D. Kracker, Revan Kumar Dhanasekaran, A. Schumacher, J. Garcke
{"title":"Method for automated detection of outliers in crash simulations","authors":"D. Kracker, Revan Kumar Dhanasekaran, A. Schumacher, J. Garcke","doi":"10.1080/13588265.2022.2074634","DOIUrl":null,"url":null,"abstract":"Abstract Stricter legal requirements in crash safety lead to more complex development processes in computer-aided engineering and result in an increasing number of simulations. Both, the construction of the simulation models as well as their evaluation are costly and time-consuming. Therefore, an automated workflow is required that significantly facilitates the analysis of the results by the engineer and increases the quality of the evaluation. In this study an automated evaluation process is proposed that detects anomalous crash behaviour in a bundle of crash simulations. The individual states from the simulation are analysed separately from each other and an outlier score is calculated using a kth-nearest-neighbour approach. Subsequently, these results are averaged into one score for each simulation. With the help of different statistical methods, a threshold value is calculated, from which a simulation can be identified as an outlier. The evaluation is carried out on 5 datasets. On average, the precision and recall of the presented method are 1.0 and 0.91, respectively.","PeriodicalId":13784,"journal":{"name":"International Journal of Crashworthiness","volume":"28 1","pages":"96 - 107"},"PeriodicalIF":1.8000,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Crashworthiness","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/13588265.2022.2074634","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Stricter legal requirements in crash safety lead to more complex development processes in computer-aided engineering and result in an increasing number of simulations. Both, the construction of the simulation models as well as their evaluation are costly and time-consuming. Therefore, an automated workflow is required that significantly facilitates the analysis of the results by the engineer and increases the quality of the evaluation. In this study an automated evaluation process is proposed that detects anomalous crash behaviour in a bundle of crash simulations. The individual states from the simulation are analysed separately from each other and an outlier score is calculated using a kth-nearest-neighbour approach. Subsequently, these results are averaged into one score for each simulation. With the help of different statistical methods, a threshold value is calculated, from which a simulation can be identified as an outlier. The evaluation is carried out on 5 datasets. On average, the precision and recall of the presented method are 1.0 and 0.91, respectively.
期刊介绍:
International Journal of Crashworthiness is the only journal covering all matters relating to the crashworthiness of road vehicles (including cars, trucks, buses and motorcycles), rail vehicles, air and spacecraft, ships and submarines, and on- and off-shore installations.
The Journal provides a unique forum for the publication of original research and applied studies relevant to an audience of academics, designers and practicing engineers. International Journal of Crashworthiness publishes both original research papers (full papers and short communications) and state-of-the-art reviews.
International Journal of Crashworthiness welcomes papers that address the quality of response of materials, body structures and energy-absorbing systems that are subjected to sudden dynamic loading, papers focused on new crashworthy structures, new concepts in restraint systems and realistic accident reconstruction.