The Intelligent Maintenance Conference is an international event on new diagnostic and intelligent monitoring methods for industrial systems! Our objective is to be a place to share fruitful discussions on predictive maintenance between experts from the industry and the academic world. Renowned speakers from varied backgrounds, including railway, healthcare, energy, and chemical industries, academics, and others, will be present.
The registration for the conference is not open yet.
Confirmed talks
Biologically-inspired Design and Control of Robots for Agile Flight and Locomotion
Prof. Dario Floreano
Laboratory of Intelligent Systems, EPFL
Animals display compliance, agility, and adaptability to a range of environmental situations that are still challenging for today's robots. In this talk I will describe the translation of biological principles of collision resilience, variable stiffness, morphological adaptation and multi-agent coordination into robots that could be deployed for inspection missions in challenging situations.
Responsible AI Development and Legal Compliance: Navigating the New Landscape of AI Regulation
Dr. Rialda Spahic
Sr Analyst Digital SW Eng, Responsible AI, Equinor
Responsible AI algorithms are crucial for organizations to ensure AI adoption is aligned with ethical standards and societal values, while enhancing trust and emphasizing safety in AI systems. With the upcoming EU AI Act, organizations are facing new legal obligations centered around risk management, data governance, bias mitigation, and accountability, with an emphasis on high-risk use cases. Therefore, it is crucial to examine the best practices and guidelines on designing and adopting responsible AI algorithms through ethical considerations, technical best practices, and regulatory compliance.
Leveraging AI and Non-Intrusive Monitoring as Intelligent Maintenance Enablers for Electromechanical Systems
Dr. Javier Diaz-Rozo
Aingura IIoT
This presentation outlines the integration of novel non-intrusive monitoring techniques with cutting-edge probabilistic machine learning advancements, establishing a robust and scalable intelligent maintenance approach for electromechanical systems. Through the analysis of two real-world case studies involving high-capacity water pumps and airport baggage inspection systems, the efficacy of this approach is demonstrated in not only detecting and predicting failures but also providing comprehensive diagnostic capabilities through explainability methodologies. Thus, a holistic methodology is showcased, from data analysis to actionable insight generation.
AI for Human Empowerment
Prof. Mihaela van der Schaar
University of Cambridge
Potential Impact of Causal Inference on Railway Predictive Maintenance
Alexandre Trilla
Senior Data Scientist Alstom
The technical field of Predictive Maintenance leverages data science to maximize the availability of industrial assets through their degradation models. In this sense, Artificial Intelligence and Machine Learning have proven to be effective techniques for extracting latent patterns from the available data. However, the observed data may sometimes be incomplete and misleading because real-world condition monitoring records rarely exhibit all of the operating regimes and wear rates of the machines. Therefore, to increase the reliability of the Predictive Maintenance solutions in a scenario where the behavior patterns change, Causal Inference emerges as an avenue of improvement by elegantly blending data-driven and expert-driven criteria to ultimately help maintainers make better informed decisions to manage their assets. In this talk, we review the potential of Causal Inference for Predictive Maintenance through two specific case-studies.
Revolutionizing Anomaly Detection in Industry: The Convergence of Large Language Models for Semantic Insights
Han Sun
EPFL IMOS
Anomaly detection is a crucial task across different domains and data types. However, existing anomaly detection models are often designed for specific domains and modalities. Recently, the advent of Large Language Models (LLMs) has ushered in a new era of possibilities across various domains, including industrial operations. In this talk, we will delve into the potential of LLMs for enhancing industrial anomaly detection, highlighting their promising performance in generic anomaly detection and understanding. We will introduce our recent research work on Diffusion Models for anomaly generation with text guidance. Empowered by LLMs, we are able to generate authentic, diverse and semantically meaningful anomaly samples, and effectively improves the performance of downstream anomaly inspection tasks.
Topic will be available soon
Ralph Fasler
Vice President Manufacturing, Ypsomed AG
Topic will be available soon
Indranil Roychoudhury1, Jose Celaya2
1Principal Scientist, Machine Learning and AI, SLB 2Principal Scientist and Manager, SLB
Topic will be available soon
Vincent Cheriere
Airbus
Topic will be available soon
Philippe de Laharpe
SNCF
Topic will be available soon
Vinay Sharma
EPFL IMOS
The full program will be available soon.
If you would like to become a sponsor please contact us using the form below or by .
VENUE
Le Polydôme
EPFL, CH-1015 Lausanne
Our team
Prof. Olga Fink
Christine Gabriel
Dr. Florent Forest
Vinay Sharma
Sergei Garmaev
Keivan Faghih Niresi
Ismail Nejjar
Mengjie Zhao
Raffael Theiler
Han Sun
Leandro Von Krannichfeldt