Research on a multiparameter fire detection method for aircraft cargo compartment based on an improved self-attention mechanism

With the rapid advancement of the aviation industry, ensuring aircraft safety, particularly in sensitive areas like cargo holds, is of paramount importance. Fires in aircraft cargo can be triggered by various factors, such as electrical malfunctions, hazardous materials, or environmental conditions, and pose significant threats to passengers and crew. Given the growing complexity of fire detection in these confined spaces, more reliable and accurate fire detection methods are urgently needed. Traditional fire detection systems, which primarily depend on single-sensor technologies, like smoke or heat detectors, have long been criticized for their high false alarm rates and limited accuracy. These deficiencies often result in delayed responses or unnecessary interventions, which ultimately compromise operational safety and efficiency. Therefore, this study aims to develop an innovative fire detection system that can overcome the limitations of conventional methods while meeting the advanced safety standards of modern aviation.

To tackle these challenges, this research introduces an improved multiparameter fire detection method leveraging an advanced self-attention mechanism within the Transformer model architecture. The approach integrates data from multiple sensors, including carbon monoxide, smoke, humidity, and temperature sensors, to capture a wide range of environmental parameters in aircraft cargo holds. Data are gathered by simulating realistic fire scenarios within a laboratory setting, ensuring that the system is trained on diverse datasets that reflect the unpredictable nature of fire development in cargo spaces. The core of the proposed method is a Transformer-based model that incorporates two key innovations: local attention mechanism and multiscale feature extraction. The local attention mechanism addresses the computational complexity of processing long sequences of input data by dividing the data into smaller, manageable windows. This allows the model to focus on localized features without the burden of analyzing the entire sequence at once, making it more efficient and suitable for real-time applications. Furthermore, the multiscale feature extraction module processes data in parallel across different time windows, capturing short-term fluctuations and long-term trends, which is crucial for detecting gradual fires, such as slow-burning or smoldering fires, that traditional systems may miss.

The proposed method was rigorously evaluated through a series of experiments on a fire detection dataset designed to mimic real-world conditions in aircraft cargo holds. A range of hyperparameters, including sequence lengths, activation functions, dropout rates, and optimizers, was tested to fine-tune model classification performance. Results revealed that the optimized model significantly outperformed traditional approaches, such as convolutional neural networks, recurrent neural networks, and long short-term memory networks, in terms of classification accuracy, particularly under challenging conditions involving noisy or incomplete sensor data. The model excelled at distinguishing between fire and non-fire events, showcasing its superior ability to handle real-world fire scenarios. Moreover, the Transformer's intrinsic parallel computing capability reduced training times, making it a practical solution for time-sensitive fire detection applications in aviation.

This study presents a novel multiparameter fire detection system that integrates an improved self-attention mechanism with local attention and multiscale feature extraction, offering several advantages over traditional models. The proposed method achieves higher accuracy, lower computational complexity, and faster training times, making it highly suitable for deployment in aircraft cargo hold fire detection systems. The promising results from the laboratory-based experiments suggest that this method can be readily adapted to real-world operational settings. Future research will focus on further validating the model's performance in live environments, aiming to extend its applicability to other safety-critical domains beyond aviation, such as industrial safety and transportation systems.