Refining Fire Safety Management through IoT Data Analysis

Introduction

The realm of fire safety management within structures grapples with numerous obstacles, such as the centralization and transparency of maintenance task oversight, analysis and prediction of water pressure, along with monitoring the firefighting water supply system. Furthermore, defining IC data and parameters, enhancing fire safety measures, and understanding the correlation between fire risk factors are areas that necessitate further investigation. Manual data collection proves to be labor-intensive and prone to inaccuracies while current methodologies fail to consider the interconnection among multiple IoT sensors. This examination aims to tackle these issues by introducing an innovative framework that leverages IoT data analysis for improved fire safety management in buildings.

Primary Issue

The primary issue in managing fire safety is a lack of centralized and transparent maintenance task oversight which impedes effective surveillance and upkeep of firefighting facilities. This leads to potential hazards escalating the likelihood of system failures. Moreover, existing techniques for analyzing and predicting water pressure within structures as well as monitoring firefighting water supply systems have limitations concerning accuracy and resource requirements. Additionally, correlations between various fire risk factors often go unexamined resulting in incomplete risk evaluations.

Solution:

To address these concerns an innovative framework leveraging IoT data analysis is proposed for enhanced building fire safety management. The framework comprises two functional blocks: short-term assessment of fire risks along maintenance & management of firefighting facilities.

  1. Short-term Fire Risk Assessment:
    The initial block focuses on executing a short-term evaluation of potential risks associated with fires based on state-of-the-art IoT data.
    This evaluation involves three sub-functional algorithms:

  • Operational Status Evaluation: Real-time detection mechanism for identifying incidents related to fires or sensor malfunctions.

  • Maintenance Evaluation: Identifies faulty sensors based on state-of-the-art collected over a specific timeframe.

  • Rectification Evaluation: Determines whether faults identified during maintenance have been rectified successfully.

By amalgamating results from these algorithms, each building's fire safety can be accurately assessed, enabling swift action and risk mitigation.

  1. Maintenance and Management of Firefighting Facilities:

The second block aims to enhance the maintenance and management of firefighting facilities. It addresses the limitations of existing methods by considering both temporal and spatial information from multiple IoT sensors. Prognostics and Health Management (PHM) algorithms are utilized to detect faults and forecast potential risks. These algorithms can analyze analog data from single or multiple sensors, providing a comprehensive understanding of the system's health.

To further refine this framework, future work should focus on:

  • Developing a PHM algorithm that automatically detects IC data from IoT sensors while adjusting parameter values.

  • Incorporating knowledge-driven maintenance methodologies for enhancing PHM algorithm performance.

  • Validating the proposed framework through more applications along with performance evaluations.

Conclusion:

The proposed framework provides an effective solution to challenges faced in fire safety management by leveraging IoT data analysis. By integrating short-term fire risk assessment with maintenance & management of firefighting facilities, it enhances the overall building's fire safety management. Future research and investigations should concentrate on advancing methodologies while incorporating more sophisticated techniques for improving reliability as well as effectiveness within this framework.