9^th Intelligent Maintenance Conference

Utilizing the Power of Augmented Intelligence in Engine Services at KLM

In the dynamic world of aircraft and engine maintenance, predictive analytics and artificial intelligence (AI) are transforming how we manage and optimize our MRO services. This presentation will explore the innovative applications of these technologies at KLM, with specific focus on the KLM Engine Service. We will cover predictive health monitoring, prescriptive analytics, and AI in automation. Predictive health monitoring benefits KLM and its clients by using PROGNOS, which leverages big data and advanced analytics to monitor aircraft systems and engine components. This provides early warnings of potential issues to prevent AOG situations, flight delays, and cancellations, thus improving airworthiness and aircraft availability. In prescriptive analytics, we are designing tools for maintenance planning and resource optimization to improve visit schedules and manage aircraft and engine lease contracts. Phaser, an optimizer recently developed at KLM, generates efficient shop visits and phase-out plans for engines, thereby maximizing utilization and reducing costs. It is effectively overseeing the phase-out of KLM's 737 aircraft, ensuring a seamless and cost-effective transition. We will also cover how GenAI is automating engine income inspections in our shop. Using speech-to-text and a smart voice bot, we streamline administrative tasks, improve data quality, increase mechanics' hands-on time, and reduce non-performance costs.

Deep Learning Image Recognition Models for Bearing Failure Modes

In industrial environments, efficient and reliable diagnosis of bearing failures is critical for predictive maintenance strategies. This talk presents a computer vision deep learning tool designed for the automatic recognition of bearing failure modes, building upon our previously developed framework. The system has been expanded to incorporate state-of-the-art architectures, including ResNet and Vision Transformer (ViT) models, significantly enhancing classification accuracy and robustness on an industrial sourced bearing damage dataset. Beyond model development, the talk will explore the practical challenges of image data management in industrial contexts—such as data labelling, quality control, and integration with maintenance systems. Emphasis will be placed on how scalable data workflows, combined with powerful deep learning models, can transform condition monitoring and failure analysis processes. The session aims to provide both a technical deep dive and practical insights for applying AI solutions effectively in real-world industrial environments.

Abstract will be available soon.

Abstract will be available soon.

Abstract will be available soon.

Enhancing Asset Reliability through Predictive Maintenance and Real-Time Analytics

The predictive maintenance framework implemented at dsm-firmenich ingredients operations seeks to enhance asset reliability by transitioning from reactive interventions to proactive, data-driven decision-making. Utilizing real-time operational data and advanced machine learning algorithms, the framework accurately forecasts the health status of critical equipment, initially targeting vacuum pump systems. These predictive insights facilitate early anomaly detection, promptly alerting engineering/maintenance teams through automated notifications. Additionally, integrated condition monitoring indicators dynamically adjust process parameters to significantly reduce equipment overload risks. This comprehensive approach optimizes maintenance scheduling, minimizes unplanned downtime, extends asset lifespan, and ensures seamless operational continuity.

Sustainability Robotics

Environmental sciences rely heavily on accurate, timely and complete data sets which are often collected manually at significant risks and costs. Robotics and mobile sensor networks can collect data more effectively and with higher spatial-temporal resolution compared to manual methods while benefiting from expanded operational envelopes and added data collection capabilities. In future, robotics and AI will be an indispensable tool for data collection in complex environments, enabling the digitalisation of forests, lakes, off-shore energy systems, cities and the polar environment. However, such future robot solutions will need to operate more flexibly, robustly and efficiently than they do today. This talk will present how animal-inspired robot design methods can integrate adaptive morphologies, functional materials and energy-efficient locomotion principles to enable this new class of environmental robotics. The talk will also include application examples, such as flying robots that can place sensors in forests, aerial-aquatic drones for autonomous water sampling, drones for aerial construction and repair, and impact-resilient drones for safe operations in underground and tunnel systems.

