Next Article in Journal
A Novell Hall Magnetometer Using Dynamic Offset Cancellation
Previous Article in Journal
Ultrasonic Coupled Passive Wireless Oscillating Sensor System
 
 
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Proceeding Paper

Improving Calibration of Chemical Gas Sensors for Fire Detection Using Small Scale Setups †

1
Department of Engineering Electronics, Universitat de Barcelona, Barcelona 08028, Spain
2
Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona 08028, Spain
*
Author to whom correspondence should be addressed.
† Presented at the Eurosensors 2017 Conference, Paris, France, 3–6 September 2017.
Proceedings 2017, 1(4), 453; https://doi.org/10.3390/proceedings1040453
Published: 9 August 2017
(This article belongs to the Proceedings of Proceedings of Eurosensors 2017, Paris, France, 3–6 September 2017)

Abstract

:
Chemical sensing may be better suited than conventional smoke-based detectors for the detection of certain type of fires, in particular in fires where smoke appears after gas emissions. However, chemical-based systems also respond to non-fire scenarios that also release volatiles. For this reason, discrimination models need to be trained under different fire and non-fire scenarios. This is usually performed in standard fire rooms, the access to which is very costly. In this work, we present a calibration model combining experiments from standard fire room and small-scale setup. Results show that the use of small-scale setup experiments improve the performance of the system.

1. Introduction

For some type of fires, volatiles are released before smoke [1]. Hence, fire detectors based on chemical sensing could provide a faster fire alarm than smoke-based detection systems [2]. The challenge, however, is the large number of situations (nuisances) that also generate volatiles and that may lead to false alarms [3,4]. In this way, chemical sensing systems for fire detection rely heavily on machine learning, signal processing and pattern recognition techniques to provide a reliable fire prediction [5,6,7]. To develop robust and reliable algorithms for fire detection, calibration and test datasets that include different fire types and nuisances experiments are fundamental [8].
The systems used to provide fire alarms should be tested in standard rooms. Chemical systems for fire detection also need to be exposed to the fire standard conditions. However, the availability of standard fire rooms to perform experiments is limited and expensive. For this reason, it would be desirable the acquisition of data at smaller scale setups that can be used in combination of data acquired at standard fire rooms to extend, at a moderate cost, the calibration dataset.
In this work, we present a calibration methodology for fire detection based on a Partial Least Squares Discriminant Analysis (PLS-DA). The model was trained using the combination of data experiments from standard fire room and data experiments from small-scale setup.

2. Experimental

2.1. Sensor Board

We built a multi-sensor system composed of a PID Sensor (PID-A1, Alphasense), a NDIR CO2 sensor (IRC-AT, Alphasense), a CO electrochemical sensor (CO-B4, Alphasense) and 8-MOX sensors (AS-MLK, AS-MLC, AS-MLX, AS-MLN; provided by AMS, two units of each, at 337 °C and 436 °C). Signals were acquired and stored locally at a sampling frequency of 10 Hz using a low-cost general-purpose microcontroller coupled to the required signal conditioning electronics.

2.2. Datasets

To assess this methodology we used two different datasets; (i) Large-scale and (ii) Small-scale dataset. To collect the first dataset, the sensing system was placed in the celling of a standard fire room while several experiments of fires and nuisance were performed.
The large-scale dataset includes open and smoldering fire experiments described in the EN-54 norm, other smoldering fire types and nuisance scenarios. Specifically, we performed two repetitions of: TF2 (smoldering), electrical fire (smoldering), PVC Fire (smoldering), PET Fire (smoldering), cables fire (smoldering), TF3 fire (smoldering), TF4 (open) and TF6 (open). The nuisance scenarios selected were based on the presentation of different products: air freshener, ethanol (96% purity), turpentine, vinegar, and gasoline. The total number of experiments included in the large-scale dataset is 25.
The small-scale experiments were performed in a 272-L chamber. Small-scale dataset includes: 4 repetitions of electrical fire, reduced TF2 and reduced TF3, and several nuisance scenarios: boiling water, air freshener, ethanol, rising temperature, and two commercial cleaning products (vinegar and floor cleaner). The total number of experiments included in the small-scale dataset is 32. The sensor array detects volatiles released in fire and nuisance experiments performed in both scenarios (small and large), though, signatures of fire and nuisances are different due to the different dimensions (Figure 1).

3. Methods and Results

The prediction models are based on Partial Least Squares Discriminant Analysis (PLS-DA). A double cross validation methodology was implemented. Internal validation was used to optimize the parameters of the model and external validation was used to assess the performance of the classifier. The first model was trained using repetitions of the large-scale dataset and is able to classify all the fire experiments. However, the model confused 8 nuisance experiments with fire, resulting in false alarms. Table 1 shows the confusion matrix after 12 iterations.
A second model was built adding to the calibration set the data captured in the small-scale setup. Along the 12 cross validation iterations, models classified 100% of the nuisance experiments and confused only the PVC fire experiment. Table 2 shows the confusion matrix

4. Conclusions

PLS-DA models were built to classify different fire scenarios and discriminate non-fire scenarios that may produce false alarms. Models that only used large-scale datasets in the calibration set showed poor specificity. Nevertheless, results indicated that the model trained with the combination of the small-scale and large-scale datasets is capable of rejecting all the nuisances and detecting most of the fires. Further work is needed to keep sensitivity to fires at higher levels when false alarm immunity is increased.

