US 11,236,867 B2Granted

Method for detecting pipe burst in water distribution systems based on pressure disturbance extraction

AssigneeTongji University

InventorsKunlun Xin, Weirong Xu, Xiao Zhou, Hexiang Yan, Tao Tao, Shuping Li

Life of this patent

Estimating the term…

Loading patent…

Abstract

The present invention introduced a method for detecting pipe bursts in water distribution systems based on pressure disturbance extraction, including following steps: (1) collecting and pre-processing monitored pressure data, and establishing a matrix of monitored pressures; (2) analyzing time-domain and frequency-domain features of the matrix of monitored pressures by Fourier transform, extracting disturbances, and generating pressure disturbances matrices; (3) identifying outliers in the pressure disturbances matrices by isolation forest algorithm; and (4) further identifying detected outliers by calculating and qualitative index A and quantitative index B and outputting a result of pipe burst detection. Compared with previous methods, the pipe burst detection method introduced in the present invention is accurate and reliable, and is more applicable to large-scale complex water distribution systems.

Claims5

Dependent claims

A method for detecting pipe bursts in water distribution systems based on pressure disturbance extraction, the method comprising:

(1) collecting and pre-processing monitored pressure data, and establishing pressure matrices;
(11) numbering date and time of historical data;
(12) for a moment t=(x,y) to be detected, extracting SCADA monitored pressure data in m days before the moment t, wherein (x,y) represents the moment y in the xth day;
(13) pre-processing the extracted monitored pressure data;
(14) dividing the pre-processed pressure data at a sensor into m row vectors, each vector having a length of N, N being the number of samples at one sensor in a day, and storing the row vectors in pre-processed pressure matrices P, wherein, for the ith sensor, the corresponding matrix P1 is:
P
i
=
(
P
x
-
m
,
y
+
1
…
p
x
-
m
,
N
P
x
-
m
+
1
,
1
…
p
x
-
m
+
1
,
y
p
x
-
m
,
y
+
1
…
P
x
-
m
-
1
p
x
-
m
+
2
,
1
…
P
x
-
m
+
2
,
y
⋮
⋱
⋮
⋮
⋱
⋮
P
x
-
1
,
y
+
1
…
P
x
-
1
,
N
P
x
,
1
…
P
x
,
y
)
,
⁢
where element pj,k in Pi represents the monitored pressure at the sensor i at the moment k in the jth day, the mth row in P1 represents the monitored pressure at the sensor i in a day before the moment to be detected, and for n sensors, total n matrices of monitored pressures are generated;
(2) analyzing time-domain and frequency-domain features of the matrix of monitored pressures by Fourier transform, extracting disturbances, and generating pressure disturbances matrices;
(3) identifying outliers in the pressure disturbances matrices by isolation forest algorithm;
and
(4) identifying detected outliers by calculating a qualitative index A and a quantitative index B, and outputting a result of pipe burst detection.

108 more elements

A method for detecting pipe bursts in water distribution systems based on pressure disturbance extraction, the method comprising:

