相位敏感光时域反射计(Φ-OTDR)凭借着传感距离长、铺设简单、耐腐蚀和抗电磁干扰等特点被广泛应用于分布式振动监测领域。随着传感任务多样化及人工智能的广泛应用, 对振动事件的类型识别成为研究的热点方向。为了使读者能更好理解识别分类器研究进展和发展趋势, 先后介绍了传统 识别分类器和基于深度学习的神经网络识别分类器, 对不同分类器性能指标、优缺点和应用场合进行了比较, 最后对Φ-OTDR振动事件识别研究方向进行了展望。

Phase-sensitive optical time domain reflectometer (Φ-OTDR) is widely used in the field of distributed vibration monitoring due to its long sensing distance, simple laying, corrosion resistance and anti-electromagnetic interference. With the diversification of sensing tasks and the wide application of artificial intelligence, the type recognition of vibration events has become a hot research direction. In order to enable readers to better understand the research progress and development trend of recognition classifiers, this paper introduces the traditional recognition classifiers and the neural network recognition classifiers based on deep learning, compares the performance indexes, advantages and disadvantages of different classifiers and their applications, and finally prospects the research direction of Φ-OTDR vibration event recognition.