{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T14:39:09Z","timestamp":1778078349534,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:00:00Z","timestamp":1684454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["LA\/P\/0063\/2020"],"award-info":[{"award-number":["LA\/P\/0063\/2020"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["CHIST-ERA\/0004\/2019"],"award-info":[{"award-number":["CHIST-ERA\/0004\/2019"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["POCI-01-0247-FEDER-041435"],"award-info":[{"award-number":["POCI-01-0247-FEDER-041435"]}]},{"name":"XPM","award":["LA\/P\/0063\/2020"],"award-info":[{"award-number":["LA\/P\/0063\/2020"]}]},{"name":"XPM","award":["CHIST-ERA\/0004\/2019"],"award-info":[{"award-number":["CHIST-ERA\/0004\/2019"]}]},{"name":"XPM","award":["POCI-01-0247-FEDER-041435"],"award-info":[{"award-number":["POCI-01-0247-FEDER-041435"]}]},{"name":"European Regional Development Fund (ERDF)","award":["LA\/P\/0063\/2020"],"award-info":[{"award-number":["LA\/P\/0063\/2020"]}]},{"name":"European Regional Development Fund (ERDF)","award":["CHIST-ERA\/0004\/2019"],"award-info":[{"award-number":["CHIST-ERA\/0004\/2019"]}]},{"name":"European Regional Development Fund (ERDF)","award":["POCI-01-0247-FEDER-041435"],"award-info":[{"award-number":["POCI-01-0247-FEDER-041435"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The early detection of fire is of utmost importance since it is related to devastating threats regarding human lives and economic losses. Unfortunately, fire alarm sensory systems are known to be prone to failures and frequent false alarms, putting people and buildings at risk. In this sense, it is essential to guarantee smoke detectors\u2019 correct functioning. Traditionally, these systems have been subject to periodic maintenance plans, which do not consider the state of the fire alarm sensors and are, therefore, sometimes carried out not when necessary but according to a predefined conservative schedule. Intending to contribute to designing a predictive maintenance plan, we propose an online data-driven anomaly detection of smoke sensors that model the behaviour of these systems over time and detect abnormal patterns that can indicate a potential failure. Our approach was applied to data collected from independent fire alarm sensory systems installed with four customers, from which about three years of data are available. For one of the customers, the obtained results were promising, with a precision score of 1 with no false positives for 3 out of 4 possible faults. Analysis of the remaining customers\u2019 results highlighted possible reasons and potential improvements to address this problem better. These findings can provide valuable insights for future research in this area.<\/jats:p>","DOI":"10.3390\/s23104902","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T10:08:55Z","timestamp":1684490935000},"page":"4902","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7861-9012","authenticated-orcid":false,"given":"Emanuel","family":"Sousa Tom\u00e9","sequence":"first","affiliation":[{"name":"Computer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"},{"name":"Bosch Security Systems, 3880-728 Ovar, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6852-8077","authenticated-orcid":false,"given":"Rita P.","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3578-7769","authenticated-orcid":false,"given":"In\u00eas","family":"Dutra","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"CINTESIS\u2014Center for Health Technology and Services Research, 4200-465 Porto, Portugal"}]},{"given":"Arlete","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Bosch Security Systems, 3880-728 Ovar, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103364","DOI":"10.1016\/j.firesaf.2021.103364","article-title":"Real-time fire detection system based on dynamic time warping of multichannel sensor networks","volume":"123","author":"Baek","year":"2021","journal-title":"Fire Saf. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.firesaf.2015.11.015","article-title":"Fast fire flame detection in surveillance video using logistic regression and temporal smoothing","volume":"79","author":"Kong","year":"2016","journal-title":"Fire Saf. J."},{"key":"ref_3","unstructured":"Ahrens, M. (2021). Smoke Alarms in US Home Fires (NFPA \u00ae) Key Findings, NFPA."},{"key":"ref_4","unstructured":"Tambe, A., Nambi, A., and Marathe, S. (July, January 24). Is your smoke detector working properly? Robust fault tolerance approaches for smoke detectors. Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, Virtual Event."},{"key":"ref_5","unstructured":"(2004). Fire Detection and Fire Alarm Systems\u2014Part 14: Guidelines for Planning, Design, Installation, Commissioning, Use and Maintenance. Standard No. CEN EN 54-14."