US 11,892,345 B2
Method and system for detecting and identifying vibration on basis of optical fiber signal feature to determine time-space
Feng Xie, Wuxi (CN); Hongliang Cong, Wuxi (CN); Guidong Li, Wuxi (CN); Xiaohui Hu, Wuxi (CN); and Longhai Xu, Wuxi (CN)
Assigned to NEUBREX CO., LTD., Hyogo (JP)
Appl. No. 17/418,459
Filed by NEUBREX CO., LTD., Hyogo (JP)
PCT Filed Dec. 25, 2019, PCT No. PCT/JP2019/050799
§ 371(c)(1), (2) Date Jun. 25, 2021,
PCT Pub. No. WO2020/138154, PCT Pub. Date Jul. 2, 2020.
Claims priority of application No. 201811635297.3 (CN), filed on Dec. 29, 2018.
Prior Publication US 2021/0396573 A1, Dec. 23, 2021
Int. Cl. G01H 9/00 (2006.01); G06N 20/10 (2019.01); G06F 18/22 (2023.01); G06F 18/214 (2023.01); G06F 18/2135 (2023.01); G06F 18/2413 (2023.01); G06F 18/243 (2023.01); G06N 5/01 (2023.01)
CPC G01H 9/004 (2013.01) [G06F 18/214 (2023.01); G06F 18/2135 (2023.01); G06F 18/22 (2023.01); G06F 18/24137 (2023.01); G06F 18/24323 (2023.01); G06N 5/01 (2023.01); G06N 20/10 (2019.01)] 7 Claims
OG exemplary drawing
 
1. A method for detecting and specifying a vibration on the basis of a feature of a fiber-optic signal to determine a time and a spatial location, comprising:
Step 1 of acquiring a feature-expanded function vector and C-number of vibration categories by expanding a feature of initial data of a vibration signal from a distributed fiber-optic sensor;
Step 2 of calculating a dimensionality reduction matrix based on the feature-expanded function vector;
Step 3 of acquiring a dimensionality-reduced feature function by operating the dimensionality reduction matrix to the initial data and the feature-expanded function vector;
Step 4 of acquiring a primary classification result of the vibration signal by performing a classification with reference to a primary classification parameter acquired from a parameter database; and
Step 5 of acquiring and outputting a secondary classification result of the vibration signal by performing removal of a wrong detection result and correction of a wrong classification result of the primary classification result, wherein
Step 2 includes:
Sub-step 21 of collecting a training sample set represented by the following Equation 1 for vibration category c;
Sub-step 22 of calculating, for each vibration category, a center of a cluster represented by the following Equation 2, a variance within the cluster represented by the following Equation 3, a sum of variances within clusters represented by the following Equation 4, a center of all the data represented by the following Equation 5, and a variance between clusters represented by the following Equation 6; and
Sub-step 23 of calculating a feature value of a matrix represented by the following Equation 7, decomposing the matrix, acquiring Q-number of maximum feature values and corresponding feature vectors, and composing the dimensionality reduction matrix by using columns of the feature vectors,

OG Complex Work Unit Math
where, in Equation 1, gk(c) denotes a k-th training sample in vibration category c,

OG Complex Work Unit Math
where, in Equation 2, Kc denotes the number of training samples in the category c,

OG Complex Work Unit Math
where, in Equation 4, C denotes the total number of vibration categories,

OG Complex Work Unit Math