1Assistant Professor, Department of ECE, V.S.B. College of Engineering Technical Campus, Coimbatore, India. Email: sindhujachairman@gmail.com
2Assistant Professor, Department of ECE, Nehru Institute of Technology, Coimbatore, Tamilnadu, India. Email: nitnarmatha@nehrucolleges.com
3UG Student, Department of ECE, V.S.B. College of Engineering Technical Campus, Coimbatore, India. Email: subashree1022005@gmail.com
4UG Student, Department of ECE, V.S.B. College of Engineering Technical Campus, Coimbatore, India. Email: valarmathigomathi11055@gmail.com
5UG Student, Department of ECE, V.S.B. College of Engineering Technical Campus, Coimbatore, India. Email: sadhusri3011@gmail.com
6UG Student, Department of ECE, V.S.B. College of Engineering Technical Campus, Coimbatore, India. Email: poovizhiraj459@gmail.com
Gas leakage in pipeline systems poses serious safety and environmental risks, particularly when micro-leaks remain undetected in early stages. Conventional gas detection methods rely on continuous sensor operation, resulting in high power consumption and limited suitability for remote or battery-powered deployment. This paper presents an event-driven neuromorphic vibration-based architecture for ultra-low power micro gas leak detection. Structural vibrations generated by pressurized gas escaping through small defects are continuously monitored using a piezoelectric sensor configured for event-based signal generation. Detected vibration events are processed using a Spiking Neural Network (SNN) implemented on an embedded microcontroller platform to discriminate between normal operational disturbances and leak-induced anomalies. Upon anomaly confirmation, an array of semiconductor gas sensors (MQ-2, MQ-4, and MQ-135) is selectively activated for concentration verification, reducing unnecessary heater power consumption. Experimental validation on a controlled pipeline setup demonstrates reliable micro-leak detection with reduced false alarms and significant power savings compared to continuous gas sensing approaches. The proposed hierarchical sensing framework enables early detection capability while maintaining energy efficiency, making it suitable for remote and embedded pipeline monitoring applications.
Keywords: Event-driven sensing, spiking neural networks, vibration-based leak detection, ultra-low power monitoring, hierarchical gas verification, embedded neuromorphic systems.
How to cite this article: Sindhuja Sankareshwari C, Narmatha D, Subashree SB, Valarmathi M, Sadhana sri M, Poovizhi R. Event-Driven Neuromorphic Vibration Processing for Ultra Low Power Micro Gas Leak Detection. Int J Drug Deliv Technol. 2026;16(19s): 17-25. DOI: 10.25258/ijddt.16.19s.3
Source of support: Nil.
Conflict of interest: None