AI for Prognostics and Health Management of your industrial assets
You are here
The EluciDATA Lab of Sirris is organizing a seminar on the application of AI-enhanced prognostic and health management (PHM). The ultimate aim of PHM is to provide methods and tools to design optimal maintenance policies for a specific asset under its distinct operating and degradation conditions, achieving a high availability at minimal costs. The seminar offers a broad overview of advanced techniques for PHM, via keynote presentations by lead companies and state-of-the-art research results illustrated with industrial use cases in different sectors.
To address the increasing demand in functionality and quality, the design of industrial assets is continuously evolving towards more complex systems, often featuring multiple interacting subsystems and components. These assets are often operating in complex industrial environments, influenced by a variety of different factors (e.g., the ambient environmental conditions, the interaction with other assets in the fleet) in interaction with a human end user (e.g. the operator). Due to the stringent requirements on system reliability in which system failures can have serious consequences, PHM techniques play a fundamental role to assess the on-going health of a product or system, provide advance warnings of failure, and provide information to improve the design and qualification of fielded and future products. Companies able to fully leverage the capabilities of PHM can achieve better actionable insights and significant operational efficiency improvements.
During this workshop, you will:
- learn about the opportunities of PHM in industrial contexts;
- be able to discuss with peers the challenges related to PHM of industrial assets;
- get inspired via real-world use cases;
- have the opportunity to get in touch and ask your questions to experts;
- network with peers.
13:00 Welcome coffee
13:30: Introduction and setting the context: the opportunities and challenges of prognostic and health management by Alessandro Murgia (The Data and AI Competence Lab of Sirris)
13:45: Industrial Keynote: DataMiner: towards a proactive and autonomous monitoring solution for ICT media and broadband platforms by Veerle Ledoux (Skyline)
As the global broadcast, media and service provider industry is going through fundamental transformations, the complexity of operational systems is quickly growing beyond the point of human comprehension. Add to this, ongoing evolutions such as the increased dynamics of new operational systems, the proliferation and elasticity of services running across them, and the ever-increasing pressure to reduce operational costs while increasing quality of service, and it is clear that the management of the underlying technology infrastructure requires innovative new paradigms to effectively tackle the many operational challenges. As in many other industries, the answer is sought in AI-enhanced solutions to unlock more autonomous and proactive management.
14:05: Industrial Keynote: Overall equipment effectiveness improvement in the steel industry by Steven Raekelboom (The Grain)
With weights up to 30 ton, the production of steel coils certainly qualifies as heavy industry. However massive the end product, steel production requires high precision tweaking and tuning, R&D and operator expertise to achieve the expected steel quality. Steel producers therefore gather an enormous amount of data throughout the whole production process and use it to increase their overall equipment effectiveness (OEE). They focus primarily on quality, safety and reduction of unplanned maintenance. In this session, the key elements of the business case of several OEE projects will be explained.
14:25: An overview of the current state-of-the-art in Prognostic and Health Management based on several real-world industrial use cases:
- Advanced methods for tool wear monitoring in an industrial setting by Robbert Verbeke (The Data and AI Competence Lab of Sirris) and Tom Jacobs (the Precision Manufacturing Group of Sirris)
- Fault detection using data-efficient digital twins by Arash Heidari (UGent)
15:05: Coffee break;
- Anomaly detection for multiple assets: exploiting fleet information by Vincent Vercruyssen (KU Leuven) and Dandan Peng (KU Leuven).
- A framework for benchmarking and validation of PHM methods by Robbert Verbeke (The Data and AI Competence Lab of Sirris)
16:05: Concluding remarks by Elena Tsiporkova (The Data and AI Competence Lab of Sirris)
16:20: Networking reception
90 EUR (exclusive of VAT)
Thanks to the support of VLAIO, technology companies located in Flanders only pay 30% of the above-mentioned price (so 27 EUR (exclusive of VAT)).
- Invoices will be sent after the event. You can consult our general conditions on our website.
- If you are unable to attend, you can be replaced by a colleague. Please notify us by email to firstname.lastname@example.org of the name of your colleague.
- Cancellations must be made by email to email@example.com. You can cancel your participation free of charge up to 3 working days before the event. In case of cancellation after this date or non-participance without cancellation, you will be invoiced for the full participation fee.