Work

Real-time vibration mode recognition of φ -OTDR based on Opus audio compression and spiking neural networks

Year: 2025

Type: article

Abstract: This study proposes a vibration monitoring system based on distributed acoustic sensing (DAS) and spiking neural networks (SNN) for real-time fault detection in industrial applications. By integrating Opus audio compression technology with SNN, the framework achieves highly efficient compression of vibration signals, allowing for a significant reduction in data volume (50-80 times compression) without losing critical information such as friction and impact events. This allows for high-quality input for subsequent fault diagnosis. Experimental results show that the system demonstrates exceptional performance, achieving over 98% accuracy in scenarios such as long-distance conveyor monitoring, pipeline monitoring, and railway safety, with minimal computational requirements and rapid real-time processing capabilities. The proposed system's modular design, which includes data acquisition, preprocessing, compression, feature extraction, and result output, ensures reliable fault detection with high generalization across different engineering fields.

Source: Optics Express

DOI: 10.1364/oe.578219

Authors: Yunfan Xu, Haitao Liu, Hao Li, Qiang Liu, Weihong Bi, Yuefeng Qi

Institution: Changchun University of Science and Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,270