{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:19:50Z","timestamp":1767323990152,"version":"3.48.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032133113","type":"print"},{"value":"9783032133120","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-13312-0_20","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:16:47Z","timestamp":1767323807000},"page":"343-352","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pervasive Intelligent Diagnostics for\u00a0High-Tech Systems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2791-6070","authenticated-orcid":false,"given":"Rob","family":"Bemthuis","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9426-4511","authenticated-orcid":false,"given":"Thomas","family":"N\u00e4gele","sequence":"additional","affiliation":[]},{"given":"Cor","family":"van der Struijf","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: International Symposium on Handheld and Ubiquitous Computing, pp. 304\u2013307. Springer (1999)","DOI":"10.1007\/3-540-48157-5_29"},{"key":"20_CR2","unstructured":"Aedo, I., Onorati, T., Tucci, C., D\u00edaz, P., Montero, \u00c1., Castro, J.: Bridging the gap between knowledge and human expertise: integrating explicit and tacit knowledge in maintenance operations. In: Proceedings of the 1st International Workshop on Human-Centered AI for Human-Machine Teams. CEUR Workshop Proceedings, vol.\u00a03978. CEUR-WS.org (2024)"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Al\u00a0Maruf, A., Bakhtin, A., Cerny, T., Taibi, D.: Using microservice telemetry data for system dynamic analysis. In: 2022 IEEE International Conference on Service-Oriented System Engineering (SOSE), pp. 29\u201338. IEEE (2022)","DOI":"10.1109\/SOSE55356.2022.00010"},{"key":"20_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2023.110156","volume":"239","author":"A Bimpas","year":"2024","unstructured":"Bimpas, A., Violos, J., Leivadeas, A., Varlamis, I.: Leveraging pervasive computing for ambient intelligence: a survey on recent advancements, applications and open challenges. Comput. Netw. 239, 110156 (2024)","journal-title":"Comput. Netw."},{"issue":"16","key":"20_CR5","doi-asserted-by":"publisher","first-page":"7087","DOI":"10.3390\/s23167087","volume":"23","author":"J Bofill","year":"2023","unstructured":"Bofill, J., Abisado, M., Villaverde, J., Sampedro, G.A.: Exploring digital twin-based fault monitoring: challenges and opportunities. Sensors 23(16), 7087 (2023)","journal-title":"Sensors"},{"key":"20_CR6","unstructured":"Breque, M., De\u00a0Nul, L., Petridis, A.: Industry 5.0: towards a sustainable, human-centric and resilient European industry (2021). https:\/\/research-and-innovation.ec.europa.eu\/knowledge-publications-tools-and-data\/publications\/all-publications\/industry-50-towards-sustainable-human-centric-and-resilient-european-industry_en"},{"key":"20_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109552","volume":"139","author":"J Chapelin","year":"2025","unstructured":"Chapelin, J., et al.: Data-driven drift detection and diagnosis framework for predictive maintenance of heterogeneous production processes: application to a multiple tapping process. Eng. Appl. Artif. Intell. 139, 109552 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Estrin, D., Govindan, R., Heidemann, J., Kumar, S.: Next century challenges: scalable coordination in sensor networks. In: Proceedings of the 5th Annual ACM\/IEEE International Conference on Mobile Computing and Networking, pp. 263\u2013270 (1999)","DOI":"10.1145\/313451.313556"},{"issue":"2","key":"20_CR9","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1108\/TLO-09-2022-0107","volume":"30","author":"N Galan","year":"2023","unstructured":"Galan, N.: Knowledge loss induced by organizational member turnover: a review of empirical literature, synthesis and future research directions (Part I). Learn. Organ. Int. J. 30(2), 117\u2013136 (2023)","journal-title":"Learn. Organ. Int. J."},{"issue":"1","key":"20_CR10","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s10586-024-04686-y","volume":"28","author":"SS Gill","year":"2025","unstructured":"Gill, S.S., et al.: Edge AI: a taxonomy, systematic review and future directions. Clust. Comput. 28(1), 18 (2025)","journal-title":"Clust. Comput."},{"key":"20_CR11","unstructured":"Huijbrechts, B.: Empower system engineering with data insights. Technical report, TNO \u2013 ESI (Embedded Systems Innovation) (2019). https:\/\/downloads.esi.nl\/leaflets\/data_insights_2019c.pdf. Accessed 30 July 2025"},{"key":"20_CR12","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1016\/j.future.2017.10.021","volume":"105","author":"R Iqbal","year":"2020","unstructured":"Iqbal, R., Doctor, F., More, B., Mahmud, S., Yousuf, U.: Big data analytics and computational intelligence for cyber-physical systems: recent trends and state of the art applications. Futur. Gener. Comput. Syst. 105, 766\u2013778 (2020)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Islam, M.S., Rakha, M.S., Pourmajidi, W., Sivaloganathan, J., Steinbacher, J., Miranskyy, A.: Anomaly detection in large-scale cloud systems: an industry case and dataset. arXiv preprint arXiv:2411.09047 (2024)","DOI":"10.1109\/ICSE-SEIP66354.2025.00039"},{"key":"20_CR14","doi-asserted-by":"publisher","first-page":"1537","DOI":"10.1016\/j.spc.2021.03.023","volume":"27","author":"K Karuppiah","year":"2021","unstructured":"Karuppiah, K., Sankaranarayanan, B., Ali, S.M.: On sustainable predictive maintenance: exploration of key barriers using an integrated approach. Sustain. Prod. Consum. 27, 1537\u20131553 (2021)","journal-title":"Sustain. Prod. Consum."},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Kim, J.H.: A review of cyber-physical system research relevant to the emerging IT trends: industry 4.0, IoT, big data, and cloud computing. J. Ind. Integr. Manag. 