A Dynamic Anomaly Detection Approach for Fault Detection on Fire Alarm System Based on Fuzzy-PSO-CNN Approach
- Title
- A Dynamic Anomaly Detection Approach for Fault Detection on Fire Alarm System Based on Fuzzy-PSO-CNN Approach
- Creator
- Kanagaraj K.; Amruthaluru U.; Selvaganesan J.; Singh Y.; Janakiraman V.; Chauhan A.
- Description
- 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. 2023 IEEE.
- Source
- International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 1124-1129.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Convolutional Neural Network (CNN); Fire Alarm System; Particle Swarm Optimization (PSO)
- Coverage
- Kanagaraj K., Srm Institute of Science and Technology, Department of Computational Intelligence, Chennai, India; Amruthaluru U., Vardhaman College of Engineering, Department of Cse, Telangana, India; Selvaganesan J., Veltech Rangarajan Dr Sagunthala R&d Institute of Science and Technology, Department of Electronics and Communication Engineering, Chennai, India; Singh Y., Mausam Bhawan, India Meteorological Department, New Delhi, India; Janakiraman V., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Mechanical Engineering, Chennai, India; Chauhan A., School of Sciences, Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835031398-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kanagaraj K.; Amruthaluru U.; Selvaganesan J.; Singh Y.; Janakiraman V.; Chauhan A., “A Dynamic Anomaly Detection Approach for Fault Detection on Fire Alarm System Based on Fuzzy-PSO-CNN Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19742.