{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:53:47Z","timestamp":1743083627803,"version":"3.40.3"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031606137"},{"type":"electronic","value":"9783031606113"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-60611-3_22","type":"book-chapter","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:06:47Z","timestamp":1717204007000},"page":"308-322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Challenges of\u00a0Machine Failure Prediction with\u00a0Product Data - A Case Study"],"prefix":"10.1007","author":[{"given":"Dominik","family":"Buhl","sequence":"first","affiliation":[]},{"given":"Carsten","family":"Lanquillon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,1]]},"reference":[{"issue":"16","key":"22_CR1","doi-asserted-by":"publisher","first-page":"8081","DOI":"10.3390\/app12168081","volume":"12","author":"M Achouch","year":"2022","unstructured":"Achouch, M., et al.: On predictive maintenance in industry 4.0: overview, models, and challenges. Appl. Sci. 12(16), 8081 (2022)","journal-title":"Appl. Sci."},{"key":"22_CR2","doi-asserted-by":"publisher","unstructured":"Aggarwal, K., Atan, O., Farahat, A.K., Zhang, C., Ristovski, K., Gupta, C.: Two birds with one network: unifying failure event prediction and time-to-failure modeling. In: Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, pp. 1308\u20131317 (1 2019). https:\/\/doi.org\/10.1109\/BIGDATA.2018.8622431","DOI":"10.1109\/BIGDATA.2018.8622431"},{"key":"22_CR3","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.isatra.2022.01.014","volume":"129","author":"MW Ahmad","year":"2022","unstructured":"Ahmad, M.W., Akram, M.U., Ahmad, R., Hameed, K., Hassan, A.: Intelligent framework for automated failure prediction, detection, and classification of mission critical autonomous flights. ISA Trans. 129, 355\u2013371 (2022)","journal-title":"ISA Trans."},{"key":"22_CR4","unstructured":"Biedermann, H.: Instandhaltung. Ersatzteil management, pp. 9\u201328 (2008)"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Bink, R., Zschech, P.: Predictive maintenance in der industriellen praxis. HMD Praxis der Wirtschaftsinformatik 55(3), 552\u2013565 (2017)","DOI":"10.1365\/s40702-017-0378-2"},{"key":"22_CR6","doi-asserted-by":"publisher","unstructured":"Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Springer Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-29854-2","DOI":"10.1007\/978-3-319-29854-2"},{"key":"22_CR7","first-page":"1","volume":"72","author":"C Chen","year":"2023","unstructured":"Chen, C., Shi, J., Shen, M., Feng, L., Tao, G.: A predictive maintenance strategy using deep learning quantile regression and kernel density estimation for failure prediction. IEEE Trans. Instrum. Meas. 72, 1\u201312 (2023)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"22_CR8","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1109\/TMECH.2020.2971503","volume":"25","author":"C Cheng","year":"2018","unstructured":"Cheng, C., et al.: A deep learning-based remaining useful life prediction approach for bearings. IEEE\/ASME Trans. Mechatron. 25, 1243\u20131254 (2018)","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"22_CR9","doi-asserted-by":"publisher","unstructured":"Davari, N., Veloso, B., de\u00a0Assis\u00a0Costa, G., Pereira, P.M., Ribeiro, R.P., Gama, J.: A survey on data-driven predictive maintenance for the railway industry. Sensors (Basel, Switzerland) 21 (2021). https:\/\/doi.org\/10.3390\/S21175739","DOI":"10.3390\/S21175739"},{"key":"22_CR10","unstructured":"Eckner, A.: A framework for the analysis of unevenly spaced time series data (2014)"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Eckstein, P.P.: Zeitreihenanalyse. Statistik f\u00fcr Wirtschaftswissenschaftler (2012)","DOI":"10.1007\/978-3-8349-3569-4"},{"issue":"2","key":"22_CR12","doi-asserted-by":"publisher","first-page":"172988142091125","DOI":"10.1177\/1729881420911257","volume":"17","author":"Y Feng","year":"2020","unstructured":"Feng, Y., Zhao, Y., Zheng, H., Li, Z., Tan, J.: Data-driven product design toward intelligent manufacturing: a review. Int. J. Adv. Rob. Syst. 17(2), 172988142091125 (2020)","journal-title":"Int. J. Adv. Rob. Syst."},{"key":"22_CR13","series-title":"Studies in Systems, Decision and Control","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-03201-2_1","volume-title":"Soft Modeling in Industrial Manufacturing","author":"P Grzegorzewski","year":"2019","unstructured":"Grzegorzewski, P., Kochanski, A.: Data and modeling in industrial manufacturing. In: Grzegorzewski, P., Kochanski, A., Kacprzyk, J. (eds.) Soft Modeling in Industrial Manufacturing. SSDC, vol. 183, pp. 3\u201313. