Arduino based Fire Detection and Alarm System Using Smoke Sensor

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 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

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.

In recent years the complexity and cost of Fire and Gas detection systems have increased rapidly with no measurable reduction in fatality rates or other losses. A principal reason for this disappointing outcome despite substantial investment is that systems designs have lost the focus of what they are trying to achieve. Another reason worth exploring is that investment in other areas may result in greater benefits. For instance, diverting funds to preventing lifting accidents, helicopter failures and materials handling incidents, could result in greater OVERALL benefit since these account for a larger proportion of risks. Nevertheless, Fire and Gas detections systems have a major role to play in preventing escalation, and this paper shows how they can be made more efficient in both cost and performance by combining four methods: targeting perceived hazards,integration from concept stage with other safety designs,control of design with effective performance specification, andcontrol of modifications throughout the system's life.

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.

A probabilistic occupant response model for fire emergencies

Fire detection and alarm systems are recognized as key features of a building’s fire prevention and protection strategy. This chapter presents a systematic technique to be used by fire protection engineers in the design and analysis of detection and alarm systems. The majority of discussion is directed toward systems used in buildings. However, many of the techniques and procedures also apply to systems used to protect planes, ships, outside storage yards, and other nonbuilding environments.

The paper starts with the introduction of the basic requirements of classical intrusion detection and alarm systems and fire detection systems. The drawbacks of such conventional systems are highlighted. Techniques of computer vision are employed to remove the drawbacks and at the same time increase the reliability and response rate of the systems. For security and low level fire detection, a fuzzy logic based image comparison algorithm is deemed adequate. In order to confirm the existence of fire or smoke, techniques related to optical flow are employed as high level fire or smoke detection, which generate a velocity field for the image so that the decision can be judged by using fizzy logic. Details of implementation and some experimental results have been included in the paper for illustration.

This paper presents some fundamental factors that influence the reliability of fire and gas (F&G) alarm systems by incorporating human factor's principles. As compared, standard alarm and the safety‐related alarm will show two different values of IPF given in process hazard analysis (PHA) studies. In this study, fault tree diagram has been used to calculate the process flow diagram (PFD) value, to identify which alarm type could meet the standard of safety integrity level (SIL1) for F&G system for a single train process. Comparison of both alarms is done for three types of selected detectors; flammable gas detectors, flame detectors, and toxic gas detectors. It has been identified that standard alarm for all type of detectors does not meet the SIL1 requirement. Compared to safety‐related alarm, all detectors are able to meet the requirement of SIL1. Since the greater IPF will be given for safety‐related alarm, it is crucial to study the holistic factor that could maintain the system with good efficiency and performance. The human factor has been identified as the most critical element for safety‐related alarm. A complete coordinated approach is needed to ensure the performance and efficiency of the F&G alarm system can be maintained while process plants are able to be safely operated within as low as reasonably achievable region.

The functioning fire protection system in the State Forests, which covers 76.9% of the forest area in the country, is constantly improved thanks to close cooperation with the Forest Research Institute. The results of the research conducted by the Institute are thus implemented in forestry practice, examples of which are presented in this article. The basis for planning and organizing rescue operations is the assessment of potential forest fire risk, which is carried out using the method of classifying forest areas to the forest fire risk category. It is determined for the forest district (average area 17500 ha) for 10 years on the basis of the frequency of fires, forest stand, climatic and anthropogenic conditions according to the formulas developed for each of the factors. The category determines the distribution of funds for fire protection and the method of forest preparation in the event of a fire, concerning fire detection, communication and alarm systems, the density of fire access roads and water supply points, and fire extinguishing equipment. The macroscopic method of forest fire risk categorization is complemented by the method of forest stand flammability classes, which determines their susceptibility to fire due to the presence of flammable material. It is based on the forest habitat type and soil cover type and is determined at the level of separation (average area 3 ha), with the possibility of generalization to the division (20 ha) and forestry (1300 ha). Both methods are used to map the potential fire risk of forests in Poland. The method of determining the degree of forest fire risk on the basis of air temperature and relative humidity, precipitation and direct measurements of moisture content of pine litter (Pinus sylvestris) is used to assess the dynamic forest fire risk, shaped by weather conditions. Determining the degree of risk is performed from the 1st of March to the 30th of September, daily at 9.00 and 13.00 in 60 forecast zones with the use of an automated network of 145 meteorological measuring points. Information on the degree of risk determines the operational readiness of ground and air forces. In the event of a fire and determination of its forest address, we can download from the Information System of the State Forests, data on the flammability class of the forest stand and the characteristics of the flammable material (type, fuel load), using the developed fuel models for 7 types of soil cover, the fires of which constitute nearly 90 % of all events. The possibility of a soil cover fire turning into a crown fire is determined based on the developed algorithms, taking into account the age of the forest stand, habitat and stand conditions, meteorological conditions and the duration of the fire. Organizing a firefighting action, including in particular having the right amount of forces and resources to extinguish a fire with the smallest possible area, can be supported by the application Model of a forest fire, which the forest service has at its disposal.

