Evaluating Sensor Algorithms to Prevent Kitchen Cooktop Ignition and Ignore Normal Cooking

Published

September 18, 2019

Author(s)

Amy E. Mensch, Anthony P. Hamins, John Lu, Wai Cheong Tam

Abstract

Cooking equipment is involved in nearly half of home fires in the USA, with cooktop fires the leading cause of deaths and injuries in cooking-related fires [1]. While new electric-coil cooktops must pass the UL 858 [2] “abnormal cooking test,” which aims to prevent cooktop fires, there is no such requirement for older and other types of cooktops. In this study, the use of gas and particle sensors to provide early warning and/or stop cooktop ignition of foods and oils are considered in an effort to develop ways to reduce the risk of cooktop fires. Thus, the objective of this study is to develop and test the performance of sensor detection algorithms using threshold analysis and machine learning methods.

Conference Dates

September 17-18, 2019

Conference Location

Denver, CO

Conference Title

Suppression, Detection and Signaling Research and Applications Conference Proceedings (SUPDET 2019)

Pub Type

Conferences

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Keywords

cooktop ignition, ignition prevention, machine learning algorithm

Citation

Mensch, A. , Hamins, A. , Lu, J. and , W. (2019), Evaluating Sensor Algorithms to Prevent Kitchen Cooktop Ignition and Ignore Normal Cooking, Suppression, Detection and Signaling Research and Applications Conference Proceedings (SUPDET 2019), Denver, CO, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928709 (Accessed December 26, 2024)