(1) collecting and pre-processing monitored pressure data, and establishing pressure matrices;
(2) analyzing time-domain and frequency-domain features of the matrix of monitored pressures by Fourier transform, extracting disturbances, and generating pressure disturbances matrices;
(21) processing row vectors in pre-processed pressure matrices by Fourier transform, wherein, given that each row in pre-processed pressure matrices is a discrete signal x[n] having a length of N, then the Fourier transform X[k] is:
X
⁡
[
n
]
=
∑
n
=
0
N
-
1
⁢
x
⁡
[
n
]
⁢
e
-
j
⁢
2
⁢
π
N
⁢
kn
,
0
≤
k
≤
N
-
1
,
where X[k] is the Fourier transform of x[n], and
e
-
j
⁢
2
⁢
π
N
⁢
kn
is a periodic complex-exponential function of
2
⁢
π
N
;
(22) extracting high-frequency components (disturbances)in the monitored signal, the high-frequency component X′[k] in the frequency domain being extracted as follows:
X
′
⁡
[
k
]
=
{
X
⁡
[
k
]
,
μ
≤
k
≤
N
-
μ
0
,
otherwise
,
0
≤
k
≤
N
-
1
where, μ is a parameter which controls the number of selected high-frequency components;
(23) reconstructing the extracted high-frequency component X′[k] in the frequency domain into a time-domain signal x′[n] by inverse Fourier transform:
x
′
⁡
[
n
]
=
1
N
⁢
∑
k
=
0
N
-
1
⁢
X
′
⁡
[
k
]
⁢
e
j
⁢
2
⁢
π
N
⁢
kn
,
0
≤
n
≤
N
-
1
,
(24) generating a matrix H of disturbed pressures, wherein, for the ith sensor, the corresponding disturbance matrix Hi is:
H
i
=
(
h
x
-
m
,
y
+
1
…
h
x
-
m
,
N
h
x
-
m
+
1
,
1
…
h
x
-
m
+
1
,
y
h
x
-
m
+
1
,
y
+
1
…
h
x
-
m
+
1
,
N
h
x
-
m
+
2
,
1
…
h
x
-
m
+
2
,
y
⋮
⋱
⋮
⋮
⋱
⋮
h
x
-
1
,
y
+
1
…
h
x
-
1
,
N
h
x
,
1
…
h
x
,
y
)
,
where element hj,k in Hi represents the high-frequency component extracted from the monitored pressure at the sensor i at the moment k in the jth day. For n sensors, total n matrices of disturbed pressures are generated;
(3) identifying outliers in the pressure disturbances matrices by isolation forest algorithm;
and
(4) identifying detected outliers by calculating a qualitative index A and a quantitative index B, and outputting a result of pipe burst detection.

243 more elements

A method for detecting pipe bursts in water distribution systems based on pressure disturbance extraction, the method comprising:

(1) collecting and pre-processing monitored pressure data, and establishing pressure matrices;
(2) analyzing time-domain and frequency-domain features of the matrix of monitored pressures by Fourier transform, extracting disturbances, and generating pressure disturbances matrices;
(3) identifying outliers in the pressure disturbances matrices by isolation forest algorithm;
(31) establishing a detection matrix D using pre-processed pressure matrices:
D
=
(
d
x
-
m
+
1
,
1
…
d
x
-
m
+
1
,
n
⋮
⋱
⋮
d
x
,
1
…
d
x
,
n
)
,
where the ith column in the detection matrix D corresponds to the last column in the matrix Hi of high-frequency components, each row in the detection matrix D is a row vector having a length of n, and the last row in the detection matrix D is the high-frequency components extracted at sensors at the moment to be detected;
(32) inputting the detection matrix D into the isolation forest algorithm, each row in the detection matrix D being a detection sample, establishing t isolation trees, randomly selecting one dimension x as a study object for each isolation tree, 1≤x ≤n, randomly selecting a boundary value in this dimension to divide samples into two parts, and repeating this process until all samples are separated from others;
(33) calculating an average path length of each sample in all isolation trees, wherein, for each sample in each isolation tree, the number of separations for this detection sample from other detection samples is the path length of this detection sample in this isolation tree, selecting k detection samples with minimal average path length as abnormal samples;
(34) if the last row in the detection matrix D is selected as an abnormal sample, then identifying the moment to be detected as an abnormal state and passing the results to step (4), otherwise outputting a result indicating that no pipe burst occurs;
and
(4) identifying detected outliers by calculating a qualitative index A and a quantitative index B, and outputting a result of pipe burst detection.

41 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/600,557Filed 13 Oct 2019
Priority: CN 201811393693.X21 Nov 2018
Published: US 2020/0158291 A121 May 2020
Granted: 1 Feb 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

E03B 7/10
Component parts or accessories: devices preventing bursting of pipes by freezing
F17D 5/02
Protection or supervision of installations: preventing, monitoring, or locating loss
G01M 3/243
Using infrasonic, sonic or ultrasonic vibrations: for pipes
G01M 3/2815
For pipes: using pressure measurements