{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:15:28Z","timestamp":1742919328847,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031366154"},{"type":"electronic","value":"9783031366161"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-36616-1_53","type":"book-chapter","created":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T18:03:41Z","timestamp":1687629821000},"page":"665-679","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Loose Wheel Bolts of\u00a0a\u00a0Vehicle Using Accelerometers in\u00a0the\u00a0Chassis"],"prefix":"10.1007","author":[{"given":"Jonas","family":"Schmidt","sequence":"first","affiliation":[]},{"given":"Kai-Uwe","family":"K\u00fchnberger","sequence":"additional","affiliation":[]},{"given":"Dennis","family":"Pape","sequence":"additional","affiliation":[]},{"given":"Tobias","family":"Pobandt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,25]]},"reference":[{"issue":"3","key":"53_CR1","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","volume":"31","author":"A Bagnall","year":"2016","unstructured":"Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31(3), 606\u2013660 (2016). https:\/\/doi.org\/10.1007\/s10618-016-0483-9","journal-title":"Data Min. Knowl. Disc."},{"key":"53_CR2","doi-asserted-by":"publisher","unstructured":"Bernhard, J., Schmidt, J., Schutera, M.: Density based anomaly detection for wind turbine condition monitoring. In: Proceedings of the 1st International Joint Conference on Energy and Environmental Engineering - CoEEE, pp. 87\u201393. INSTICC, SciTePress (2022). https:\/\/doi.org\/10.5220\/0011358600003355","DOI":"10.5220\/0011358600003355"},{"key":"53_CR3","doi-asserted-by":"publisher","unstructured":"Braei, M., Wagner, S.: Anomaly detection in univariate time-series: a survey on the state-of-the-art. CoRR (2020). https:\/\/doi.org\/10.48550\/arXiv.2004.00433","DOI":"10.48550\/arXiv.2004.00433"},{"key":"53_CR4","doi-asserted-by":"publisher","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 233\u2013240. Association for Computing Machinery, New York (2006). https:\/\/doi.org\/10.1145\/1143844.1143874","DOI":"10.1145\/1143844.1143874"},{"key":"53_CR5","unstructured":"Dodd, M.: Heavy vehicle wheel detachment and possible solutions-phase 2-final report (2010)"},{"key":"53_CR6","doi-asserted-by":"publisher","unstructured":"Dohi, K., et al.: Description and discussion on DCASE 2022 challenge task 2: unsupervised anomalous sound detection for machine condition monitoring applying domain generalization techniques (2022). https:\/\/doi.org\/10.48550\/ARXIV.2206.05876","DOI":"10.48550\/ARXIV.2206.05876"},{"key":"53_CR7","doi-asserted-by":"publisher","unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: Proceedings of the International Conference on Learning Representations (2019). https:\/\/doi.org\/10.48550\/ARXIV.1812.04606","DOI":"10.48550\/ARXIV.1812.04606"},{"key":"53_CR8","unstructured":"H\u00e4gg, J.: Loose wheel indicator selected by Audi for a range of car models (2019). https:\/\/www.mynewsdesk.com\/se\/nira-dynamics\/pressreleases\/loose-wheel-indicator-selected-by-audi-for-a-range-of-car-models-2966123"},{"key":"53_CR9","unstructured":"Stra\u00dfenverkehrs-ordnung (stvo) \u00a723 sonstige pflichten von fahrzeugf\u00fchrenden (2013). https:\/\/www.gesetze-im-internet.de\/stvo_2013\/_23.html. Accessed 04 Mar 2023"},{"key":"53_CR10","doi-asserted-by":"crossref","unstructured":"Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 580\u2013585 (1985)","DOI":"10.1109\/TSMC.1985.6313426"},{"key":"53_CR11","doi-asserted-by":"publisher","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). https:\/\/doi.org\/10.48550\/ARXIV.1412.6980","DOI":"10.48550\/ARXIV.1412.6980"},{"key":"53_CR12","doi-asserted-by":"publisher","unstructured":"Koizumi, Y., et al.: Description and discussion on DCASE2020 challenge task2: unsupervised anomalous sound detection for machine condition monitoring (2020). https:\/\/doi.org\/10.48550\/ARXIV.2006.05822","DOI":"10.48550\/ARXIV.2006.05822"},{"key":"53_CR13","doi-asserted-by":"crossref","unstructured":"Krishna, K., Murty, M.N.: Genetic k-means algorithm. