BIG DATA FOR SMART MANUFACTURING

Knowledge and understanding. On completion of the course, the student shall 1) know the basic principles of the smart manufacturing according to the novel IC technologies 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.

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 aim of the course is to provide an in-depth introduction to i) the basic principles of the smart manufacturing in the modern industry; 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”

The aim of the course is to provide an in-depth introduction to i) the basic principles of the smart manufacturing; ii) basic principles on management problems of a production plants, with particular enphasis on failure management, 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 distributed through the Studium platform.

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