An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/10/4902 |
| _version_ | 1797598335428722688 |
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| author | Emanuel Sousa Tomé Rita P. Ribeiro Inês Dutra Arlete Rodrigues |
| author_facet | Emanuel Sousa Tomé Rita P. Ribeiro Inês Dutra Arlete Rodrigues |
| author_sort | Emanuel Sousa Tomé |
| collection | DOAJ |
| description | 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’ 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’ results highlighted possible reasons and potential improvements to address this problem better. These findings can provide valuable insights for future research in this area. |
| first_indexed | 2024-03-11T03:20:49Z |
| format | Article |
| id | doaj.art-cef1846c6ef34b4aac1a8d4cc6777f10 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:49Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cef1846c6ef34b4aac1a8d4cc6777f102023-11-18T03:14:18ZengMDPI AGSensors1424-82202023-05-012310490210.3390/s23104902An Online Anomaly Detection Approach for Fault Detection on Fire Alarm SystemsEmanuel Sousa Tomé0Rita P. Ribeiro1Inês Dutra2Arlete Rodrigues3Computer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, PortugalComputer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, PortugalComputer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, PortugalBosch Security Systems, 3880-728 Ovar, PortugalThe 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’ 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’ results highlighted possible reasons and potential improvements to address this problem better. These findings can provide valuable insights for future research in this area.https://www.mdpi.com/1424-8220/23/10/4902predictive maintenanceindustry 4.0machine learningbig datadata streamstime series |
| spellingShingle | Emanuel Sousa Tomé Rita P. Ribeiro Inês Dutra Arlete Rodrigues An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems Sensors predictive maintenance industry 4.0 machine learning big data data streams time series |
| title | An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems |
| title_full | An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems |
| title_fullStr | An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems |
| title_full_unstemmed | An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems |
| title_short | An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems |
| title_sort | online anomaly detection approach for fault detection on fire alarm systems |
| topic | predictive maintenance industry 4.0 machine learning big data data streams time series |
| url | https://www.mdpi.com/1424-8220/23/10/4902 |
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