This paper proposes a design and experimentally implements of a multimodal machine learning (ML)-based indoor fire detection and alarm system which combines image processing and Internet-of-Things (IoT)-based environmental sensing for mitigating false alarm. The proposed system consists of two fire ML-based detecting models: the image processing model used for detecting a fire based on fire images captured by a camera, a sensing data processing model used for predicting a fire based on room environmental data. Additionally, a communication module (i.e. 4G/Wi-Fi) is equipped to use for interacting with the home-owner and the fire station to get a confirmation before activating any further actions. In the proposed model, detected results of both abovementioned detecting models are combined to enhance the reliability of the final decision (i.e. whether it is a true fire or false fire). A true fire is recognized when two detecting modules both detected that it is a fire. Otherwise, when only one detecting module recognized that it is a false fire, it is recognized as uncertain. In this case, it needs to be confirmed by the home-owner based on fire images captured by the camera and sent over the communication module. If it is recognized as a true fire, a true alarm is raised and an alarm message containing house’s location information is sent to the fire station for activating the emergency case of fire. Otherwise, the fire is recognized as a false alarm and then no action is taken. The numerical and experimental results show that the proposed system achieves a high accuracy of fire detection and avoids false alarms.

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.

The product that is presented is a fire detection system based on small wireless devices that have been the sensors. In this work, a fire alarm system will be developed that will include several hardware elements: a circuit that will act as a wireless sensor, a sensor that will act as coordinator of the sensor network and communication bridge with the application in Ubidots. Regarding the software, an application will be developed for the sensor that will control the values of the temperature and smoke sensors.

In the era of smart home technology, early warning systems and emergency services are inevitable. To make smart homes safer, early fire alarm systems can play a significant role. Smart homes usually utilize communication, sensors, actuators, and other technologies to provide a safe and smart environment. This research work introduced a model for the fire alarm system and designed a fire alarm detection (FAD) simulator to produce a synthetic dataset. The designed simulator utilizes a variety of sensors (temperature, gas, and humidity) to simulate fire alarm scenarios based on real-world data. The produced data is investigated and analyzed to classify the possible fire behaviors based on key assumptions taken from real-world scenarios. Different classification models are used to determine an optimal classifier for fire detection. The proposed technique can identify the false alarms based on parameters like temperature, smoke, and gas values of different sensors embedded in a fire alarm detection simulator.

77% of fatal fires in Spain take place in residential buildings, and more than 60% of deaths are people over 65 years old. The collection of fire loss data in Spain is poorly regulated and it does not have homogeneity. This makes it extremely difficult to study the fire problem and the effectiveness of different safety strategies in general, and more specifically with relation to vulnerable groups, such as the elderly. As first step to assess the measures aimed to reduce mortality in residential buildings, a methodology for collecting fire data has been developed, based on information extracted from the media. Information on 289 fatal dwelling fires has been systematically analyzed in order to identify the prevention and protection strategies more effective to prevent fire deaths. Among the identified measures, they stand out the installation of fire detection and alarm systems in residential buildings, which is not required by law at the present time, and the installation of automatic extinguishing systems, such as household sprinklers. The use of safe heating systems, avoiding old and bad-maintained portable heaters, is a particularly useful prevention strategy for the elderly people.