Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Pattern recognition using self-reference feature extraction for φ-OTDR

Not Accessible

Your library or personal account may give you access

Abstract

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 $\varphi$-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%.

© 2022 Optica Publishing Group

Full Article  |  PDF Article
More Like This
Single and composite disturbance event recognition based on the DBN-GRU network in φ-OTDR

Mingxuan Liu, Xin Wang, Sheng Liang, Xinzhi Sheng, and Shuqin Lou
Appl. Opt. 62(1) 133-141 (2023)

Highly discriminative and adaptive feature extraction method based on NMF–MFCC for event recognition of Φ-OTDR

Yi Huang, Jingyi Dai, Wei Shen, Xiaofeng Chen, Chengyong Hu, Chuanlu Deng, Lin Chen, Xiaobei Zhang, Wei Jin, Jianming Tang, and Tingyun Wang
Appl. Opt. 62(35) 9326-9333 (2023)

Machine learning methods for identification and classification of events in ϕ-OTDR systems: a review

Deus F. Kandamali, Xiaomin Cao, Manling Tian, Zhiyan Jin, Hui Dong, and Kuanglu Yu
Appl. Opt. 61(11) 2975-2997 (2022)

Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (18)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (8)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (1)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.