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-03201-2_1"},{"key":"22_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100799","volume":"27","author":"MK Hasan","year":"2021","unstructured":"Hasan, M.K., Alam, M.A., Roy, S., Dutta, A., Jawad, M.T., Das, S.: Missing value imputation affects the performance of machine learning: a review and analysis of the literature (2010\u20132021). Inform. Med. Unlocked 27, 100799 (2021)","journal-title":"Inform. Med. Unlocked"},{"key":"22_CR15","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1109\/TIM.2010.2047662","volume":"60","author":"HM Hashemian","year":"2011","unstructured":"Hashemian, H.M.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Meas. 60, 226\u2013236 (2011)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"22_CR16","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TSM.2022.3164578","volume":"35","author":"CY Hsu","year":"2022","unstructured":"Hsu, C.Y., Lu, Y.W., Yan, J.H.: Temporal convolution-based long-short term memory network with attention mechanism for remaining useful life prediction. IEEE Trans. Semicond. Manuf. 35, 220\u2013228 (2022)","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"H\u00e4nisch, T.: Grundlagen industrie 4.0. Industrie 4.0, pp. 9\u201331 (2017)","DOI":"10.1007\/978-3-658-15557-5_2"},{"key":"22_CR18","first-page":"49","volume":"25","author":"K Janocha","year":"2017","unstructured":"Janocha, K., Czarnecki, W.M.: On loss functions for deep neural networks in classification. Schedae Informaticae 25, 49\u201359 (2017)","journal-title":"Schedae Informaticae"},{"key":"22_CR19","doi-asserted-by":"publisher","unstructured":"Jollife, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. Royal Soc. A Math. Phys. Eng. Sci. 374 (2016). https:\/\/doi.org\/10.1098\/RSTA.2015.0202","DOI":"10.1098\/RSTA.2015.0202"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Kim, Y.J., Ausin, M.S., Chi, M.: Multi-temporal abstraction with time-aware deep q-learning for septic shock prevention. In: Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, pp. 1657\u20131663 (2021)","DOI":"10.1109\/BigData52589.2021.9671662"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Kim, Y.J., Chi, M.: Temporal belief memory: imputing missing data during RNN training (2018)","DOI":"10.24963\/ijcai.2018\/322"},{"key":"22_CR22","doi-asserted-by":"publisher","unstructured":"Kirchg\u00e4ssner, G., Wolters, J.: Introduction to Modern Time Series Analysis, pp. 1\u2013274. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-73291-4","DOI":"10.1007\/978-3-540-73291-4"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Leukel, J., Gonz\u00e1lez, J., Riekert, M.: Machine learning-based failure prediction in industrial maintenance: improving performance by sliding window selection. Int. J. Qual. Reliab. Manag. ahead-of-print (2022)","DOI":"10.1108\/IJQRM-12-2021-0439"},{"key":"22_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106113","volume":"89","author":"H Li","year":"2020","unstructured":"Li, H., Zhao, W., Zhang, Y., Zio, E.: Remaining useful life prediction using multi-scale deep convolutional neural network. Appl. Soft Comput. 89, 106113 (2020)","journal-title":"Appl. Soft Comput."},{"key":"22_CR25","doi-asserted-by":"publisher","first-page":"17149","DOI":"10.1007\/s00521-020-05169-y","volume":"32","author":"IE Livieris","year":"2020","unstructured":"Livieris, I.E., Stavroyiannis, S., Pintelas, E., Pintelas, P.: A novel validation framework to enhance deep learning models in time-series forecasting. Neural Comput. Appl. 32, 17149\u201317167 (2020)","journal-title":"Neural Comput. Appl."},{"key":"22_CR26","doi-asserted-by":"publisher","unstructured":"Lu, H., Barzegar, V., Nemani, V.P., Hu, C., Laflamme, S., Zimmerman, A.T.: GAN-LSTM predictor for failure prognostics of rolling element bearings. In: 2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021, June 2021. https:\/\/doi.org\/10.1109\/ICPHM51084.2021.9486650","DOI":"10.1109\/ICPHM51084.2021.9486650"},{"key":"22_CR27","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.cirpj.2022.11.004","volume":"40","author":"P Nunes","year":"2023","unstructured":"Nunes, P., Santos, J., Rocha, E.: Challenges in predictive maintenance - a review. CIRP J. Manuf. Sci. Technol. 40, 53\u201367 (2023)","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Pedrycz, W., Chen, S.M.: Time series analysis, modeling and applications: a computational intelligence perspective. Intell. Syst. Ref. Libr. 47 (2013)","DOI":"10.1007\/978-3-642-33439-9"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research 24, 45\u201377 (2014)","DOI":"10.2753\/MIS0742-1222240302"},{"key":"22_CR30","unstructured":"Pomberger, G., Pree, W., Stritzinger, A.: Methoden und werkzeuge f\u00fcr das prototyping und ihre integration. Inform. Forsch. Entwickl. 