Acknowledgments

This work was partially funded by the Spanish MINECO program, under grants TEC2014-59229-R (SIGVOL) and PCIN-2013-195 (SENSIBLE), and from the EC’s JU program, grant 621272 (SAFESENS).and CERCA Programme/Generalitat de Catalunya. The authors are members of the research group 2014-SGR-1445. JF acknowledges the support of ACCIÓ, grant TECSPR15-1-0031. AS acknowledges the support of CONACYT grant 34610.

Conflicts of Interest

Authors declare no conflict of interest.

References

  1. Fleming, J.M. Smoke Detector Technology and the Investigation of Fatal Fires May 2000; Boston Fire Department: Boston, MA, USA, 2000. [Google Scholar]
  2. Grosshandler, W.L.; Good, M.L.; Prabhakar, A. A Review of Measurements and Candidate Signatures for Early Fire Detection; National Institute of Standards and Technology, Building and Fire Research Laboratory: Gaithersburg, MD, USA, 1995. [Google Scholar]
  3. Gutmacher, D.; Hoefer, U.; Wöllenstein, J. Gas sensor technologies for fire detection. Sens. Actuators B Chem. 2012, 175, 40–45. [Google Scholar] [CrossRef]
  4. McAvoy, T.J.; Milke, J.; Kunt, T.A. Using multivariate statistical methods to detect fires. Fire Technol. 1996, 32, 6–24. [Google Scholar] [CrossRef]
  5. Milke, J.A.; Hulcher, M.E.; Worrell, C.L.; Gottuk, D.T.; Williams, F.W. Investigation of multi-sensor algorithms for fire detection. Fire Technol. 2003, 39, 363–382. [Google Scholar] [CrossRef]
  6. Rose-Pehrsson, S.L.; Hart, S.J.; Street, T.T.; Tatem, P.A.; Williams, F.W.; Hammond, M.H.; Gottuk, D.T.; Wright, M.T.; Wong, J.T. Real-Time Probabilistic Neural Network Performance and Optimization for Fired Detection and Nuisance Alarm Rejection. In Proceedings of the 12th International Conference on Automatic Fire Detection, Gaithersburg, MD, USA, 25–28 March 2001. [Google Scholar]
  7. Reimann, P.; Schütze, A. Fire detection in coal mines based on semiconductor gas sensors. Sens. Rev. 2012, 32, 47–58. [Google Scholar] [CrossRef]
  8. Conrad, T.; Reimann, P.; Schütze, A. A hierarchical strategy for under-ground early fire detection based on a T-cycled semiconductor gas sensor. In Proceedings of the 2007 IEEE Sensors, Atlanta, GA, USA, 28–31 October 2007. [Google Scholar]
Figure 1. Signals captured for a TF3 experiment (smoldering cotton fire) in the small-scale set up (left) and in the fire room (right). Sensors detect volatiles released during the experiments, however, their reaction is different due to (mostly) the dimensions of the standard fire room.
Figure 1. Signals captured for a TF3 experiment (smoldering cotton fire) in the small-scale set up (left) and in the fire room (right). Sensors detect volatiles released during the experiments, however, their reaction is different due to (mostly) the dimensions of the standard fire room.
Proceedings 01 00453 g001
Table 1. Confusion matrix of the model when it is trained with data from standard fire room only. Although the system is able to detect all the fires, the number of false positive is unacceptable.
Table 1. Confusion matrix of the model when it is trained with data from standard fire room only. Although the system is able to detect all the fires, the number of false positive is unacceptable.
Fire AlarmNo Fire Alarm
Fire150
Nuisance82
Table 2. Confusion matrix of the model when it is trained with data from standard fire room and data acquired at smaller-scale setup. Additional measurements help to adjust alarm levels so none of the nuisances generates a false alarm.
Table 2. Confusion matrix of the model when it is trained with data from standard fire room and data acquired at smaller-scale setup. Additional measurements help to adjust alarm levels so none of the nuisances generates a false alarm.
Fire AlarmNo Fire Alarm
Fire141
Nuisance010
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Solórzano, A.; Fonollosa, J.; Marco, S. Improving Calibration of Chemical Gas Sensors for Fire Detection Using Small Scale Setups. Proceedings 2017, 1, 453. https://doi.org/10.3390/proceedings1040453

AMA Style

Solórzano A, Fonollosa J, Marco S. Improving Calibration of Chemical Gas Sensors for Fire Detection Using Small Scale Setups. Proceedings. 2017; 1(4):453. https://doi.org/10.3390/proceedings1040453

Chicago/Turabian Style

Solórzano, Ana, Jordi Fonollosa, and Santiago Marco. 2017. "Improving Calibration of Chemical Gas Sensors for Fire Detection Using Small Scale Setups" Proceedings 1, no. 4: 453. https://doi.org/10.3390/proceedings1040453

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop