ECTS - University of Pisa
The student will learn about
The student will be assessed on his/her demonstrated ability to discuss the main course contents using the appropriate terminology. During the oral exam the student must be able to demonstrate his/her knowledge of the concepts developed in the course.
The student who successfully completes the course is able to leverage the acquired knowledge, complemented by hands-on activities developed during the course, in order to recognize the characteristics of the basic components, both computing and networking ones, of an industrial control system.
Verification of skills occurs through an in-depth discussion during the oral exam.
Verification of behaviors occurs through an in-depth discussion during the oral exam.
Basic knowledge of computer systems
In classroom face-to-face lectures, with use of slides.
Course web platform used for sharing teaching material, and communication between the lecturer and the student.
Introduction to industrial communication systems. Industrial IoT: evolution and challenges.
Foundations of computer networks. Physical layer transmission; Link layer and local area networks. Ethernet LANs. Wireless networks: LANs, PANs. Internet architecture and protocols: IP and TCP/UDP protocols.
Today’s industrial networks: types of traffic, performance requirements. Fieldbus technologies, real-time Ethernet, industrial wireless networks. Industrial IoT: network interoperability: IPv6 over low power and lossy networks. Low-power Wide Area Networks.
Foundations of distributed computing and middleware services. Application layer protocols, data encoding & representation. Service Oriented Architectures, Web services. Cloud/edge computing principles and services. Industrial IoT: service and platform interoperability. Web of Things, protocols and (cloud-based) platforms.
Industrial Process Control System: Machine Level (PLC); Plant Level (DCS, SCADA); main components, configuration, architectures , dimensioning criteria. PLC programming basics; addressing, programming languages Ladder (LAD), Function Block Diagram (FBD), Graph. Introduction to CFC programming language; objects, faceplates.
Quality Control System: web scanner, sensors; operating principles. Machine Direction (MD) controls and Cross-Machine direction (CD) controls.
The Cross-Industry Standard Process for Data Mining (CRISP-DM). Data selection and data preparation, categorisation, and prediction models. Supervised and unsupervised learning. Algorithms: the basic methods: Inferring rudimentary rules, Decision trees, Rule induction and association rules, Regression and clustering models, Neural networks.
Implementations: real machine learning schemes and prognostics applications. Verification and validation of models. Credibility. Enhancing the analysis: ensemble modelling.
Recorded lessons will be available through the MS Teams online platform.
The exam is taken orally.
The student is asked to present a personal script related to Industrial Process Control Systems, which consists of:
1. an application of the tools learned in the course assigned by the teachers.
2. a technical study based on a state-of-art survey paper provided by the teachers.
The student is then asked to answer oral questions spanning the course contents to demonstrate his/her knowledge of the concepts developed.