7(2), 49\u201361 (1992)"},{"key":"22_CR31","unstructured":"Rahman, M.M., et al.: Real-time cavity fault prediction in CEBAF using deep learning. In: NAPAC 2022: Proceedings of the North American Particle Accelerator Conference, January 2022"},{"key":"22_CR32","unstructured":"Ran, Y., Zhou, X., Lin, P., Wen, Y., Deng, R.: A survey of predictive maintenance: systems, purposes and approaches. IEEE Commun. Surv. Tutor. XX (2019)"},{"key":"22_CR33","doi-asserted-by":"crossref","unstructured":"Runkler, T.A.: Datenvorverarbeitung. Data Mining, pp. 21\u201334 (2010)","DOI":"10.1007\/978-3-8348-9353-6_3"},{"key":"22_CR34","doi-asserted-by":"crossref","unstructured":"Sang, G.M., Xu, L., de\u00a0Vrieze, P., Bai, Y., Pan, F.: Predictive maintenance in industry 4.0. In: Proceedings of the 10th International Conference on Information Systems and Technologies, ICIST 2020. ACM, June 2020","DOI":"10.1145\/3447568.3448537"},{"key":"22_CR35","doi-asserted-by":"publisher","unstructured":"Serradilla, O., Zugasti, E., Rodriguez, J., Zurutuza, U.: Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect. Appl. Intell. 52, 10934\u201310964 (2020). https:\/\/doi.org\/10.48550\/arxiv.2010.03207","DOI":"10.48550\/arxiv.2010.03207"},{"issue":"12","key":"22_CR36","first-page":"310","volume":"6","author":"S Sharma","year":"2017","unstructured":"Sharma, S., Sharma, S., Athaiya, A.: Activation functions in neural networks. Towards Data Sci. 6(12), 310\u2013316 (2017)","journal-title":"Towards Data Sci."},{"key":"22_CR37","doi-asserted-by":"publisher","unstructured":"Shukla, S.N., Marlin, B.M.: Interpolation-prediction networks for irregularly sampled time series. In: 7th International Conference on Learning Representations, ICLR 2019, September 2019. https:\/\/doi.org\/10.48550\/arxiv.1909.07782","DOI":"10.48550\/arxiv.1909.07782"},{"key":"22_CR38","unstructured":"Shukla, S.N., Marlin, B.M.: A survey on principles, models and methods for learning from irregularly sampled time series (2020)"},{"key":"22_CR39","doi-asserted-by":"publisher","unstructured":"Shukla, S.N., Marlin, B.M.: Multi-time attention networks for irregularly sampled time series, January 2021. https:\/\/doi.org\/10.48550\/arxiv.2101.10318","DOI":"10.48550\/arxiv.2101.10318"},{"key":"22_CR40","doi-asserted-by":"publisher","unstructured":"Si, X.S., Wang, W., Hu, C.H., Zhou, D.H.: Remaining useful life estimation - a review on the statistical data driven approaches. Eur. J. Oper. Res. 213, 1\u201314 (2011). https:\/\/doi.org\/10.1016\/J.EJOR.2010.11.018","DOI":"10.1016\/J.EJOR.2010.11.018"},{"key":"22_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24436-5","volume-title":"Product Lifecycle Management (Volume 2)","author":"J Stark","year":"2016","unstructured":"Stark, J.: Product Lifecycle Management (Volume 2). Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-24436-5"},{"key":"22_CR42","doi-asserted-by":"crossref","unstructured":"Tang, H., Yin, Y.: Forecast position for ship in port based on irregular time series. In: Proceedings - 2022 International Symposium on Electrical, Electronics and Information Engineering, ISEEIE 2022, pp. 135\u2013138 (2022)","DOI":"10.1109\/ISEEIE55684.2022.00031"},{"key":"22_CR43","doi-asserted-by":"crossref","unstructured":"Van, T.T., Chan, H.L., Parthasarathi, S., Lim, C.P., Chua, Y.Q.: IoT and machine learning enable predictive maintenance for manufacturing systems: a use-case of laser welding machine implementation. SSRN Electron. J. (2022)","DOI":"10.2139\/ssrn.4073901"},{"key":"22_CR44","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zheng, S., Farahat, A., Serita, S., Gupta, C.: Remaining useful life estimation using functional data analysis. In: 2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019, April 2019","DOI":"10.1109\/ICPHM.2019.8819420"},{"key":"22_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, W., Yang, D., Wang, H.: Data-driven methods for predictive maintenance of industrial equipment: a survey. IEEE Syst. J. 13, 2213\u20132227 (2019)","DOI":"10.1109\/JSYST.2019.2905565"},{"key":"22_CR46","doi-asserted-by":"crossref","unstructured":"Zonta, T., da\u00a0Costa, C.A., da\u00a0Rosa\u00a0Righi, R., de\u00a0Lima, M.J., da\u00a0Trindade, E.S., Li, G.P.: Predictive maintenance in the industry 4.0: a systematic literature review. Comput. Ind. Eng. 150, 106889 (2020)","DOI":"10.1016\/j.cie.2020.106889"},{"key":"22_CR47","doi-asserted-by":"crossref","unstructured":"Z\u00fcfle, M., Agne, J., Grohmann, J., D\u00f6rtoluk, I., Kounev, S.: A predictive maintenance methodology: predicting the time-to-failure of machines in industry 4.0. In: IEEE International Conference on Industrial Informatics (INDIN) (2021)","DOI":"10.1109\/INDIN45523.2021.9557387"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in HCI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-60611-3_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:19:58Z","timestamp":1717204798000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-60611-3_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031606137","9783031606113"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-60611-3_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Washington DC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}