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Key Topics in Auditory Warnings
Published in Neville A. Stanton, Judy Edworthy, Human Factors in Auditory Warnings, 2019
Neville A. Stanton, Judy Edworthy
There are many contextual issues surrounding alarm and warning design and implementation, not least of which is the fundamental issue of false alarms. Bliss considers this issue, showing that people match their response rate to alarm reliability. He also shows that there are interaction effects between false alarms, alarm criticality and task importance. Highly critical warnings were responded to even if the false alarm rate was quite high, independently of task importance. This demonstrates that people are able to adapt quite quickly to the environmental context in which they find themselves. Stanton and Edworthy (Chapter 6) also suggest that the context may have an important influence upon what people expect the warning sound to be, particularly the environment in which the sounds are learnt.
Analysis of 985 fire incidents related to oil- and gas production on the Norwegian continental shelf
Published in Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, Safety and Reliability – Safe Societies in a Changing World, 2018
C. Sesseng, K. Storesund, A. Steen-Hansen
The next main causes for false alarms are technical errors and malfunctioning detection systems. At the same time, compared to other barrier elements, fire detection systems have the lowest failure rate when tested. Each year, some 50,000 tests of fire detectors are performed on offshore facilities in the Norwegian sector, and since the beginning of the reporting of these tests in 2002 the mean fail rate has been declining. In 2002, around 0.9% of the tested detectors failed the tests, whereas only 0.1% failed in 2015 (Petroleum Safety Authority Norway, 2016, 2015, 2010; Vinnem, 2010). The trend is positive, and it should therefore be a continued focus on maintenance and testing.
Security management
Published in Michael L. Madigan, First Responders Handbook, 2017
With any kind of alarm, the need exists to balance between, on the one hand, the danger of false alarms (called “false positives”)—the signal going off in the absence of a problem—and on the other hand, failing to signal an actual problem (called a “false negative”). False alarms can be an expensive waste of resources and can even be dangerous. For example, false alarms of a fire can waste manpower, making firefighters unavailable for a real fire, and risk injury to them and others as the fire engines race to the alleged fire’s location.
A state-of-the-art review on artificial intelligence for Smart Buildings
Published in Intelligent Buildings International, 2021
More recently, Mahdipour and Dadkhah (2014) investigated the use of various intelligent technologies for automatic fire detection. These were divided into four sections: fire detection; reduction in false alarm systems; fire data analysis; and fire prediction. The authors looked at a wide range of technologies including image processing, video processing, computer vision, wireless sensor networks, statistics, LIDAR (light detection and ranging), and intelligent techniques that include ANN and fuzzy rule-based systems. The study found favourable performance from fire detection systems in buildings that used ION based smoke detectors in combination with photo, temperature, and CO gas monitoring devices as input sensors for classifier algorithms such as NNs. Intelligent systems such as ANN and FL were determined to be more effective at reducing false alarms.
Pre-ignition detection and early fire detection in mining vehicles
Published in Mining Technology, 2021
Flame detection takes advantage of the electromagnetic radiation from the flames and will therefore not provide any pre-ignition detection. A free line of sight is crucial for the operability of the flame detector and any obstacle in the line of sight or obscuration on the lens will delay or even prevent the detection. The detector will also have to be fairly well aimed at the potential fire source, preventing delays in the detection. As opposed to heat detectors, flame detectors are not affected by the airflow to any great extent except regarding the tilting of flames. Potential causes of false alarms are hot objects and flashes of light, but technical solutions are available to eliminate the error sources. Flame detectors could for example be an option in engine compartments, aimed at detecting rapidly growing, flaming fires such as spray fires.
PriMa: a prescriptive maintenance model for cyber-physical production systems
Published in International Journal of Computer Integrated Manufacturing, 2019
Fazel Ansari, Robert Glawar, Tanja Nemeth
Applying PriMa in an industrial use-case results in demonstrating and verifying its practical potential, which has been revealed by significant improvements (e.g. in the reduction of downtime and the ratio of downtime, especially for load dependent behaviour of parts). To cope with temporal characteristics of loads, it is, however, essential to extend the scope of the study and apply the model for time-dependent components. Considering load independent analysis, identifying a correlation between quality and failure effects is essential. However, it has not been achieved due to data collection problems as well as incomprehensiveness of data sources for all selected machine components. Furthermore, machine components without a frequent failure pattern (such as linear guides) do not provide the necessary information for this kind of prescriptive maintenance application. While the developed maintenance control centre provides a positive impact for the maintenance operator, there are still a significant number of ‘false alarms’. Hence, advanced machine learning approaches are required to improve the false alarm detection, support detection of false-positive and false-negative errors, and ultimately automate the decision support process.