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        <titl xml:lang="sv">SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance</titl>
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        <titl xml:lang="sv">SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance</titl>
        <parTitl xml:lang="en">SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance</parTitl>
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        <AuthEnty xml:lang="en" affiliation="Department of Computer and Systems Sciences, DSV, Stockholm University">Lindgren, Tony</AuthEnty>
        <AuthEnty xml:lang="sv" affiliation="Institutionen för data- och systemvetenskap, DSV, Stockholms universitet">Lindgren, Tony</AuthEnty>
        <AuthEnty xml:lang="en" affiliation="Strategic Product Planning and Advanced Analytics, Scania CV AB">Steinert, Olof</AuthEnty>
        <AuthEnty xml:lang="sv" affiliation="Strategic Product Planning and Advanced Analytics, Scania CV AB">Steinert, Olof</AuthEnty>
        <AuthEnty xml:lang="en" affiliation="Connected Intelligence, Scania CV AB">Andersson Reyna, Oskar</AuthEnty>
        <AuthEnty xml:lang="sv" affiliation="Connected Intelligence, Scania CV AB">Andersson Reyna, Oskar</AuthEnty>
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        <AuthEnty xml:lang="en" affiliation="Department of Computer and Systems Sciences, DSV, Stockholm University">Magnússon, Sindri</AuthEnty>
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      <abstract xml:lang="en" contentType="abstract">This data is a real-world, multivariate time series dataset collected from an anonymized engine
component (called Component X) of a fleet of trucks from SCANIA, Sweden. This dataset includes diverse variables capturing detailed operational data, repair records, and specifications of trucks while maintaining confidentiality by anonymization. It is well-suited for a range of machine learning applications, such as classification, regression, survival analysis, and anomaly detection, particularly when applied to predictive maintenance scenarios. The large population size and variety of features in the format of histograms and numerical counters, along with the inclusion of temporal information, make this real-world dataset unique in the field. The objective of releasing this dataset is to give a broad range of researchers the possibility of working with real-world data from a well-known international company and introduce a standard benchmark to the predictive maintenance field, fostering reproducible research.</abstract>
      <abstract xml:lang="sv" contentType="abstract">Datasetet innehåller data från en multivariat tidsserie-studie av en avidentifierad motorkomponent (Component X) i olika lastbilar från Scania AB. Se den engelskspråkiga katalogposten för utförlig information.</abstract>
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        <restrctn xml:lang="en">Access to data through SND. Data are freely accessible.</restrctn>
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            <titl xml:lang="sv">Kharazian, Zahra, Tony Lindgren, Sindri Magnússon, Olof Steinert, and Oskar Andersson Reyna. "Scania component x dataset: A real-world multivariate time series dataset for predictive maintenance." Scientific Data 12, no. 1 (2025): 493.</titl>
            <parTitl xml:lang="en">Kharazian, Zahra, Tony Lindgren, Sindri Magnússon, Olof Steinert, and Oskar Andersson Reyna. "Scania component x dataset: A real-world multivariate time series dataset for predictive maintenance." Scientific Data 12, no. 1 (2025): 493.</parTitl>
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