Digitalisering genomsyrar fler och fler delar av dagens samhälle. Ett område där man nyligen börjat bedriva forskning är digitalt underhåll. Detta område är särskilt intressant när det gäller komponenter med lång livslängd som i exempelvis gruv- och transportindustrin då tiden för driftstopp av enheten kan minskas och rätt typ av underhåll kan ske genom rätt typ av diagnos. Detta leder till sänkta kostnader för företagen som implementerar digitalt underhåll. Fördelar med digitalt underhåll finns beskrivna i litteratur, men de viktiga faktorerna som behöver bearbetas för att kunna implementera det finns det i dagsläget inte mycket forskning kring. Syftet med denna studie var att utforma ett ramverk för vilka viktiga faktorer som bör tas i beaktning och dess samband då en organisation vill implementera digitalt underhåll, samt identifiera det resultat som uppstår då övergången till digitalt underhåll har genomförts. För att uppnå syftet genomfördes en abduktiv fallstudie kring digitalisering av järnvägsunderhåll på Sweco Rail AB, med fokus på deras största kund Trafikverket. En litteraturstudie genomfördes för att skapa en bild av problemet och data samlades in genom insamling av dokumenterat material och 26 intervjuer i tre faser; explorativa, semistrukturerade och uppföljande. Den data som samlades in analyserades sedan genom komparativ analys och tematisk kodning för att bidra till studiens resultat. Slutligen validerades resultatet med hjälp av experter inom området. Resultatet av studien var ett ramverk där fem faktorer som identifierats som viktiga att bearbeta för att implementera digitalt underhåll presenteras, deras samband samt det resultat som uppstår av att implementera det digitala underhållet visas. Ramverket innehåller faktorerna Digital teknik, Organisatorisk utveckling, Förändrade arbetssätt, Regelverk och Informationssäkerhet. I varje faktor identifierades även underkategorier. Ramverket visade även vad resultatet blir av att implementera digitalt underhåll.  Det praktiska bidraget är att ramverket hjälper organisationer att fokusera på de delarna som identifierats som viktiga. Detta för att implementeringen av digitalt underhåll ska bli så lyckad som möjligt, samtidigt som det visar de resultat som går att uppnå som en tydlig målbild. Det teoretiska bidraget är en fördjupad förståelse för området digitalt underhåll. Framtida forskning kan undersöka faktorerna hos företag som redan har genomfört en implementering av digitalt underhåll för att på så sätt verifiera resultaten från denna studie.

Railway has the potential to become one of the most sustainable mediums for passenger and freight transport. This is possible by continuous updates to the asset management regime supporting Prognostics and Health Management (PHM). Railway tracks and catenaries are linear assets, and their length plays a vital role in maintenance. Railway catenary does not present many failures as compared to the rail track, but the failures that occur do not give enough opportunity for quick recovery. These failures cause extensive time delays disrupting railways operations. Such situations can be handled better by updating the maintenance approach. The domain of maintenance explores possible tools, techniques, and technologies to retain and restore the systems. PHM is dependent on data acquisition and analytics to predict the future state of a system with the least possible divergence. In the case of railway catenary and many other domains, this new technology of data acquisition is Light Detection And Ranging (LiDAR) device-based spatial point cloud collection. Current methods of catenary inspection depend on contact-based methods of inspection of railway catenary and read signals from the pantograph and contact wire while ignoring the rest of the wires and surroundings. Locomotive-mounted LiDAR devices support the collection of spatial data in the form of point-cloud from all the surrounding equipment and environment. This point cloud data holds a large amount of information, waiting for algorithms and technologies to harness it. A Digital Twin (DT) is a virtual representation of a physical system or process, achieved through models and simulations and maintains bidirectional communication for progressive enrichment at both ends. A systems digital twin is exposed to all the same conditions virtually. Such a digital twin can be used to provide prognostics by varying factors such as time, malfunction in components of the system, and conditions in which the system operates. Railways is a multistakeholder domain that depends on many organisations to support smooth function. The development of digital twins depends on the understanding of the system, the availability of sensors to read the state and actuators to affect the system’s state. Enabling a digital twin depends on governance restrictions, business requirements and technological competence. A concrete step towards enablement of the digital twin is designing an architecture to accommodate the technical requirements of content management, processing and infrastructure while addressing railway operations' governance and business aspects.The main objective of this work is to develop and provide architecture and a platform for the enablement of a DT solution based on Artificial Intelligence (AI) and digital technologies aimed at PHM of railway catenary system. The main results of this thesis are i) analysis of content management and processing requirements for railway overhead catenary system ii) methodology for catenary point cloud data processing and information representation iii) architecture and infrastructure requirements for enablement of Digital Twin and iv) roadmap for digital twin enablement for PHM of railway overhead catenary system.

