Cooking equipment is involved in nearly half of home fires in the United States, with cooktop fires the leading cause of deaths and injuries in cooking-related fires. In this study, we evaluate 16 electrochemical, optical, temperature and humidity sensors, placed in the cooktop exhaust duct, for use in predicting and preventing cooktop ignition. The sensors were evaluated in a series of 60 experiments conducted in a mock kitchen. Experiments covered a broad range of conditions, including both unattended cooking and normal cooking scenarios, where 39 experiments led to auto-ignition. The experiments involved a variety of cooking oils and foods and were conducted using either an electric coil cooktop, gas-fueled cooktop, or electric oven. The sensor data collected in the experiments were used in two types of analysis, threshold analysis and neural-network analysis, to estimate the performance of the sensors for predicting ignition and ignoring normal cooking conditions. The combined information from multiple sensors was evaluated in sensor ratios with threshold analysis, and in the neural-network models developed using selected pairs of sensor inputs. Some of the multiple-sensor cases performed as well as or better than the individual sensor thresholds and individual sensor models. Consistently across threshold and machine learning analysis, the best performing sensor was the sensor measuring volatile organic compounds. This sensor was also included in all of the best performing sensor ratios and machine learning models.

在美国，将近一半的家庭火灾涉及烹饪设备，而炉灶式火灾是与烹饪相关的火灾中死亡和受伤的主要原因。在这项研究中，我们评估了放置在炉灶台排气管中的16个电化学，光学，温度和湿度传感器，用于预测和防止炉灶台着火。在模拟厨房中进行了60项实验，对传感器进行了评估。实验涵盖了广泛的条件，包括无人值守烹饪和正常烹饪场景，其中39个实验导致了自动点火。实验涉及多种食用油和食物，并使用电炉灶，燃气灶具或电烤箱进行。实验中收集的传感器数据用于两种类型的分析，阈值分析和神经网络分析，以评估用于预测点火和忽略正常烹饪条件的传感器的性能。来自多个传感器的组合信息通过阈值分析在传感器比率中进行评估，并在使用选定的传感器输入对开发的神经网络模型中进行评估。一些多传感器案例的性能与单独的传感器阈值和单独的传感器模型一样好，甚至更好。在阈值和机器学习分析中，性能最佳的传感器始终是测量挥发性有机化合物的传感器。该传感器还包括在所有性能最佳的传感器比率和机器学习模型中。来自多个传感器的组合信息通过阈值分析在传感器比率中进行评估，并在使用选定的传感器输入对开发的神经网络模型中进行评估。一些多传感器案例的性能与单独的传感器阈值和单独的传感器模型一样好，甚至更好。在阈值和机器学习分析中，性能最佳的传感器始终是测量挥发性有机化合物的传感器。该传感器还包括在所有性能最佳的传感器比率和机器学习模型中。来自多个传感器的组合信息通过阈值分析在传感器比率中进行评估，并在使用选定的传感器输入对开发的神经网络模型中进行评估。一些多传感器案例的性能与单独的传感器阈值和单独的传感器模型一样好，甚至更好。在阈值和机器学习分析中，性能最佳的传感器始终是测量挥发性有机化合物的传感器。该传感器还包括在所有性能最佳的传感器比率和机器学习模型中。在阈值和机器学习分析中，性能最佳的传感器始终是测量挥发性有机化合物的传感器。该传感器还包括在所有性能最佳的传感器比率和机器学习模型中。在阈值和机器学习分析中，性能最佳的传感器始终是测量挥发性有机化合物的传感器。该传感器还包括在所有性能最佳的传感器比率和机器学习模型中。