Huang Yingzhe, Zhao Hongmin, Zhao Xiaoting, Lin Bo, Meng Fanchao, Ding Jinmin, Lou Shuqin, Wang Xin, He Jingwen, Sheng Xinzhi, Liang Sheng
Appl Opt. 2022 Dec 10;61(35):10507-10518. doi: 10.1364/AO.476614.
This paper proposes a pattern recognition method for φ-OTDR based on self-reference features, where machine learning is applied to classify the vibration monitored. The -OTDR collects the light amplitude-time-space sequence, establishes a reference position in the spatial dimension, and combines the two dimensions of the vibration and reference positions to form self-reference features, which are then used as machine learning features. These self-reference features can effectively improve the pattern recognition accuracy. This paper selects a low sampling frequency for data collection, analyzes the influence of sample definition methods of different time lengths on the pattern recognition accuracy, and determines that the optimal sample length is 10 data points. The contribution of different feature parameters to pattern recognition is analyzed, and eight eigenvalues such as average, maximum, and minimum are finally determined to form self-reference features that are used as the input of the machine learning algorithm. The recognition accuracies of five machine learning algorithms including kNN, Decision Tree, Random Forest, LightGBM, and CatBoost are analyzed and compared, and the CatBoost algorithm in the integrated learning algorithm is finally determined as the optimal algorithm. On this basis, this paper proposes a filtering algorithm to deal with abnormal signals, which can effectively compensate for abnormal data and further improve the accuracy of pattern recognition. Finally, this paper conducts the pattern recognition study on four common events of tapping, bending, trampling, and blowing, and obtains the average recognition rate of 98%. In addition, this paper innovatively carried out pattern recognition research on five types of mining equipment, including ball mills, vibrating screens, conveyor belts, filters, and industrial pumps, and obtained the average recognition rate of 93.5%.
本文提出了一种基于自参考特征的φ-OTDR模式识别方法，将机器学习应用于对监测到的振动进行分类。φ-OTDR采集光幅度-时间-空间序列，在空间维度上建立参考位置，并将振动和参考位置这两个维度相结合形成自参考特征，然后将其用作机器学习特征。这些自参考特征能够有效提高模式识别精度。本文选择低采样频率进行数据采集，分析了不同时长样本定义方法对模式识别精度的影响，确定最优样本长度为10个数据点。分析了不同特征参数对模式识别的贡献，最终确定了均值、最大值、最小值等八个特征值来形成自参考特征，用作机器学习算法的输入。分析比较了kNN、决策树、随机森林、LightGBM和CatBoost这五种机器学习算法的识别精度，最终确定集成学习算法中的CatBoost算法为最优算法。在此基础上，本文提出一种处理异常信号的滤波算法，能够有效补偿异常数据并进一步提高模式识别精度。最后，本文对敲击、弯曲、踩踏和吹气这四种常见事件进行模式识别研究，获得了98%的平均识别率。此外，本文创新性地对球磨机、振动筛、传送带、过滤器和工业泵这五种采矿设备进行模式识别研究，获得了93.5%的平均识别率。
