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
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
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.
Predictive maintenance for heavy duty vehicles
Prof. Sepideh Pashami
Halmstad University
Predictive maintenance for heavy duty vehicles, like any other equipment, aims at predicting failures by analyzing sensor data. Yet the unique nature of heavy-duty vehicles operating in uncontrolled environment poses novel challenges in effectively detecting symptoms of impending issues. AI has been shown to provide accurate detection and identification of deviations from typical behavior. The question now is that how to proceed with flexible, robust, understandable and scalable solutions. This talk describes the application area and presents implementation of domain adaptation and explainability methods to address some of the existing challenges.
Accelerating Production Ramp-ups with a Single Pane of Glass for Production Data
Mathias Pawlowsky
Planted Foods
Plant-based meat is a rapidly growing industry, driven by the increasing demand from consumers seeking to reduce their animal meat consumption without sacrifice. In this highly competitive market, the speed of product innovation and subsequent production ramp-up is crucial. This talk showcases how Planted combines disparate production data sources into a single pane of glass to enable better and faster decisions during ramp-ups. You will hear how the production team monitors machines and runs root cause analysis on the process data, and how insights from the process data translate to improved control of the machines.
eXplainable Artificial Intelligence (XAI) in Industrial Diagnostic: Interpretable Unsupervised Anomaly Detection
Prof. Gian Antonio Susto
University of Padova
Unsupervised learning approaches have strong applicability in many scenarios, given the unavailability of labeled data in many settings. In particular, anomaly detection approaches have become paramount for ensuring the monitoring of critical systems. This talk delves into eXplainable Artificial Intelligence (XAI) in anomaly detection, with reference to an innovative approach called ‘Interpretable Anomaly Detection with DIFFI: Depth-based Feature Importance of Isolation Forest’. Moreover, in the talk, some applications to industrial use cases will be discussed, and other directions for research in anomaly detection and XAI will be outlined.
Revolutionizing Anomaly Detection in Industry: The Convergence of Large Language Models for Semantic Insights
Han Sun
IMOS, EPFL
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.
A Novel Physics-informed Neural Networks Approach for Efficient Multimodal Mapping and Inversion of Vibrations
Dr. Saeid Hedayatrasa
Flanders Make-MotionS
The vibrational characteristics of structural components contain valuable insights into their inherent mechanical properties and overall health status. Consequently, there's a pressing demand for efficient physics-based inversion algorithms. These algorithms must effectively reconstruct responses at unmeasured locations and/or identify unknown mechanical properties using a sparse set of noisy sensory data. Physics-informed neural networks (PINNs) offer promising solutions by seamlessly integrating the governing physics equations into their framework. Nonetheless, challenges arise due to the complex, multimodal, and multiscale nature of vibrational responses, along with the spectral bias of PINNs. In this talk we present a novel PINNs approach for efficient multimodal mapping of vibrations in a composite laminate and identification of its orthotropic elastic properties, given limited number of noisy simulation data points.
Topic will be available soon
Ralph Fasler
Ypsomed AG
Topic will be available soon
Prof. Ajith Parlikad
University of Cambridge
Topic will be available soon
Jose Celaya
SLB
Topic will be available soon
Vincent Cheriere
Airbus
Topic will be available soon
Vinay Sharma
IMOS, EPFL
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