning, optimization and linear programming. Knowledge and practice about R-language is also required.

Attendance is not mandatory, but strongly recommended.

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

Smart Manufacturing. 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. Real case studies using Microsoft Azure Machine Learning Platform.

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

[2] - Handouts available in the repository of the course (information about the repository used is given in Studium platform https://studium.unict.it).

[3] - V.Fontama, R.Barga, H.Tok, "Predictive Analytics with Microsoft Azure Machine Learning", Apress.

The exam, aimed at evaluating the student's understanding of the topics of the course and the achievement of the learning objectives, is organized in only one exam. The exam is a mix of theoretical questions (to which the students may be requested to give written answers) and practical questions (in this case, the students are requested  to solve one or more exercises using the languages/platforms used during the course).

To ensure equal opportunities and in compliance with current laws, interested students may request a personal interview in order to plan any compensatory and/or dispensatory measures based on educational objectives and specific needs. Students can also contact the CInAP (Centro per l’integrazione Attiva e Partecipata - Servizi per le Disabilità e/o i DSA) referring teacher within their department.

It is possible to download examples of questions and/or exercises from the repository used during the course. Information about the repository officially used, is given at the beginning of the course and is specified in the studium portal of the course at http://studium.unict.it