Bublin Mugdim
FH Campus Wien, University of Applied Sciences, 1100 Vienna, Austria.
Sensors (Basel). 2021 Nov 12;21(22):7527. doi: 10.3390/s21227527.
Distributed Acoustic Sensing (DAS) is a promising new technology for pipeline monitoring and protection. However, a big challenge is distinguishing between relevant events, like intrusion by an excavator near the pipeline, and interference, like land machines. This paper investigates whether it is possible to achieve adequate detection accuracy with classic machine learning algorithms using simulations and real system implementation. Then, we compare classical machine learning with a deep learning approach and analyze the advantages and disadvantages of both approaches. Although acceptable performance can be achieved with both approaches, preliminary results show that deep learning is the more promising approach, eliminating the need for laborious feature extraction and offering a six times lower event detection delay and twelve times lower execution time. However, we achieved the best results by combining deep learning with the knowledge-based and classical machine learning approaches. At the end of this manuscript, we propose general guidelines for efficient system design combining knowledge-based, classical machine learning, and deep learning approaches.
分布式声学传感（DAS）是一种用于管道监测和保护的很有前景的新技术。然而，一个巨大的挑战是区分相关事件，如管道附近挖掘机的侵入，和干扰，如地面机器。本文通过模拟和实际系统实现，研究了使用经典机器学习算法是否有可能实现足够的检测精度。然后，我们将经典机器学习与深度学习方法进行比较，并分析两种方法的优缺点。虽然两种方法都能实现可接受的性能，但初步结果表明，深度学习是更有前景的方法，它无需费力的特征提取，事件检测延迟低六倍，执行时间低十二倍。然而，我们通过将深度学习与基于知识的和经典机器学习方法相结合取得了最佳结果。在本文末尾，我们提出了结合基于知识的、经典机器学习和深度学习方法进行高效系统设计的一般指导原则。