Integrated planning of operations and maintenance of offshore electrical systems on the Norwegian Continental shelf (NCS)

The Norwegian Continental shelf has been a substantial oil and gas production province since the 1970’s. Recently, the area is undergoing a major transition, with the electrification of the existing assets that used to be powered by gas turbine generated energy. The operating model is changing with electrification of the offshore assets, from traditionally, ‘island mode’ of energy production and maintenance of systems. Each, asset has been responsible for providing their own power supply and plan/execute the maintenance of the local systems on their installation. As of today, there are several area electrification solutions (e.g.: Utsira High) emerging where the assets are bundled together via a shared onshore grid power supply or with offshore wind farms/parks. The oil and gas assets are more dependent on a ‘collaboration mode’ of energy supply and demand and they need to coordinate and integrate their maintenance schedules. One oil and gas operator asset is typically defined as the TSO or system responsible for the offshore grid and responsible for coordinating the utilization of the grid energy across the different assets and also create an integrated plan for maintenance of the systems that comprise the offshore grid. The system responsible need to align the maintenance plans across the bundled assets and their local plans. There are emerging tools for developing optimized maintenance schedules, knowing the technical condition and down-time of electric equipment are critical to maintain a good availability of the offshore power grid in an area where several assets share the same onshore power supply. The presentation will introduce the integrated planning of maintenance in such an offshore grid and provide some insights on the software tools that are used to optimize the utilization of the energy supply and for integrated planning of maintenance of the offshore grid.

Edge-Intelligent Sensing for Predictive Maintenance: From Self-Sustaining Nodes to Robotic Dogs

As predictive maintenance shifts from reactive strategies to proactive intelligence, the convergence of novel sensing technologies, including ultra-low-power, neuromorphic, and energy-harvesting sensors, with Edge AI is transforming the field. In this talk, I present an AI-based approach to predictive maintenance, driven by fast, local decision-making and energy-efficient sensing architectures tailored for battery-operated devices as well as robotic platforms. Drawing from our research at ETH Zurich, I will showcase embedded and efficient intelligence across multiple systems, from self-sustaining sensor nodes to autonomous robotic drones and dogs equipped with multimodal perception capabilities (radar, depth, and inertial sensing). These platforms enable robust condition monitoring, anomaly detection and predictive maintenance in dynamic environments, leveraging real-time edge processing to reduce latency, bandwidth, and energy consumption. Finally, I will explore how sensor fusion, neural model optimization, and custom hardware-software co-design unlock scalable, sustainable solutions for the next generation of maintenance in industrial, agricultural, and infrastructure monitoring domains.

Abstract will be available soon.

Abstract will be available soon.

Abstract will be available soon.

Abstract will be available soon.

From Predictive Maintenance to Comprehensive Life-Cycle Management of Track Infrastructure

The evolution from predictive maintenance to comprehensive life-cycle management of track infrastructure is a significant advancement in railway operations. Automated track inspection technologies have revolutionized maintenance strategies for railway infrastructure through precise measurements and data collection. By leveraging cutting-edge sensors and data analytics, potential failures can be predicted, and maintenance schedules optimized, enhancing the reliability and safety of railway operations. Building on these predictive maintenance techniques, the concept of life-cycle management is introduced. This approach not only focuses on immediate maintenance needs but also considers the long-term performance and sustainability of the track infrastructure. Integrating life-cycle management principles aims to extend the lifespan of assets, reduce overall costs, and improve the efficiency of resource allocation. The presentation will also provide a forward-looking perspective on how life-cycle management can be further enhanced with emerging technologies and innovative practices. The transition from predictive maintenance to life-cycle management can transform the way track infrastructure is managed and maintained, ensuring a more sustainable and efficient future for railway systems.

Towards Federated Learning for Predictive Maintenance in Comercial Vehicles

Federated learning is a promising approach for data preserving machine intelligence for predictive maintenance in the vehicle industry. However, industrializing this technology comes with significant technical challenges. I will share insights in our ongoing efforts to implement federated learning and discuss practical and technical challenges involved in building a scalable FL infrastructure, deploying edge-based ML machine learning models for multivariate anomaly detection, and implementing online learning for sensor-time series data. If times permits, I will touch on approaches for uncertainty-aware remaining useful life (RUL) prediction and causal discovery—both crucial for developing robust and interpretable predictive systems.

How Agent Based Modeling helps us keeping the Swiss mobility performance

In the face of increasing demands and a congested railway network, the Swiss railway system is confronted with significant challenges, including a growing backlog of maintenance and infrastructure work. This talk explores how Agent Based Modeling (ABM) and reinforcement learning can be leveraged to enhance scenario creation and evaluation, providing valuable insights into the complexities of network management. By simulating various operational scenarios, we can identify optimal strategies to address current pressures and future demands. Our approach aims to ensure a stable and robust railway system, fulfilling our commitment to high mobility performance well into the second half of the century. The talk will discuss the integration of advanced modeling techniques into our planning processes, enabling us to navigate the complexities of modern railway management effectively.

Abstract will be available soon.