Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Mechanical measurement system
Reexamination Certificate
2000-01-14
2002-09-17
Shah, Kamini (Department: 2863)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Mechanical measurement system
C702S054000, C073S04050A, C073S579000, C073S592000
Reexamination Certificate
active
06453247
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to leak detection and more particularly to a method and system for locating leaks in municipal water distribution pipes.
BACKGROUND OF THE INVENTION
In most municipal water distribution systems a significant percentage of water is lost while in transit from treatment plants to users. According to an inquiry made in 1991 by the International Water Supply Association (IWSA), the amount of lost water is typically in the range of 20 to 30% of production. In the case of some systems, mostly older ones, the percentage of lost water could be as high as 50% or even more. Lost water is usually attributed to several causes including leakage, metering errors, and theft—according to the IWSA survey, leakage is the major cause.
Water leakage is a costly problem—not only in terms of wasting a precious natural resource but also in economic terms. The primary economic loss due to leakage is the cost of raw water, its treatment, and transportation. Leakage inevitably also results in secondary economic loss in the form of damage to the pipe network itself, e.g., erosion of pipe bedding and major pipe breaks, and in the form of damage to foundations of roads and buildings. Besides the environmental and economic losses caused by leakage, leaky pipes create a public health risk as every leak is a potential entry point for contaminants if a pressure drop occurs in the system.
Economic pressure, concern over public health risk and simply the need to conserve water motivate water system operators to implement leak detection surveys. Leaks may in some cases be detected visually by spotting leaking water on the ground surface. In most cases, however, leaks never surface and normally acoustic methods are used to locate leaks by utilizing the sound or vibration induced by water as it escapes from pipes under pressure.
Locating leaks using acoustic equipment normally consist of two phases. In the first phase, an initial survey is conducted by listening for leak sounds using for example listening rods or aquaphones on all accessible contact points with the distribution system such as fire hydrants, valves, etc. Suspect leak locations found in this phase are noted for more accurate determination (pinpointing) in the second phase. Leaks are pinpointed by using geophones or ground microphones to listen for leak sounds on the ground directly above the pipe at very close intervals, e.g., every 1 m (3.3 ft); or by using leak noise correlation devices known as leak correlators.
Listening devices utilize sensitive mechanisms or materials, e.g., piezo-electric elements, for sensing leak-induced sound and vibration. They could be either of the mechanical or electronic type. Modern electronic devices may include signal amplifiers and noise filters, which could be very helpful in adverse environments. The use of listening devices is usually straightforward but their effectiveness depends on the experience of the user.
Locating leaks in water distribution pipes is a classical application of the cross-correlation method described in the book “Engineering applications of Correlation and Spectral Analysis” written by J. S. Bendat and A. G. Piersol, and published by John Wylie and Sons, New York, 1980. The method has been applied in U.S. Pat. Nos. 4,083,229 and 5,205,173. In U.S. Pat. No. 5,974,862 several enhancements of the cross-correlation method were applied to improve the method's accuracy for detecting leaks. The enhancements included achieving higher signal-to-noise ratio by transmitting leak signals using a digital wireless system wherein signals are digitized at the sensor, achieving a higher dynamic range of the measurement system by applying variable-gain to leak signals to utilize the full range of analog-to-digital converters, and introducing an incremental approach in the calculation of the cross-correlation function.
The cross-correlation method works by measuring vibration or sound in the pipe at two points that bracket the location of a suspected leak. Vibration sensors (normally accelerometers) are attached to fire hydrants or any other contact points with water pipes as shown schematically in FIG.
1
. Alternatively, hydrophones (or underwater microphones) can be used. These are inserted into fire hydrants using modified hydrant caps. Vibration or sound signals are transmitted from the sensors to the correlator wirelessly or using wires. The cross-correlation is calculated for the measured leak signals directly in the time domain or indirectly in the frequency domain. For leak signals ƒ
1
(t) and ƒ
2
(t) in digital form, the estimate of the cross-correlation function (Ĉ
12
) is calculated in the time domain using the following sum expression:
C
^
12
(
i
Δ
t
)
=
1
N
-
i
∑
k
=
1
N
-
i
f
1
k
f
2
k
+
i
where i=1, 2, . . . , M, &Dgr;t is the sampling interval, ƒ
1
k
and ƒ
2
k
are the k
th
samples of signals
1
and
2
, respectively, and N is the total number of digital samples. In the above expression, fewer and fewer terms will be included with increasing i. It is therefore necessary to limit M to a small fraction of N, say {fraction (
1
/
4
)}. In the frequency domain, the estimate of the cross-correlation function (Ĉ
12
) is obtained via the inverse Fourier transform of the cross-spectral function as
C
^
12
c
(
τ
)
=
Re
[
1
π
∫
0
π
/
Δ
t
E
^
12
(
ω
)
ⅇ
j
ω
τ
ⅆ
ω
]
where j=−1, and Ê
12
is estimate of the cross-spectral density function defined as
Ê
12
(&ohgr;)={circumflex over (F)}
1
*
(&ohgr;){circumflex over (F)}
2
(&ohgr;)
{circumflex over (F)}
1
and {circumflex over (F)}
2
are the spectral density functions of signals f
1
(t) and f
2
(t), respectively. {circumflex over (F)}
1
*
is the complex conjugate of {circumflex over (F)}
1
. The spectral density function of a signal f(t) is obtained via the Fourier Transform as
F
^
(
ω
)
=
∫
0
T
f
(
t
)
ⅇ
-
j
ω
t
ⅆ
t
For signals in digital form, the integrals in the above equations are evaluated by using equivalent summation expressions.
The cross-correlation function obtained in the frequency domain is circular, as indicated by the superscript c. This is due to the implicit periodicity of the time signals in the Fourier transform of finite signals. Time delays corresponding to peaks of circular correlation functions might be distorted. The circular effect is easily eliminated by padding time signals with a zero-amplitude segment of length T.
The estimate of the cross-spectral density function used in the above equations is usually obtained by averaging the results of calculations performed for several records or measurements of time signals as
E
^
12
(
ω
)
=
1
N
r
∑
k
=
1
N
r
F
1
k
*
(
ω
)
F
2
k
(
ω
)
where k designates signal or record number and N
r
is the total number of measurements. Averaging reduces the effect of incoherent random noise on the accuracy of the cross-correlation function. A measure of the relationship of the response at the two measurement points for a particular frequency components is provided by the coherence function defined as
γ
^
12
2
(
ω
)
=
&LeftBracketingBar;
E
^
12
(
ω
)
&RightBracketingBar;
2
E
^
11
(
ω
)
E
^
22
(
ω
)
where Ê
11
and Ê
22
are estimates of the auto-spectra of measurements at locations
1
and
2
, respectively. The value of {circumflex over (&ggr;)}
12
2
ranges from 0 to 1—a value of 1 indicates that signals at location
1
and
2
are caused by the same source(s) and value of 0 indicates that the signals at the two locations are unrelated. Values between 0 and 1 indicate the presence of related and unrelated components.
If a leak exists between the two measurement points, the cross-
Freedman & Associates
National Research Council of Canada
Shah Kamini
LandOfFree
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