Big Data analytics has attracted intense interest recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional, streaming, and nonlinear data are being collected and curated to support decision-making. The detection of faults in these data is an important application in eMaintenance solutions, as it can facilitate maintenance decision-making. Early discovery of system faults may ensure the reliability and safety of industrial systems and reduce the risk of unplanned breakdowns. Complexities in the data, including high dimensionality, fast-flowing data streams, and high nonlinearity, impose stringent challenges on fault detection applications. From the data modelling perspective, high dimensionality may cause the notorious “curse of dimensionality” and lead to deterioration in the accuracy of fault detection algorithms. Fast-flowing data streams require algorithms to give real-time or near real-time responses upon the arrival of new samples. High nonlinearity requires fault detection approaches to have sufficiently expressive power and to avoid overfitting or underfitting problems. Most existing fault detection approaches work in relatively low-dimensional spaces. Theoretical studies on high-dimensional fault detection mainly focus on detecting anomalies on subspace projections. However, these models are either arbitrary in selecting subspaces or computationally intensive. To meet the requirements of fast-flowing data streams, several strategies have been proposed to adapt existing models to an online mode to make them applicable in stream data mining. But few studies have simultaneously tackled the challenges associated with high dimensionality and data streams. Existing nonlinear fault detection approaches cannot provide satisfactory performance in terms of smoothness, effectiveness, robustness and interpretability. New approaches are needed to address this issue. This research develops an Angle-based Subspace Anomaly Detection (ABSAD) approach to fault detection in high-dimensional data. The efficacy of the approach is demonstrated in analytical studies and numerical illustrations. Based on the sliding window strategy, the approach is extended to an online mode to detect faults in high-dimensional data streams. Experiments on synthetic datasets show the online extension can adapt to the time-varying behaviour of the monitored system and, hence, is applicable to dynamic fault detection. To deal with highly nonlinear data, the research proposes an Adaptive Kernel Density-based (Adaptive-KD) anomaly detection approach. Numerical illustrations show the approach’s superiority in terms of smoothness, effectiveness and robustness.

Railways are a main element of a sustainable transport policy in several countries as they are considered a safe, efficient and green mode of transportation. Owing to these advantages, there is a cumulative request for the railway industry to increase the performance, the capacity and the availability in addition to safely transport goods and people at higher speeds. To meet the demand, large adjustment of the infrastructure and improvement of maintenance process are required.  Inspection activities are essential in establishing the required maintenance, and it is periodically required to reduce unexpected failures and to prevent dangerous consequences.  Maintenance of railway catenary systems is a critical task for warranting the safety of electrical railway operation.Usually, the catenary inspection is performed manually by trained personnel. However, as in all human-based inspections characterized by slowness and lack of objectivity, might have a number of crucial disadvantages and potentially lead to dangerous consequences. With the rapid progress of artificial intelligence, it is appropriate for computer vision detection approaches to replace the traditional manual methods during inspections.  In this thesis, a strategy for monitoring the health of catenary wires is developed, which include the various steps needed to detect anomalies in this component. Moreover, a solution for detecting different types of wires in the railway catenary system was implemented, in which a deep learning framework is developed by combining the Convolutional Neural Network (CNN) and the Region Proposal Network (RPN).