Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

Abstract

High-sensitivity early fire detection is an essential prerequisite to intelligent building safety. However, due to the small changes and erratic fluctuations in environmental parameters in the initial combustion phase, it is always a challenging task. To address this challenge, this paper proposes a hybrid feature fusion-based high-sensitivity early fire detection and warning method for in-building environments. More specifically, the temperature, smoke concentration, and carbon monoxide concentration were first selected as the main distinguishing attributes to indicate an in-building fire. Secondly, the propagation neural network (BPNN) and the least squares support vector machine (LSSVM) were employed to achieve the hybrid feature fusion. In addition, the genetic algorithm (GA) and particle swarm optimization (PSO) were also introduced to optimize the BPNN and the LSSVM, respectively. After that, the outputs of the GA-BPNN and the PSO-LSSVM were fused to make a final decision by means of the D-S evidence theory, achieving a highly sensitive and reliable early fire detection and warning system. Finally, an early fire warning system was developed, and the experimental results show that the proposed method can effectively detect an early fire with an accuracy of more than 96% for different types and regions of fire, including polyurethane foam fire, alcohol fire, beech wood smolder, and cotton woven fabric smolder.

Simulation-Based Engineering of Heterogeneous Collaborative Systems—A Novel Conceptual Framework

“…Security, safety, and access control General aspects of safety and security in intelligent habitats [47,48], indoor and outdoor early fire prediction [49], detection, and monitoring [50].…”

Prediction Model of Residual Current Based on Grey Association and Neural Network

“…By this technique, the parameters detected by various sensors, such as temperature, smoke concentration, and carbon monoxide levels, are synthesized to evaluate the potential and intensity of fires. (4)(5)(6)(7) Methods and algorithms pertinent to this topic have been proposed by several scholars. An early warning algorithm for indoor fires, based on a backpropagation neural network and integrated temperature, smoke, and carbon monoxide data to estimate fire likelihood, was introduced.…”

Intelligent Soft Sensors

“…By cleverly merging visual data with traditional time series process data, the method achieves significant improvements in predicting complicated quality variables such as FeO content. In the area of safety and infrastructure, research on a highly sensitive fire detection and early-warning system [ 11 ] highlights the value of fusing multiple fire indicators for timely and effective fire detection in buildings. This hybrid feature-fusion-based strategy represents potential for much safer smart building systems.…”