Ufa State Petroleum Technological University (department of fire safety, postgraduate student)
Ufa State Oil Technical University (starshiy prepodavatel')
graduate student
Russian Federation
Russian Federation
This article develops a Python-based neural network for predicting false alarms in smoke alarms within a fire alarm system. This research is relevant due to the high rate of false alarms, which disrupt facility operations, increase personnel workload, and reduce confidence in the alarm system. The novelty lies in the transfer of reliability and false alarm root cause analysis issues to predictive analytics. A formalized approach to predicting false alarms using telemetry time series and operational logs is proposed, along with a feature structure aligned with typical causes of failures and false alarms. The paper describes the data requirements, episode labeling logic, preprocessing scheme, architecture of a recurrent model, and quality criteria. Particular attention is paid to considering operational factors (installation, ventilation, dust levels, power supply instability, and room conditions) when generating features. The goal of this work is to propose a reproducible method for constructing a false alarm prediction model that can be subsequently integrated into maintenance procedures. The solution utilizes source analysis, comparative analysis of approaches, formalization, and design of a learning algorithm. This article is helpful for developers of fire alarm systems and researchers of applied machine learning in industrial diagnostics.
fire alarm, smoke detector, false alarms, prediction, time series, LSTM, Python, machine learning, reliability, diagnostics
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