阿爾斯通公司

Automated inspection of the welding process is essential for ensuring consistent quality and high operational reliability. While the opportunities offered by the recent advances in artificial intelligence have allowed for good progress in automated process monitoring, most proposals rely on static image analysis and overlook the temporal dynamics inherent to welding operations. In this work, we introduce a sequential dataset that we designed to capture four distinct welding process states: no welding, welding stable, welding deviated, welding unstable. This dataset differs from the state-of-the-art datasets by its temporal structure and the active vision setup, which enables the study of the dynamic behavior of the process. The introduced temporal dimension is essential for capturing the complex patterns associated with welding defects, particularly in scenarios involving weaving motions. Additionally, we evaluate and compare different state-of-the-art sequential data analysis deep learning models. The study carried out aims to assess the capabilities of these models to detect process instabilities from video sequences in real time and to establish a baseline for future research in intelligent welding process monitoring. Upon this benchmark, we provide recommendations on the most suitable deep learning models for similar applications involving sequential image analysis of the welding process. The obtained results demonstrated that the proposed dataset provides informative clues about the spatial and temporal state of the process. Notably, the MC3 model was capable of achieving the best compromise between performance and computational efficiency, classifying the state of the process at an accuracy of 99.12% with a relatively small latency.

Artificial Intelligence (AI) is currently only applied to non-safety critical applications due to the strict standards and regulations for railway industries. We propose to review the three main fields necessary to increase trust in data science and AI algorithms and reach compliance: robustness, Operational Design Domain (ODD), and explainability. Robustness is the ability of an AI system to maintain its level of performance under any circumstances (ISO24029). ODDs allow the explicit definition of operating conditions under which a system is intended to operate, according to the recently published DIN DKE SPEC 99004. Explainability is the property of an AI system to express important factors influencing the AI system results in a way that humans can understand. Those 3 domains of research are already well investigated by nonrailway actors, with algorithms and methods ready to use for railway applications. A system view is necessary to ensure all trustworthy requirements interact continuously in a safe MLOps environment thereby fostering acceptance from regulators, operators and the public. Beyond safeguarding safety-critical applications, we aim to show that fostering deep trust in AI, as now required by regulatory frameworks worldwide, will unlock its full potential and transform the pace of adoption across mission-critical domains.

The rapid digitisation of critical infrastructure has made traditional fragmented risk assessment practices increasingly challenging to scale. For global industrial leaders managing hundreds of diverse projects, there is a real need for a unified methodology that ensures technical rigour, cross-project reproducibility, and scalability. This paper introduces the Advanced Risk Assessment Methodology for Industrial Systems (ARAMIS), an innovative framework developed through a strategic partnership between Airbus Protect and Alstom. ARAMIS merges the structured, requirement-driven security levels of ISA/IEC 62443 with the scenario-based approach of Expression des Besoins et Identification des Objectifs de Sécurité Risk Manager (EBIOS RM). The paper details the five-module structure of ARAMIS, its unique multilayered modelling of operational scenarios and its algorithmic approach to calculating security levels target (SL-T). Finally, it discusses the implementation of the methodology within the Fence risk management tool to ensure seamless reproducibility and knowledge capitalisation across global project portfolios. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

Dual-mode wireless devices that implement Bluetooth and Wi-Fi in the same physical layer platform and share a single 2.4 GHz antenna must manage the mutual interference and power consumption that arise from concurrent operation of two radio systems in overlapping spectrum. The coexistence challenge encompasses both the RF interference dimension, where simultaneous Bluetooth and Wi-Fi transmissions in the 2.4 GHz ISM band produce adjacent-channel and cochannel collisions that degrade throughput and packet error rate for both protocols, and the power consumption dimension, where the activation states of the Bluetooth and Wi-Fi transceivers, power amplifiers, and baseband processors interact to produce aggregate device power profiles that determine battery life in portable IoT and wearable applications. This paper presents a system-level power behavior model for Bluetooth and Wi-Fi coexistence in dual-mode wireless devices, developed from current profiling measurements of the transmit, receive, and idle current states of the Bluetooth 5.3 and Wi-Fi 6 radio subsystems under a range of coexistence configurations including time-division multiplexing, adaptive frequency hopping, and packet traffic arbitration. The model characterizes the power contribution of each protocol as a function of its duty cycle, packet size, data rate, and coexistence mode, and aggregates the per-protocol contributions into a system-level power model that predicts the average current consumption of the dual-mode device under mixed Bluetooth and Wi-Fi traffic loads. The model is assessed through measured current profiles from a commercial Bluetooth 5.3 and Wi-Fi 6 combo module under controlled traffic conditions and demonstrates a mean prediction error of 3.2 percent across the evaluated operating scenarios. Applications of the model to battery life estimation for IoT wearable devices and to coexistence mode selection for optimal power efficiency are presented.