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.inffus.2020.10.001","article-title":"Smart anomaly detection in sensor systems: A multi-perspective review","volume":"67","author":"Erhan","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1541882","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly Detection: A Survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1145\/3229329.3229332","article-title":"A Survey on Anomaly detection in Evolving Data [with Application to Forest Fire Risk Prediction]","volume":"20","author":"Salehi","year":"2018","journal-title":"SIGKDD Explor. Newsl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Davari, N., Veloso, B., Costa, G.d.A., Pereira, P.M., Ribeiro, R.P., and Gama, J. (2021). A survey on data-driven predictive maintenance for the railway industry. Sensors, 21.","DOI":"10.3390\/s21175739"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106024","DOI":"10.1016\/j.cie.2019.106024","article-title":"A systematic literature review of machine learning methods applied to predictive maintenance","volume":"137","author":"Carvalho","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zheng, S., Farahat, A., Serita, S., and Gupta, C. (2019, January 17\u201320). Remaining useful life estimation using functional data analysis. Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019, San Francisco, CA, USA.","DOI":"10.1109\/ICPHM.2019.8819420"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2907070","article-title":"A Survey of Predictive Modeling on Imbalanced Domains","volume":"49","author":"Branco","year":"2016","journal-title":"ACM Comput. Surv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10994-016-5584-6","article-title":"Sequential anomalies: A study in the Railway Industry","volume":"105","author":"Ribeiro","year":"2016","journal-title":"Mach. Learn."},{"key":"ref_14","first-page":"6699313","article-title":"Sensors Anomaly Detection of Industrial Internet of Things Based on Isolated Forest Algorithm and Data Compression","volume":"2021","author":"Liu","year":"2021","journal-title":"Sci. Program."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Goh, J., Adepu, S., Tan, M., and Lee, Z.S. (2017, January 12\u201314). Anomaly detection in cyber physical systems using recurrent neural networks. Proceedings of the IEEE International Symposium on High Assurance Systems Engineering, Singapore.","DOI":"10.1109\/HASE.2017.36"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, D., Jin, B., Shi, L., Goh, J., and Ng, S.K. (2019, January 17\u201319). In Proceedings of the MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks BT\u2014Artificial Neural Networks and Machine Learning\u2014ICANN 2019: Text and Time Series. Munich, Germany.","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Garcia-Font, V., Garrigues, C., and Rif\u00e0-Pous, H. (2016). A comparative study of anomaly detection techniques for smart city wireless sensor networks. Sensors, 16.","DOI":"10.3390\/s16060868"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.neucom.2012.11.050","article-title":"Network anomaly detection with the restricted Boltzmann machine","volume":"122","author":"Fiore","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Luo, T., and Nagarajany, S.G. (2018, January 20\u201324). Distributed anomaly detection using autoencoder neural networks in WSN for IoT. Proceedings of the IEEE International Conference on Communications, Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422402"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1007\/s10694-020-01056-z","article-title":"A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems","volume":"57","author":"Abid","year":"2021","journal-title":"Fire Technol."},{"key":"ref_21","first-page":"311","article-title":"Use of ai techniques for residential fire detection in wireless sensor networks","volume":"475","author":"Bahrepour","year":"2009","journal-title":"CEUR Workshop Proc."},{"key":"ref_22","unstructured":"Iyer, V., Iyengar, S.S., Nandan, P., Garmiela, R.M., and Mandalika, M.B.S. (2011, January 21\u201327). Machine Learning and Dataming Algorithms for Predicting Accidental Small Forest Fires. Proceedings of the SENSORCOMM 2011: The Fifth International Conference on Sensor Technologies and Application, Porto, Portugal."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, X., Lu, X., and Leung, H. (2017, January 5\u20138). An Adaptive Threshold Deep Learning Method for Fire and Smoke Detection. Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8122904"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fonollosa, J., Sol\u00f3rzano, A., and Marco, S. (2018). Chemical sensor systems and associated algorithms for fire detection: A review. Sensors, 18.","DOI":"10.3390\/s18020553"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Alimenti, F., Roselli, L., and Bonafoni, S. (2016). Microwave radiometers for fire detection in trains: Theory and feasibility study. Sensors, 16.","DOI":"10.3390\/s16060906"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, P., and Zhao, W. (2020). Image fire detection algorithms based on convolutional neural networks. Case Stud. Therm. Eng., 19.","DOI":"10.1016\/j.csite.2020.100625"},{"key":"ref_27","unstructured":"Zheng, A., and Casari, A. (2018). Feature Engineering for Machine Learning, Taylor & Francis."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108101","DOI":"10.1016\/j.knosys.2021.108101","article-title":"Meta-features for meta-learning","volume":"240","author":"Rivolli","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_29","unstructured":"Huyen, C. (2022). Designing Machine Learning Systems, O\u2019Reilly."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4902\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:38:28Z","timestamp":1760125108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4902"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,19]]},"references-count":29,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104902"],"URL":"https:\/\/doi.org\/10.3390\/s23104902","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,19]]}}}