False Fire Alarm Detection Using Data Mining Techniques

Fire can really be devastating to properties if improperly managed, it is due to this problem that the fire detection and alarm systems were sought for. However, traditional fire alarm system is based on a wiring network which have drawbacks and limitations such as inflexibility of the FDAS layout plan during building construction, and difficulties in renovation where the removal and relocation of traditional FDAS requires additional amount of work. To address these problems, a fire detection and alarm system that is based on wireless sensor network was developed. The FDAS is mainly composed of a fire detection node, a fire alarm node, and a fire alarm control panel. The wireless communication of the nodes was achieved using XBee as the wireless transceiver. The significant characteristic of XBee is the mesh routing protocol which helps the system to be more robust and flexible. The fire alarm control panel is a LabVIEW based program that utilizes the state machine architecture which follows the detect-evaluate-store algorithm. In evaluating the overall system, results illustrate the network of nodes adapts to its location and connects to other nodes whenever it is within their range. Furthermore, the average response time of each detection node with respect to the fire alarm control panel is below the standard of 10-second as stated by the National Fire Alarm and Signaling Code.

The fire detection system combines the simultaneous measurements of smoke, carbon monoxide, and carbon dioxide. The security of campus against intruders moving in laboratories, class rooms, staffrooms or washrooms. The fire alarm system consists of Fire detectors (with can be smoke detector, heat, or Infra-Red detectors), control unit and alarm system. A fire detection system is developed based on the simultaneous measurements of temperature and smoke. The fire detection system with the alarm algorithm detected fires that were not alarmed by smoke sensors, and alarmed in shorter times than smoke sensors operating alone. Previous fire detection algorithms used data from sensors for temperature, smoke, and combustion products. The smoke sensor alarms when the analog output signal exceeds or equal the threshold value. The node includes analog sensors to measure smoke, carbon monoxide (CO) and temperature. A fire alarm system should reliably and in a timely way notify building occupants about the presence of fire indicators, such as smoke or high temperatures.

The requirement of the government thru the Bureau of Fire Protection to install Fire Detection and Alarm Systems (FDAS) to high occupancy buildings or structures has raised the need of skilled workers to perform this task. To address this matter, Technical Skills Development Authority (TESDA) included in the Electrical Installation and Maintenance (EIM) NCII qualification the competency of FDAS installation, set-ups, and troubleshooting. With the inclusion of the FDAS in the said qualification a decline of passers was observed from July 2017 to December 2017. In response, the researcher developed a portable device to be utilized in facilitating a retentive means of teaching the required knowledge, skills and attitude of students in the wiring, installation and troubleshooting of FDAS and to encourage training institutions to incorporate this training device in their training programs and institution. This study evaluated the training device to determine its acceptability in premises of design, performance, and safety. The evaluation purposively involved seven senior high school faculty who were teaching electrical installation subjects in the Northeastern part of Leyte Province. A mean of 4.937 was obtained from the evaluation of the portable device, and affirmed that the FDAS Trainer is very acceptable to the evaluators. Having a very acceptable rating in the three criterions, it can be inferred that this portable trainer device can be used as an effective training device that can develop knowledge, skills, and attitude of students and trainees in electrical installation classes, auxiliary systems installation classes, and other allied electrical installation programs. Keywords: FDAS, Fire alarm, Fire detection and alarm systems, Trainer, Technology development

In fire alarm and monitoring system, fire detector is provided with great function. But traditional fire detector does not possess or be short of the ability to detect in early state fire. Because that signal of fire alarm and monitoring isn't alarmed in time and fire is controlled effective, to cause heavy losses by fire. In this paper, it is start that to analyze mechanism of fire occurred process and combine case of fire, multi-sensor fire detector is design. And the proposed fire detector may detect and recognizes signal of early fire in the early state of fire by photo-electricity, temperature. Acquisitive signal is sent to amplify and shape. And then shaped signal is introduced TMS320VC5402 DSP chip. Intelligent recognition program is embedded in the TMS320VC5402 DSP. The program embedded judges alarm signal. And the position of fire happed and the fire signal are messaged to host computer by bus means. Fire signal and fire alarm are confirmed in real time by this proposed fire detector. And ability of its sensitivity and reliability is advanced and enhanced.

In the occasion of outdoor warehouse and large scale indoor warehouse, the currently widely used infrared and smoke sensors for fire alarm may can not work properly for various kinds of interferences and noises, which is prone to error fire prediction or omission prediction and other problems. Based on the design of reliable flame recognition algorithm, the paper constructed practical fire alarm system based on multi-source information fusion. The system used time-division multiplexing technology to combine temperature sensor, photoelectric smoke detector and infrared CCD image detector into multi-source fire disaster detector group. It also utilized multi-source information fusion decision-making method to complete task of accurate prediction of fire level and alarm, which improves the reliability of fire alarm and decrease the false alarm rate.

