SMART – Smart Predictive Maintenance for the Pharmaceutical Industry
Objective
The main objective of this project is to improve the productivity of two packaging lines within the SweOps Steriles function, as measured by Overall Equipment Effectiveness (OEE) and competence in line staffing. We aim to achieve the objective by enabling next-best-action decision support for front-line operators.
Background
Modern pharmaceutical packaging lines are complex systems with multiple intricate physical and digital components. Operators gain domain expertise through extended exposure and interaction with the systems. They see, touch, and listen to the operating parts of the system. With time, they develop deep procedural knowledge and reach the level where they can predict when the physical systems need maintenance. How do they do this? This is the basic question of the project SMART: Smart Predictive Maintenance for the Pharmaceutical Industry, a collaboration between AstraZeneca and KTH.
Our approach deploys three pillars: 1) sensor networks in manufacturing, 2) machine learning predictive models, and 3) interactive immersive and contextual visualizations. We will observe and interview expert operators to acquire their procedural knowledge and focus on the sensing and machine learning tools to produce a rich sensor-based predictive model that we visualize peripherally in the plant and immersively to the operators. The project aims to enhance the operators’ abilities to perform predictive maintenance and expedite the transfer of these skills to novice operators via novel digital tools.
About the Digital Futures Industrial Postdocs
Recruitment ongoing
Main supervisor
Lihui Wang, Professor and Chair of Sustainable Manufacturing at KTH
Co-supervisors
Jan Kronqvist, Assistant Professor at KTH
Ming Xiao, Associate Professor, Division of ISE at KTH EECS
Mario Romero, Associate professor at KTH EECS School, Division of Computational Science and Technology
Watch the recorded presentation at the Digitalize in Stockholm 2023 event:
Contacts
Lihui Wang
Professor and Chair of Sustainable Manufacturing at KTH, Main supervisor: SMART – Smart Predictive Maintenance for the Pharmaceutical Industry, Co-PI: Towards Safe Smart Construction - Algorithms, Digital Twins and Infrastructures, Former Co-supervisor: Intelligent wireless communications and high-accuracy positioning systems, Digital Futures Faculty
lihuiw@kth.se
Jan Kronqvist
Assistant Professor at KTH, Co-supervisor: SMART – Smart Predictive Maintenance for the Pharmaceutical Industry, Former Co-PI: Autonomous coordination and control of smart converters for sustainable power systems, Digital Futures Faculty
jankr@kth.se
Ming Xiao
Associate Professor, Division of ISE at KTH EECS, Working group Learn, Co-supervisor: SMART – Smart Predictive Maintenance for the Pharmaceutical Industry, Co-supervisor: Fast Distributed Learning based on Adaptive Gradient Coding with Convergence Guarantees, Former Main supervisor: Intelligent wireless communications and high-accuracy positioning systems, Digital Futures Faculty
+46 8 790 65 77mingx@kth.se
Mario Romero
Associate Professor, Division of Computational Science and Technology at KTH EECS School, Co-PI: Platform for Smart People (PSP): Understanding Inclusion Challenges to Design and Develop an Independent Living Platform in a smart Society for and with people with autism, Co-supervisor: SMART – Smart Predictive Maintenance for the Pharmaceutical Industry, Digital Futures Faculty
marior@kth.se