2(03), 1750011 (2017)","DOI":"10.1142\/S2424862217500117"},{"issue":"4","key":"20_CR16","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1002\/inst.12406","volume":"25","author":"W Leibbrandt","year":"2022","unstructured":"Leibbrandt, W., Wesselius, J., Beenker, F.: TNO-ESI-Systems engineering methodologies for managing complexity in the high-tech equipment industry: our roadmap. Insight 25(4), 15\u201321 (2022). https:\/\/doi.org\/10.1002\/inst.12406","journal-title":"Insight"},{"key":"20_CR17","unstructured":"List, F., Verberk, R., Janssen, V., Hulshof, E., van Ulsen, P., Stojanovic, I.: Roadmap for semiconductor manufacturing equipment 2024\u20132027. Technical report, Holland High Tech (2024). https:\/\/hollandhightech.nl\/_asset\/_public\/Innovatie\/Technologieen\/z_pdf_roadmaps\/240115-Roadmap-Semiconductor-Manufacturing-Equipment-2024-2027-V3.pdf. Accessed 30 July 2025"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Mc\u00a0Court, K., Mc\u00a0Court, X., Du, S., Zeng, Z.: Use digital twins to support fault diagnosis from system-level condition-monitoring data. In: 2025 IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD), pp. 1064\u20131069. IEEE (2025)","DOI":"10.1109\/SSD64182.2025.10989951"},{"key":"20_CR19","unstructured":"Meier, B., Skelin, M., Beenker, F., Leibbrandt, W.: HTSM systems engineering roadmap. Technical report, Holland High Tech (2020). https:\/\/hollandhightech.nl\/_asset\/_public\/Innovatie\/Technologieen\/z_pdf_roadmaps\/Roadmap-Systems-Engineering-update-2020-final-v20200724.pdf"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"N\u00e4gele, T., Barbini, L., van den Braak, G., Lipplaa, M., Piedrafita, A.: From knowledge graphs to probabilistic models for system-level diagnostics. In: 13th IMA International Conference on Modelling in Industrial Maintenance and Reliability - MIMAR2025 (2025)","DOI":"10.19124\/ima.2025.01.52"},{"key":"20_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2023.111917","volume":"209","author":"M Panahandeh","year":"2024","unstructured":"Panahandeh, M., Hamou-Lhadj, A., Hamdaqa, M., Miller, J.: Serviceanomaly: an anomaly detection approach in microservices using distributed traces and profiling metrics. J. Syst. Softw. 209, 111917 (2024)","journal-title":"J. Syst. Softw."},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Pham, L., Zhang, H., Ha, H., Salim, F., Zhang, X.: Rcaeval: a benchmark for root cause analysis of microservice systems with telemetry data. In: Companion Proceedings of the ACM on Web Conference 2025, pp. 777\u2013780 (2025)","DOI":"10.1145\/3701716.3715290"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Sinha, D., Roy, R.: Reviewing cyber-physical system as a part of smart factory in industry 4.0. IEEE Eng. Manag. Rev. 48(2), 103\u2013117 (2020)","DOI":"10.1109\/EMR.2020.2992606"},{"key":"20_CR24","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.future.2020.10.015","volume":"116","author":"B Steenwinckel","year":"2021","unstructured":"Steenwinckel, B., et al.: Flags: a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning. Futur. Gener. Comput. Syst. 116, 30\u201348 (2021)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"20_CR25","unstructured":"University of Twente, EEMCS - PS Group: PS Group - Pervasive Systems (2025). https:\/\/www.utwente.nl\/en\/eemcs\/ps\/. Accessed 30 July 2025"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Van\u00a0Oudenhoven, B., Van\u00a0de Calseyde, P., Basten, R., Demerouti, E.: Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective. Int. J. Prod. Res. 61(22), 7846\u20137865 (2023)","DOI":"10.1080\/00207543.2022.2154403"},{"issue":"3","key":"20_CR27","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/329124.329126","volume":"3","author":"M Weiser","year":"1999","unstructured":"Weiser, M.: The computer for the 21st century. ACM SIGMOBILE Mob. Comput. Commun. Rev. 3(3), 3\u201311 (1999)","journal-title":"ACM SIGMOBILE Mob. Comput. Commun. Rev."},{"key":"20_CR28","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1016\/j.procs.2022.01.348","volume":"200","author":"Y You","year":"2022","unstructured":"You, Y., Chen, C., Hu, F., Liu, Y., Ji, Z.: Advances of digital twins for predictive maintenance. Procedia Comput. Sci. 200, 1471\u20131480 (2022)","journal-title":"Procedia Comput. Sci."},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, Y., Zhang, S., Song, L., Tao, F.: Artificial intelligence-enhanced digital twin systems engineering towards the industrial metaverse in the era of Industry 5.0. Chin. J. Mech. Eng. 38(1), 40 (2025)","DOI":"10.1186\/s10033-025-01210-0"},{"key":"20_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.110663","volume":"150","author":"J Zhang","year":"2025","unstructured":"Zhang, J., et al.: Multimodal data imputation and fusion for trustworthy fault diagnosis of mechanical systems. Eng. Appl. Artif. Intell. 150, 110663 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Zhong, D., Xia, Z., Zhu, Y., Duan, J.: Overview of predictive maintenance based on digital twin technology. Heliyon 9(4) (2023)","DOI":"10.1016\/j.heliyon.2023.e14534"}],"container-title":["Lecture Notes in Computer Science","Sensor-Based Activity Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-13312-0_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:16:49Z","timestamp":1767323809000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-13312-0_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032133113","9783032133120"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-13312-0_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"iWOAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Enschede","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwoar2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iwoar.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}