Early fire detection based on gas sensor arrays: Multivariate calibration and validation
Por:
Solorzano, A, Eichmann, J, Fernandez, L, Ziems, B, Jimenez-Soto, JM, Marco, S and Fonollosa J
Publicada:
1 feb 2022
Ahead of Print:
1 nov 2021
Resumen:
Smoldering fires are characterized by the production of early gas emissions that can include high levels of CO and Volatile Organic Compounds (VOCs) due to pyrolysis or thermal degradation. Nowadays, standalone CO sensors, smoke detectors, or a combination of these, are standard components for fire alarm systems. While gas sensor arrays together with pattern recognition techniques are a valuable alternative for early fire detection, in practice they have certain drawbacks-they can detect early gas emissions, but can show low immunity to nuisances, and sensor time drift can render calibration models obsolete. In this work, we explore the performance of a gas sensor array for detecting smoldering and plastic fires while ensuring the rejection of a set of nuisances. We conducted variety of fire and nuisance experiments in a validated standard fire room (240 m(3)). Using PLS-DA and SVM, we evaluate the performance of different multivariate calibration models for this dataset. We show that calibration models remain predictive after several months, but perfect performance is not achieved. For example, 4 months after calibration, a PLS-DA model provides 100% specificity and 85% sensitivity since the system has difficulties in detecting plastic fires, whose signatures are close to nuisance scenarios. Nevertheless, our results show that systems based on gas sensor arrays are able to provide faster fire alarm response than conventional smoke-based fire alarms. We also propose the use of small-scale fire experiments to increase the number of calibration conditions at a reduced cost. Our results show that this is an effective way to increase the performance of the model, even when evaluated on a standard fire room. Finally, the acquired datasets are made publicly available to the community (doi: 10.5281/zenodo.5643074).
Filiaciones:
Solorzano, A:
Minimax Viking Res & Dev GmbH, Ind Str 10-12, D-23840 Bad Oldesloe, Germany
Barcelona Inst Sci & Technol, Signal & Informat Proc Sensing Syst, Inst Bioengn Catalonia IBEC, Baldiri Reixac 10-12, Barcelona 08028, Spain
Univ Barcelona, Dept Elect & Biomed Engn, Marti I Franques 1, Barcelona 08028, Spain
Eichmann, J:
Minimax Viking Res & Dev GmbH, Ind Str 10-12, D-23840 Bad Oldesloe, Germany
Fernandez, L:
Barcelona Inst Sci & Technol, Signal & Informat Proc Sensing Syst, Inst Bioengn Catalonia IBEC, Baldiri Reixac 10-12, Barcelona 08028, Spain
Univ Barcelona, Dept Elect & Biomed Engn, Marti I Franques 1, Barcelona 08028, Spain
Ziems, B:
Minimax Viking Res & Dev GmbH, Ind Str 10-12, D-23840 Bad Oldesloe, Germany
Jimenez-Soto, JM:
Barcelona Inst Sci & Technol, Signal & Informat Proc Sensing Syst, Inst Bioengn Catalonia IBEC, Baldiri Reixac 10-12, Barcelona 08028, Spain
Marco, S:
Barcelona Inst Sci & Technol, Signal & Informat Proc Sensing Syst, Inst Bioengn Catalonia IBEC, Baldiri Reixac 10-12, Barcelona 08028, Spain
Univ Barcelona, Dept Elect & Biomed Engn, Marti I Franques 1, Barcelona 08028, Spain
Fonollosa J:
Univ Politecn Cataluna, B2SLab, Dept Engn Sistemes Automat & Informat Ind, Barcelona 08028, Spain
Networking Biomed Res Ctr Bioengn Biomat & Nanome, Madrid, Spain
Inst Recerca St Joan Deu, Esplugas de Llobregat 08950, Spain
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