Indoor Fire Dataset with Distributed Multi-Sensor Nodes (Industrial Hall)

Published: 16 September 2024| Version 1 | DOI: 10.17632/yghykzm4km.1
Contributor:
Pascal V

Description

The dataset contains 4 incipient fire scenarios (wood, candles, cable, lunts) along with 8 different nuisance scenarios (smoke from a fog machine, deodorant, ethanol, CO release, exhaust gases, green waste, welding, and abrasive cutting) carried out in a (10 x 22 x 8)m^3 industrial hall without ventilation. Each scenario was repeated 3 times in random with background sequences in between to reduce the influence of prehistory. The dataset consists of 248,502 rows and 18 columns and is structures as a continuous multivariate time series. Each row represents the sensor measurements (CO2, CO, H2, humidity, particulate matter of different sizes, air temperature, VOC and UV) from a unique sensor node position ("Sensor_ID") in the industrial hall at a specific timestamp. The columns correspond to the sensor measurements and include additional labels: a scenario-specific label ("scenario_label"), a binary label ("anomaly_label") distinguishing between "Normal" (background) and "Anomaly" (fire or nuisance scenario), a "fire_label" intersecting the anomalies into fire-relevant or non-fire-relevant anomalies and a progress label ("progress_label") that allows for dividing the events into sub-sequences based on ongoing physical sub-processes. The "Sensor_ID" column can be utilized to access data from different sensor node positions.

Files

Institutions

Otto von Guericke Universitat Magdeburg

Categories

Data Mining, Machine Learning, Time Series Prediction, Multivariate Analysis, Data Fusion Multiple Sensor, Time Series, Application of Sensors, Fire Detection, Stream

Funding

Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie

13N15415

Licence