A probabilistic occupant response model for fire emergencies

Performance characteristics and assessment of fire alarm system

With the widespread application of automatic fire alarm systems in various types of buildings, the problem of fire false alarms has gradually become prominent, which not only causes resource waste, but also may reduce users’ trust in the alarm system, thereby affecting the efficiency of emergency response in actual fires. According to data from a certain fire cloud platform, 99.85% of the suspected fires predicted by its system are false alarms. Although existing models can recognize most fire accidents, the accuracy of fire alarm recognition is only 0.15%, due to loose judgment logic, which still requires a large amount of manpower to verify alarms. This article analyzes a large amount of false alarm data and explores the main causes of false alarms, including environmental interference, equipment failure, and improper human operation. By using a fire dynamics simulator (FDS) to establish fire simulation models under different data settings, horizontal and vertical multi-scene fire simulation data are obtained. The study combines simulation and platform data to form a fire and false alarm dataset using a one-dimensional convolutional neural network (1D-CNN) and deep neural network (DNN) deep learning techniques to learn the deductive rules of the fire scene, establish a two-stage judgment model, and gradually, accurately, judge the results. By quantifying the precision, recall, and F1 score of the model, a deep learning model designed to accurately identify genuine fire alarms while filtering out false ones is proposed that can significantly reduce the false alarm rate. The results indicate that the model can identify 1705 false alarms out of 2255 highly suspected true alarms identified by existing systems in multiple practical scenarios and eliminate 75.61% of false positive alarms. On the premise of ensuring an authenticity recognition rate greater than 98%, the accuracy of fire alarm recognition increased from 0.15% to 28.85%, which will significantly reduce the workload of staff verifying alerts, and has good practical value.

Smoke and fire detection, fire alarm and fire fighting systems for the Washington Metropolitan Area Transit Authority (WMATA) Mass Rapid Transit System, including some alternate fire fighting system designs, are presented. A combined rate-of-rise/fixed temperature fire detector is used in areas where rapid temperature changes are abnormal. Fixed temperature fire detectors are used in the other areas requiring protection. The below grade passenger stations have a wet pipe standpipe system. This system also includes a dry pipe and siamese connection to increase the volume and pressure of the water above the nominal city water pressure. The aerial and surface stations have a dry pipe standpipe system. All passenger station standpipe systems provide water to a hose valve within each fire equipment cabinet and to each angle hose valve located under manhole covers on the platform. The train tunnels also have a dry standpipe system.

Evaluation of image fire detection algorithms based on image complexity

An accurate and fast fire smoke detection algorithm is urgently needed to solve the emergency linkage measures to prevent early fire spread and after the fire. In this paper, the depth matrix of motion difference is constructed for background modeling, the moving target is extracted using flame smoke motion characteristics, and the suspected area of flame smoke is obtained through corrosion and expansion. The unique characteristics of flame and smoke are then extracted, and the smoke flame recognition model is built using a BP neural network optimized by a genetic algorithm. The experimental results show that the algorithm can reliably eliminate the interference of moving vehicles and lights in the tunnel, detect flames and smoke, and generate an alarm, and that it can be applied to fire smoke detection in real-world scenes. Finally, the emergency linkage measures in the corresponding area are automatically initiated based on the position of the fire point via information interaction with the fire smoke detection and alarm system.

A real-time shipboard fire-detection system based on grey-fuzzy algorithms

The increasing availability of low-cost smart devices is bringing them to be used more and more in the smart home. However, the development of a smart home environment requires to take into account several aspects. First of all, designers must consider the end user (namely the person that actually uses the smart home), and not only the technology, at the center of any intervention. Another important aspect is the interaction between the smart home and the appliances that are already deployed in the same smart environment. Moreover, most of smart home solutions are static and do not allow end users to customize them according to their real needs and preferences. Finally, not all end users may possess the necessary knowledge and skills to customize a smart home, but someone else, such as an adult child or a caregiver, may be called on to carry out this task for them. In this paper, we analyze all these aspects that can influence the development and evolution of a smart home. We then propose a model supporting developers and software engineers to deploy and evaluate a smart home solution that adopts end-user development techniques. It is based on the International Classification of Functioning scale, which is used to characterize the person that is mainly going to live in the smart home and define a solution suitable to his/her needs.

Many countries use conventional fire alarm control panels (FACPs) and conventional detectors as fire alarm systems (FASs) because of economic advantages. This study analyzes several problems with conventional FASs and describes an advanced FAS, the traceable FAS, that we developed to solve these problems. The traceable FAS can trace the exact location of a fire, can detect multiple fires that occur simultaneously, and can be used with conventional detectors without disrupting the other detectors operation. The traceable FAS consists of a traceable FACP based on the conventional FACP and a traceable heat detector based on the rate-of-rise spot-type conventional heat detector. The new system offers traceability in addition to all the features of the conventional FAS. The traceable FACP has 40 zones; one zone can connect up to 20 traceable heat detectors. Thus, this FACP can connect up to 800 traceable heat detectors, all of which can detect and trace exact fire locations in sequence. Furthermore, the traceable FACP can be used with both traceable heat detectors and conventional heat detectors in the same zone. Even if one detector (either traceable heat detector or conventional heat detector) detects a fire first, other traceable heat detectors in the same zone can also detect it and display the results on the traceable FACP. Prototypes of the traceable FACP and traceable heat detector (rate-of-rise spot-type) have officially received model approvals for use in South Korea. Conventional FASs can be upgraded to traceable FASs simply by changing to the traceable FACP and traceable heat detectors, without any rewiring.

The proper design of new or renovated laboratories is critical to the success of the research or teaching to be conducted in the laboratory as well as the safety and health of the occupants. The factors that can lead to the successful design, construction and operation of a laboratory are having the relevant disciplines represented and engaged from the beginning and following a defined process to assure that all potential issues are addressed. From a health and safety perspective the key design issues are the layout of the space, adequate contaminant control ventilation as well as ventilation for comfort control and the proper safety features such as emergency eye washes and showers, fire detection, alarm and suppression systems, and electrical and mechanical safety systems. Applicable regulatory requirements as well as building codes and best practices must also be considered. This chapter describes the process to follow as well as key issues that should be addressed depending on the type of laboratory and the materials, equipment, and procedures to be employed in the space. It also provides additional resources that can be consulted for guidance.