Method for locating abnormal temperature event of distributed optical fiber

Patent metadata

Field	Value
Publication number	US-12411048-B2
Application number	US-202318192224-A
Country	US
Kind code	B2
Filing date	Mar 29, 2023
Priority date	Apr 6, 2022
Publication date	Sep 9, 2025
Grant date	Sep 9, 2025

Abstract

A method of locating a temperature anomalies of a distributed optical fiber includes the steps of: (a) generating a training dataset having training samples; (b) setting labels for training samples; (c) building a convolutional neural network composed of multi-layer convolutional networks and a fully connected layer, training to form a convolutional neural network model; (d) utilizing a fiber-optic temperature sensing system for measurement of testing object; (e) sending acquired data into the convolutional neural network model to obtain output features, then processing mapping and binarization; (f) offsetting the binary feature to obtain an offset feature and calculating a cosine similarity; and (g) obtaining a location of the abnormal temperature event by identifying the offset feature with a largest cosine similarity and identifying its location in the sequence P.

First claim

What is claimed is:

1. A method of locating an abnormal temperature event of a distributed optical fiber, comprising the steps of:
(a) generating a training dataset by:
(a.1) placing a sensing optical fiber of a fiber-optic temperature sensing system in a constant temperature water tank, and presetting a plurality of background temperatures and a plurality of abnormal temperatures, wherein the fiber-optic temperature sensing system acquires an array with a length of L×2 each time, each column of data corresponds to a location information on the sensing optical fiber, and L number of location information constitute a location sequence of length L;
(a.2) collecting multiple times at each of the background temperatures, and obtaining one background temperature array each time and a total of A number of background temperature arrays; collecting multiple times at each of the abnormal temperatures, and obtaining one abnormal temperature array each time and a total of B number of abnormal temperature arrays;
(a.3) normalizing the background temperature arrays and the abnormal temperature arrays to obtain normalized background temperature arrays and normalized abnormal temperature arrays respectively, and normalizing the location sequence to obtain a sequence P;
(a.4) constructing A number of A-type arrays by constructing one L×3 array with the normalized background temperature arrays and the sequence P respectively; constructing B number of B-type arrays by constructing one L×3 array with the normalized abnormal temperature arrays and the sequence P respectively;
(a.5) dividing each of the B-type arrays into a plurality of intervals with a length M, selecting a segment in each of the intervals randomly as an abnormal temperature range, replacing data at one corresponding location of the A-type arrays by data in the abnormal temperature range to form one replacement sample for each of the B-type arrays;
(a.6) dividing all the replacement samples and the A-type arrays evenly into training samples having a size of M×3, wherein all the training samples constitute the training data set;
(b) setting labels for each training sample with a label length M, if a j-th column of the particular training sample refers to the data in the abnormal temperature range, the j-th bit of the label is set as 0, otherwise set as 1, j=1˜M;
(c) constructing a convolutional neural network which comprises a multi-layer convolutional network and a fully connected layer, wherein the multi-layer convolutional network is formed by multiple convolutional layers, with an input size M×3, an output size 512, and an output size of a fully connected layer M; and processing training by sending the training samples into the convolutional neural network, and using the labels as expected output to obtain the convolutional neural network model;
d) utilizing the fiber-optic temperature sensing system on a testing object for measurement, and collecting one L×2 temperature array, which is normalized and constructs into an L×3 array with the sequence P, and dividing the L×3 array evenly into a plurality of M×3 sub-arrays;
(e) sending the sub-arrays into the convolutional neural network model to obtain an output feature with a length M, which is mapped to the 0-1 interval to obtain a mapped feature, and then carrying out a binarization process to obtain a binary feature;
(f) for each of the binary feature, performing an offset in an x direction to get an offset feature for each offset, calculating a cosine similarity between the offset feature and a first row of data in its corresponding sub-array;
(g) obtaining a location of the abnormal temperature event of the sub-array on the sensing fiber by identifying the offset feature with a largest cosine similarity and searching a location of the offset feature in the sequence P; and
(h) repeating steps (e)-(g) for the rest of the sub-arrays to obtain the location of abnormal temperature events of all sub-arrays on the sensing optical fiber.

2. The method of locating an abnormal temperature event of a distributed optical fiber according to claim 1 , wherein the temperature array with the length of L×2 collected by the fiber-optic temperature sensing system has a first row and a second row, wherein the first row is anti-Stokes original data and the second row is Stokes original data.

3. The method of locating an abnormal temperature event of a distributed optical fiber according to claim 1 , in step (a.3), further comprising the steps of:
composing the background temperature array and the abnormal temperature array into temperature array, finding a minimum value R and a maximum value of all elements in the temperature array, and calculating a difference D between the minimum value R and the maximum value,
processing normalization for each row of data in the background temperature array and the abnormal temperature array with formula (i):

x
i
′

=

x
i

-
R

D

,

i
=
1

,
2
,

3
⁢

…
⁢

L

where x i is the i-th value of a row of data before normalization, and x′ i is the i-th value of the row of data after normalization; and
processing normalization for the location sequence to obtain the sequence P by formula (ii):

L
i
′

=

L
i

L
+
1

,

i
=
1

,
2
,

3
⁢

…
⁢

L

where L i is the i-th value of the location sequence before normalization, and L′ i is the i-th value of the sequence P.

4. The method of locating an abnormal temperature event of a distributed optical fiber according to claim 1 , in step (a.5), further comprising the steps of:
(a.5-1) labelling the A-type arrays sequentially as A 1 -A A respectively, labelling the B-type arrays sequentially as B 1 -B B respectively;
(a.5-2) dividing B 1 into a plurality of intervals with the length M, M=64˜256, selecting 3-15 consecutive points randomly in each of the intervals as the abnormal temperature range;
(a.5-3) randomly selecting an array A i from the A-type arrays, i=1˜A, replacing A i by data of the abnormal temperature range in B 1 to constitute a replacement sample;
(a.5-4) repeating steps (a.5-2)-(a.5-3) for processing B 2 -B B to obtain a total of B replacement samples; and
(a.5-5) repeat steps (a.5-2)-(a.5-4) for T times to obtain T×B number of replacement samples.

5. The method of locating an abnormal tempera

Assignees

• Univ Chengdu Technology

Inventors

• Wang Honghui
• Wang Xiang
• Yao Guangle
• Peng Peng
• Yang Jianbo
• Tuo Xianguo

Classifications

• G06N3/08

  Learning methods · CPC title

• G06N3/0464 Primary

  Convolutional networks [CNN, ConvNet] · CPC title

• G01K3/005

  Circuits arrangements for indicating a predetermined temperature (fire detection G08B17/00) · CPC title

• G01K11/324

  using Raman scattering · CPC title

• G06N3/048

  Activation functions · CPC title