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 433\u2013439 (1999)","DOI":"10.1109\/3477.764879"},{"key":"53_CR14","unstructured":"Verordnung \u00fcber sicherheit und gesundheitsschutz bei der verwendung von arbeitsmitteln (betriebssicherheitsverordnung - betrsichv) \u00a74 grundpflichten des arbeitgebers (2015). https:\/\/www.gesetze-im-internet.de\/betrsichv_2015\/_4.html. Accessed 04 Mar 2023"},{"key":"53_CR15","unstructured":"Logan, B.: Mel frequency cepstral coefficients for music modeling. In: International Society for Music Information Retrieval Conference (2000)"},{"key":"53_CR16","doi-asserted-by":"publisher","unstructured":"Lyon, D.A.: The discrete Fourier transform, part 4: spectral leakage. J. Object Technol. 23\u201334 (2009). https:\/\/doi.org\/10.5381\/jot.2009.8.7.c2","DOI":"10.5381\/jot.2009.8.7.c2"},{"key":"53_CR17","unstructured":"Malhotra, P., Vig, L., Shroff, G., Agarwal, P., et al.: Long short term memory networks for anomaly detection in time series. In: Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (2015)"},{"key":"53_CR18","doi-asserted-by":"crossref","unstructured":"Nannavecchia, A., Girardi, F., Fina, P.R., Scalera, M., Dimauro, G.: Personal heart health monitoring based on 1D convolutional neural network. J. Imaging (2021)","DOI":"10.3390\/jimaging7020026"},{"key":"53_CR19","doi-asserted-by":"publisher","unstructured":"O\u2019Shea, K., Nash, R.: An introduction to convolutional neural networks. CoRR (2015). https:\/\/doi.org\/10.48550\/arXiv.1511.08458","DOI":"10.48550\/arXiv.1511.08458"},{"key":"53_CR20","doi-asserted-by":"publisher","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks (2018). https:\/\/doi.org\/10.48550\/ARXIV.1801.04381","DOI":"10.48550\/ARXIV.1801.04381"},{"key":"53_CR21","doi-asserted-by":"publisher","unstructured":"Schmidl, S., Wenig, P., Papenbrock, T.: Anomaly detection in time series: a comprehensive evaluation. Proc. VLDB Endow. 1779\u20131797 (2022). https:\/\/doi.org\/10.14778\/3538598.3538602","DOI":"10.14778\/3538598.3538602"},{"key":"53_CR22","unstructured":"Smith, J.O.: Mathematics of the Discrete Fourier Transform (DFT): With Audio Applications. BookSurge Publishing (2008)"},{"key":"53_CR23","doi-asserted-by":"publisher","unstructured":"Socor\u00f3, J.C., Al\u00edas, F., Alsina-Pag\u00e9s, R.M.: An anomalous noise events detector for dynamic road traffic noise mapping in real-life urban and suburban environments. Sensors 17(10) (2017). https:\/\/doi.org\/10.3390\/s17102323","DOI":"10.3390\/s17102323"},{"key":"53_CR24","doi-asserted-by":"publisher","unstructured":"Suefusa, K., Nishida, T., Purohit, H., Tanabe, R., Endo, T., Kawaguchi, Y.: Anomalous sound detection based on interpolation deep neural network. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2020, pp. 271\u2013275 (2020). https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9054344","DOI":"10.1109\/ICASSP40776.2020.9054344"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36616-1_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T18:14:14Z","timestamp":1687630454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36616-1_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031366154","9783031366161"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36616-1_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"25 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IbPRIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberian Conference on Pattern Recognition and Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Alicante","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ibpria2022b","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"86","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}