US 11,892,345 B2Granted

Method and system for detecting and identifying vibration on basis of optical fiber signal feature to determine time-space

AssigneeNeubrex Co., Ltd.

InventorsFeng Xie, Hongliang Cong, Guidong Li, Xiaohui Hu, Longhai Xu

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Abstract

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 of the present invention includes: 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 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.

Claims7

Dependent claims

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,
Training
⁢
sample
⁢
set
:
{
g
1
(
c
)
,
g
2
(
c
)
,
…
,
g
K
c
(
c
)
}
,
(
Equation
⁢
1
)
where, in Equation 1, gk(c) denotes a k-th training sample in vibration category c,
Center
⁢
of
⁢
a
⁢
cluster
:
g
¯
(
c
)
=
1
K
c
⁢
∑
k
=
1
K
c
⁢
g
K
(
c
)
,
(
Equation
⁢
2
)
where, in Equation 2, Kc denotes the number of training samples in the category c,
Variance
⁢
within
⁢
the
⁢
cluster
:
(
Equation
⁢
3
)
S
(
c
)
=
∑
k
=
1
K
c
⁢
(
g
k
(
c
)
-
g
¯
(
c
)
)
⁢
(
g
k
(
c
)
-
g
¯
(
c
)
)
T
,
Sum
⁢
of
⁢
variances
⁢
within
⁢
clusters
:
S
∑
=
∑
c
=
1
C
⁢
S
(
c
)
,
(
Equation
⁢
4
)
where, in Equation 4, C denotes the total number of vibration categories,
Center
⁢
of
⁢
all
⁢
of
⁢
the
⁢
data
:
g
¯
=
1
∑
c
=
1
C
⁢
K
c
⁢
∑
c
=
1
C
⁢
∑
k
=
1
K
c
⁢
g
K
(
c
)
,
(
Equation
⁢
5
)
Variance
⁢
between
⁢
clusters
:
S
=
∑
c
=
1
C
⁢
K
c
(
g
_
-
g
_
(
c
)
)
⁢
(
g
_
-
g
_
(
c
)
)
T
,
(
Equation
⁢
6
)
Matrix
:
S
∑
-
1
⁢
S
.
(
Equation
⁢
7
)

271 more elements

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 5 includes:
Sub-step 51 of converting the primary classification result represented by the following Equation 8 to a logical variable represented by the following Equation 9;
Sub-step 52 of calculating, for each vibration category, a score of B-number of whole logical equations {J1(L), J2(L), . . . , JB(L)} in a logical rule base on the basis of a calculation equation represented by the following Equation 10;
and
Sub-step 53 of outputting a vibration category corresponding to a highest score represented by the following Equation 11 as an output of a secondary classifier,
Primary
⁢
Classification
⁢
Result
:
e
⁡
(
t
,
d
)
,
(
Equation
⁢
8
)
Logical
⁢
Variable
:
L
m
,
n
(
t
)
=
{
1
if
⁢
e
⁡
(
t
-
m
,
d
-
n
)
==
i
0
otherwise
,
(
Equation
⁢
9
)
where, in Equation 9, Lm,n(i) denotes a logical variable when a fiber-optic signal at a time (t−m) and a location (d−n) is determined to be a vibration of category i in Step 4,
Calculation
⁢
Equation
:
s
(
c
)
=
∑
b
=
1
B
⁢
J
b
(
L
)
⁢
s
b
(
c
)
,
(
Equation
⁢
10
)
where, in Equation 10, s(c) denotes a score on the basis of the whole logical equations in the logical rule base when the current vibration belongs to category c, the input L in Jb(L) configures all the logical variables Lm,n(i), the subscript b denotes the b-th logical equation, and sb(c) denotes a contribution degree to a score in each vibration category when the logical equation Jb (L) is established,
Vibration
⁢
category
⁢
corresponding
⁢
to
⁢
a
⁢
highest
⁢
score
:
(
Equation
⁢
11
)
e
*
(
t
,
d
)
=
arg
max
c
s
(
c
)
.

133 more elements

A system 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:

a feature expansion unit for 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;
a dimensionality reduction matrix calculation unit for calculating a dimensionality reduction matrix based on the feature-expanded function vector;
a linear dimensionality reduction unit for acquiring a dimensionality-reduced feature function by operating the dimensionality reduction matrix to the initial data and the feature-expanded function vector;
a primary classification unit for 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
a secondary classification unit for 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
the dimensionality reduction matrix calculation unit
collects a training sample set represented by the following Equation 1 for vibration category c;
calculates, 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
calculates 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,
Training
⁢
sample
⁢
set
:
{
g
1
(
c
)
,
g
2
(
c
)
,
…
,
g
K
c
(
c
)
}
,
(
Equation
⁢
1
)
where, in Equation 1, gk(c) denotes a k-th training sample in vibration category c,
Center
⁢
of
⁢
a
⁢
cluster
:
g
¯
(
c
)
=
1
K
c
⁢
∑
k
=
1
K
c
⁢
g
K
(
c
)
,
(
Equation
⁢
2
)
where, in Equation 2, Kc denotes the number of training samples in the category c,
Variance
⁢
within
⁢
the
⁢
cluster
:
(
Equation
⁢
3
)
S
(
c
)
=
∑
k
=
1
K
c
⁢
(
g
k
(
c
)
-
g
¯
(
c
)
)
⁢
(
g
k
(
c
)
-
g
¯
(
c
)
)
T
,
Sum
⁢
of
⁢
variances
⁢
within
⁢
clusters
:
S
∑
=
∑
c
=
1
C
⁢
S
(
c
)
,
(
Equation
⁢
4
)
where, in Equation 4, C denotes the total number of vibration categories,
Center
⁢
of
⁢
all
⁢
of
⁢
the
⁢
data
:
g
¯
=
1
∑
c
=
1
C
⁢
K
c
⁢
∑
c
=
1
C
⁢
∑
k
=
1
K
c
⁢
g
K
(
c
)
,
(
Equation
⁢
5
)
Variance
⁢
between
⁢
clusters
:
S
=
∑
c
=
1
C
⁢
K
c
(
g
_
-
g
_
(
c
)
)
⁢
(
g
_
-
g
_
(
c
)
)
T
,
(
Equation
⁢
6
)
Matrix
:
S
∑
-
1
⁢
S
.
(
Equation
⁢
7
)

271 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/418,459Filed 25 Dec 2019
Priority: CN 201811635297.329 Dec 2018
Published: US 2021/0396573 A123 Dec 2021
Granted: 6 Feb 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification12 codes

G01H 9/004
Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means: using fibre optic…
G06F 18/2135
Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods: based on approximation criteria, e.g.…
G06F 18/214
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: generating training…
G06F 18/22
Analysing: matching criteria, e.g. proximity measures

Family

US 2021/0396573 A1Pre-grant publication · published 23 Dec 2021
US 11,892,345 B2This patent

From the US filing history. Foreign family members aren’t listed.

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