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英文字典中文字典相关资料:


  • SCANIA Component X dataset: a real-world multivariate time series . . .
    This paper introduces a real-world, multivariate time series dataset collected exclusively from a single anonymized engine component (Component X) across a fleet of SCANIA trucks
  • SCANIA Component X Dataset: A Real-World Multivariate Time Series . . .
    The dataset includes operational data, repair records, and specifications related to Component X, while maintaining confidentiality through anonymization It is well-suited for a range of machine learning applications, including classification, regression, survival analysis, and anomaly detection, particularly in predictive maintenance scenarios
  • Cost-Optimised Machine Learning Model Comparison for Predictive Maintenance
    Predictive maintenance is essential for reducing industrial downtime and costs, yet real-world datasets frequently encounter class imbalance and require cost-sensitive evaluation due to costly misclassification errors This study utilises the SCANIA Component X dataset to advance predictive maintenance through machine learning, employing seven supervised algorithms, Support Vector Machine
  • Two-Stage LightGBM Framework for Cost-Sensitive . . . - ScienceDirect
    This study proposed a Two-stage LightGBM framework to address the cost-sensitive predictive maintenance problem using the Scania Component X dataset The method integrates statistical and trend-based descriptors extracted via the last_k_summary procedure with categorical vehicle specifications, thereby capturing degradation dynamics beyond
  • Predicting the Failure of Component X in the Scania Dataset with Graph . . .
    This paper addresses this challenge by investigating the performance of two models on a real-world multivariate timeseries dataset, “SCANIA Component X”, for predicting maintenance needs in a vehicle fleet One of the novelties introduced in this paper is the application of Graph Neural Networks (GNNs) [2] to PdM
  • SCANIA Component X Dataset: A Real-World Multivariate Time Series . . .
    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
  • Cost-Optimised Machine Learning Model Comparison for Predictive Maintenance
    Abstract: Predictive maintenance is essential for reducing industrial downtime and costs, yet real-world datasets frequently encounter class imbalance and require cost-sensitive eval-uation due to costly misclassification errors This study utilises the SCANIA Component X dataset to advance predictive maintenance through machine learning, employing seven supervised algorithms, Support Vector
  • Two-Stage LightGBM Framework for Cost-Sensitive Prediction of Impending . . .
    This study proposed a Two-stage LightGBM framework to address the cost-sensitive predictive maintenance problem using the Scania Component X dataset The method integrates statistical and trend-based descriptors extracted via the last_k_summary procedure with categorical vehicle specifications, thereby capturing degradation dynamics beyond
  • SCANIA Component X dataset: a real-world multivariate time . . . - Nature
    Predicting failures and maintenance time in predictive maintenance is challenging due to the scarcity of comprehensive real-world datasets, and among those available, few are of time series format
  • Scania Component X Remaining Useful Life (RUL) Prediction
    This project implements a Remaining Useful Life (RUL) prediction system for Scania truck Component X using GRU neural networks and cost-sensitive learning The system converts RUL prediction into a 5-class classification problem optimized for maintenance decision-making, as is suggested by the competition which provided the dataset Inherently giving a prediction of whether the component will
  • SCANIA Component X dataset: a real-world multivariate time series . . .
    The dataset includes operational data, repair records, and specifications related to Component X, while maintaining confidentiality through anonymization It is well-suited for a range of machine learning applications, including classification, regression, survival analysis, and anomaly detection, particularly in predictive maintenance scenarios





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