Abstract This paper 1 presents a study to examine the potential use of machine learning models to build a real-time detection algorithm for prevention of kitchen cooktop fires. Sixteen sets of time-dependent sensor signals were obtained from 60 normal/ignition cooking experiments. A total of 200,000 data instances are documented and analyzed. The raw data are preprocessed. Selected features are generated for time series data focusing on real-time detection applications. Utilizing the leave-one-out cross validation method, three machine learning models are built and tested. Parametric studies are carried out to understand the diversity, volume, and tendency of the data. Given the current dataset, the detection algorithm based on Support Vector Machine (SVM) provides the most reliable prediction (with an overall accuracy of 96.9%) on pre-ignition conditions. Analyses indicate that using a multi-step approach can further improve overall prediction accuracy. The development of an accurate detection algorithm can provide reliable feedback to intercept ignition of unattended cooking and help reduce fire losses.

中文翻译：

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使用检测和多步机器学习算法防止灶具着火

摘要 本文 1 提出了一项研究，以检查机器学习模型在构建实时检测算法以预防厨房灶具火灾方面的潜在用途。从 60 次正常/点火烹饪实验中获得了 16 组与时间相关的传感器信号。总共记录和分析了 200,000 个数据实例。原始数据经过预处理。为专注于实时检测应用的时间序列数据生成选定的特征。利用留一法交叉验证方法，构建并测试了三个机器学习模型。进行参数研究以了解数据的多样性、数量和趋势。给定当前数据集，基于支持向量机 (SVM) 的检测算法提供了最可靠的预测（总体准确率为 96。9%) 在预燃条件下。分析表明，使用多步方法可以进一步提高整体预测精度。开发准确的检测算法可以提供可靠的反馈，以拦截无人看管的烹饪点火并有助于减少火灾损失。