Details

Autor(en) / Beteiligte

• Kanagaraj, Kaavya
• Amruthaluru, Umadatta
• Selvaganesan, Jana
• Singh, Yogendra
• Janakiraman, V
• Chauhan, Amit

Titel

A Dynamic Anomaly Detection Approach for Fault Detection on Fire Alarm System Based on Fuzzy-PSO-CNN Approach

Ist Teil von

• 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), 2023, p.1124-1129

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE

Beschreibungen/Notizen

• Early detection is crucial due to the catastrophic threats to life and property that are involved with fires. Sensory systems used in fire alarms are prone to false alerts and breakdowns, endangering lives and property. Therefore, it is essential to check the functionality of smoke detectors often. Traditional plans for such systems have included periodic maintenance; however, because they don't account for the condition of the fire alarm sensors, they are sometimes carried out not when necessary but rather on a predefined conservative timeframe. They describe a data-driven online anomaly detection of smoke detectors, which analyzes the behavior of these devices over time and looks for aberrant patterns that may imply a failure, to aid in the development of a predictive maintenance approach. The suggested procedure begins with three steps: preprocessing, segmentation, and model training. A pre-processing unit can enhance data quality by compensating for sensor drifts, sample-to-sample volatility, and disturbances (noise). The proposed approach normalizes the data in preparation. The smoke source can be detected by using segmentation to differentiate it from the background. Following segmentation, Fuzzy-PSO-CNN is used to train the models. CNN and PSO, two of the most used alternatives, are both outperformed by the proposed method.