DALMINE
Overview
Date/time interval
Syllabus
Course Objectives
Students will acquire advanced knowledge of criteria and methodologies that govern the analysis of an industrial system, being able to evaluate its sustainability according to various perspectives (e.g., economic, environmental).
At the end of the course, students will be able to:
• Understand what a sustainable industrial system is.
• Explain the concepts of Product-Service Systems (PSS), Total Cost of Ownership (TCO), Causal Loop Diagrams (CLD), and System Dynamics (SD) in the manufacturing domain.
• Describe the relationship that exists between PSS, Maintenance, and TCO.
• Select the Key Performance Indicators (KPI) that can be used to evaluate the economic and environmental sustainability of an industrial system.
• Identify the interactions between actors in complex systems and illustrate them using CLD.
• Develop an SD simulation model in AnyLogic starting from the content of a CLD.
• Assess the economic and environmental sustainability of a complex industrial system using SD simulation.
• Define the setting of condition-based maintenance solutions.
• Understand what a sensor is, and which types of sensors can be used to develop-condition based maintenance solutions.
• Understand the concepts behind signals, filtering and signal processing tools.
• Apply the signal processing methods to detect faults in machine components.
• Analyze the health status data of machine components and assess their health status using MATLAB.
Course Prerequisites
Basics of Operations Management, Data analysis, Simulation, Industrial Asset Management.
Basics of control system (analysis in the frequency domain and filters)
Teaching Methods
The course is structured into theoretical lectures (24 hours) and laboratory applications (24 hours) aimed at improving the students’ technical and communication skills.
Professors will:
• Present the main theoretical concepts (PSS, Maintenance, TCO, simulation) in theory lectures using PowerPoint slides and additional resources (e.g., scientific papers, videos).
• Present examples of CLD and SD modeling through practical exercises (e.g., CLD) and tutorials (e.g., System Dynamics in AnyLogic, data analysis in MATLAB) developed in class with the students.
• Present the exam project providing information on the expected results and achievements to be satisfied.
• Support students in the development of the project through feedback provisions throughout the course.
• Present the concepts of condition-based maintenance.
• Describe the main types of sensors for condition-based maintenance.
• Present signal processing tools to process data acquired from sensors.
• Present signal processing tools to develop condition-based maintenance solutions.
• Present tutorial lessons on MATLAB programming.
Students will:
• Listen, take notes, and actively participate during theory lectures through questions and discussions.
• Practice the theory concepts explained during theory lectures in practical sessions where singularly or in groups will be asked to solve small problems provided by professors (e.g., draw a CLD, create a small SD model, analyze data in MATLAB).
• Autonomously create groups for the development of a case study aimed at evaluating the economic and environmental sustainability of an industrial system that will be provided during the course that will be used for their examination.
• Ask for meetings with professors to receive feedback on their project work.
• Develop a MATLAB script to develop a condition-based maintenance algorithm.
Assessment Methods
The assessment will be based on a case study presented during the course. The aim of the case study will be to evaluate the economic and environmental sustainability of an industrial system. To demonstrate to have achieved the requirements for passing the course, students will have to:
• Work in groups (min 1 student – max 3 students) to develop the case study.
• Draw a CLD to describe the relationships between the components of the industrial system, with the aim of highlighting connections influencing the economic and environmental sustainability of the system.
• Use MATLAB to analyze a dataset connected to the case study to infer the health status of machine components.
• Produce an SD simulation model in AnyLogic based on the CLD previously developed and the results of the MATLAB analysis. The aim of the SD will be to numerically evaluate the economic and environmental sustainability of the system based on the logical connection identified through the CLD and the results of the data analysis carried out in MATLAB.
• Write a report describing the logical assumptions used to develop the case study. In the report, students will discuss the CLD they have drawn, as well as its content. Additionally, a discussion on the procedure used for the data analysis in MATLAB and the results of the SD simulation will be required.
• Produce a PowerPoint presentation that will be used for the discussion of the case study development and results. The discussion will last around 30 minutes per group, including clarification questions from professors.
As for the Simulation module, the use of artificial intelligence (in any form) is not, in general, prohibited, but it must be declared and documented transparently — for example, by attaching conversations with ChatGPT — explaining and justifying the reasons for its use.
For instance, AI use is allowed for:
- reasoning about the contextual scenario of the case study,
- clarifications on simulation model issues (e.g., errors that block the simulation).
However, the use of artificial intelligence is strictly prohibited for:
- the logical modeling of the case study, i.e., the creation of the CLD (Causal Loop Diagram),
- the identification of relationships between variables in the CLD.
Any violation will result in the project being annulled.
The examination procedures for non-attending students are the same as those for attending students.
Non-attending students are however invited to contact the lecturer to assess any supplementary materials.
Contents
Starting from the presentation of the fundamental ideas at the basis of the Product-Service Systems (PSS) business model, the course introduces the principles of sustainability according to the Triple Bottom Line (TBL) – i.e., economic, social, and environmental sustainability. The TBL will be then linked to various areas of the industrial systems (e.g., production and/or service processes) aimed at the creation of value for the industrial system, trying to understand how some of these affect the sustainability of the industrial system operations. Maintenance basics concepts will be addressed, and methodologies for the evaluation of the economic sustainability of a PSS offering will be explained (e.g., Total Cost of Ownership). Then, the course will address the evaluation of the economic and environmental sustainability of a PSS offering through means of static and dynamic representation using Causal Loop Diagrams (CLD), and System Dynamics (SD) simulation. Examples of the usage of these means of analysis will be provided using case studies and scientific literature.
Regarding the control systems module, the course will introduce the concepts of condition-based maintenance, consisting in algorithms that process the machine data acquired from sensors. First, the description of several types of sensors for condition-based maintenance will be discussed. Then, the concepts of continuous-time and digital signals are introduced. The time-domain and frequency-domain representations of a signal is described. The course then introduced several signal-processing tools, like filtering, Fourier transform, demodulation and other tools. Finally, signal processing tools will be used to describe several methods to perform condition-based maintenance. Applicative case studies will be also presented. The course will exemplify all the content with the software MATLAB. Tutorial lessons about MATLAB programming are planned.
Online Resources
More information
Would the course be taught in dual or distance-based lecturing modes, modifications might be carried out with respect to the syllabus, to make the course and the exams accessible under the new lecturing modes.