Morandi, David

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David Morandi

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2020 - 20223

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  • Vorschaubild
    Publikation
    On the track to application architectures in public transport service companies
    (MDPI, 2022) Jüngling, Stephan; Fetai, Ilir; Rogger, André; Morandi, David; Peraic, Martin
    There are quite some machine learning (ML) models, frameworks, AI-based services or products from different IT solution providers available, which can be used as building blocks to embed and use in IT solution architectures of companies. However, the path from initial prototypical proof of concept solutions until the deployment of proven systems into the operational environment remains a major challenge. The potential of AI-based software components using ML or knowledge engineering (KE) is huge and the majority of small to medium enterprises are still unsure whether their internal developer teams should be extended by additional ML or KE skills to enrich their IT solution architectures with novel AI-based components where appropriate. How can enterprises manage the change and visualize the current state and foreseeable road-map? In the current paper, we propose an AI system landscape for the public transport sector, which is based on existing AI-domains and AI-categories defined by different technical reports of the European Commission. We collect use-cases from three different enterprises in the transportation sector and visualize them on the proposed domain specific AI-landscape. We provide some insights into different maturity levels of different AI-based components and how the different ML and KE based components can be embedded into an AI-based software development life-cycle (SDLC). We visualize, how the AI-based IT-solution architecture evolved over the last decades with respect to coupling and decoupling of layers and tiers in the overall Enterprise Architecture.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Anomaly detection in railway infrastructure
    (2021) Morandi, David; Jüngling, Stephan
    In order to keep complex railway systems fail-safe, sophisticated maintenance of the rolling stock and infrastructure are most essential. Although AI-based predictive maintenance systems exist in many different industries, there is still quite large potential for different application scenarios. The current research shows such an example, where machine learning can be applied to detect anomalies in the pantograph-catenary system by using a simple convolutional neural network that is able to detect arc ignitions during train operation. The paper provides some insights into the process of the system development life cycle. Starting from the initial idea to use machine learning for anomaly detection, over the system design of a prototype and the training of the Keras-based machine-learning model, up until the evaluation of the conducted experiments. The arcVision system prototype provides valuable insights into how a predictive maintenance process could be established by combining the results from the machine-learning model with rules and insights from manual inspections.
    04B - Beitrag Konferenzschrift
  • Kein Vorschaubild vorhanden
    Publikation
    Anomaly detection in railway infrastructure
    (Hochschule für Wirtschaft FHNW, 2020) Morandi, David; Jüngling, Stephan
    This thesis elaborates the topic of artificial intelligence used for anomaly detection in railway infrastructure. In Switzerland, railways are the backbone of the public transport system and an important factor in the economy and society. Therefore, it is important to detect unwanted anomalies in railway infrastructure as fast as possible and before they result in an incident, whereby even a minor interruption can evolve into a major disturbance. Some of the challenges arising from this field can be met with artificial intelligence, especially with machine learning techniques and knowledge engineering. Railway infrastructures offer a wide range of potential in anomaly detection, since they are complex systems. Especially sub-systems, which are exposed to forces (e.g. acceleration or deceleration, rotating or moving) which can result in material wear and maintenance effort, provide promising use cases for anomaly detection. During the research, one particular problem was identified: The arc ignition in the pantograph-catenary system during train operation. Frequent arc ignition will accelerate attrition, respectively, the faster loss of material results in more frequent maintenance or malfunctioning, which eventually leads to a higher idle time or an interruption in operations. The literature review has shown that little effort was expended to solve the problem of arc ignition detection in the pantograph-catenary system....
    11 - Studentische Arbeit