C 4 Anders Leak Detection Oct30 06

Novel Leak Detection System for Pipe-Type Cable Installations

George Anders Kinectrics Inc.Reza Ghafurian Con EdisonWojtek Tylman Technical University of Lodz

ICC Meeting in Tampa, Fl.

October 30, 2006

2Novel Leak Detection System for Pipe-Type Cable Installations

Outline of the presentation

z Motivationz Test installation and requirementsz Monitoring system overview z Components of the monitoring systemz Some resultsz Summary


Motivation

z Widespread usage of pipe type cables installations in large cities in North America

z Rapid growth in knowledge in the field of artificial intelligence

z Enormous computing capabilities of desktop computers

z Availability of data from real installations

z Environmental and technical problems when a leak occurs

z Scarcity of fairly inexpensive and easily applied methods for leak detection


Pipe type cables and leak detection requirements

The leak detection system should:z Be able to discover fairly small leaks,z Should not produce false alarms,

z Use sensors that are either already installed or such that are easy to install and monitor.

Electric cable Pipe

Tank

Pomp Valve


Ideas behind proposed approach (I)

z System operator is often able to discover leaks on the bases of the data obtained from installed sensors and his/her experience working with this installation

z The requirement for continuous monitoring and unreliability of humans requires that an automated system is used.

z The monitoring system should mimic human actions by learning typical system behavior, and then sound an alarm when the observed behavior departs from normal


Ideas behind proposed approach II)

We have selected three parameters that could be measured and that could be used to build algorithms describing the state of the installation (markers). We observed that during a leak:

z Pressure in the pipe is lower than values from other measured quantities would indicate (marker C)

z The frequency of pomp operation is greater (marker P)z Pressure does not reach the valve opening threshold (marker Z)

It was also noted that the measurement noise and occasional changes in system operation may produce results that could be interpreted as a leak by the monitoring system.Values produced by the markers together with the information about non-leak disturbances must be combined to produce a decision whether to call an alarm or not.


Modules of the monitoring system

Marker CModule

Decision Module ?

Marker PModule

Marker ZModule

Initial data analysis

Alternative sources of disturbances

Filtration of data

Pipe temp.

Soil temp.

Oil pressure

Current

Time


Neural Network (I)

Neural network computes fluid pressure on the basis of the remaining measured quantities. The network structure and its learning strategy should assure:

z high precision in computing the pressure in the installation,

z Ability to generalize,z High resistance to errors in the input data.

The speed of learning and the accuracy of results were main parameters on which the selection of the structure of the network was tested.


Neural Network (II)

z The selection of the network was tested for a given installation.

z Different cable systems may require different network construction.

z As a part of this project additional programs were developed to rapidly construct and test various neural networks

10


p

r

e

s

s

u

r

e

Heuristic algorithm pomp operation

z The algorithm is based on the observed dependence of the pressure on temperature.

z The values produced by the marker are several times greater during a leak than during a normal operation.

t

e

m

p

e

r

a

t

u

r

e

Pomp operation

Marker P

Pump operationt t

11


Heuristic algorithm valve operation

z The algorithm is based on the observation that during a leak in spite of the temperature rise the pressure often does not reach the valve operation threshold.

z The algorithm analyzes the temperature changes during a 24 h period and signals a leak when:1) the temperature rise is higher than typical, and2) the pressure did not reach the valve opening level

p

r

e

s

s

u

r

e

t

e

m

p

e

r

a

t

u

r

e

Marker Z

Valve opening thresholdtt

12


Probabilistic network basics

The basis of a description is agraph, which shows the dependencies between the variables

leak

disturbances

pressure differences

valve does not open

temperature does not rise

The graph is supplemented by probability tables,

0,80,1~p

0,20,9p

~dd

10,40,80~v

00,60,21v

~d,~t~d,td,~td,t

node cnode r

defined for each node

13


Pressure difference 4h



Pump operation -temperature

Pressure difference

disturbances Valve operation

Probabilistic network applied in the system

Rapid temperature

changeleak Temperature change

Input nodesOutput nodes

z Probability tables are based on several years

of observation for the installation

There are 31104 combinations of the possible input values, the probabilistic network reduces the requirement for the input quantities to 196 values.

Internal nodes

14


Sample results (I)

z Several leak tests were conducted by the utilitiesz Initially, only some of the tests were successful.z Many undetected leaks showed that unforeseen

disturbances or peculiarities of the installation caused misinterpretation of the input data.

z This led to the development of a sophisticated data filtration system and an implementation of the probabilistic network.

15


0

0,2

0,4

0,6

0,8

1

p

r

o

b

a

b

i

l

i

t

y

0 10 20 30 40 50 60 70 80 90 1000

0,20,40,60,8

1

time [days]

p

r

o

b

a

b

i

l

i

t

y

Sample results (III)

2004 test - leak amount: 1400 litres (0.12% of volume)

2002 test - leak amount: 750 litres (0.07% of volume)

16


Conclusions

z The leak detection system described here discovers leaks introduced during the tests conducted during the last 3 years by 2 utilities.

z System applies neural and probabilistic networks and uses some heuristic algorithms.

z The work is being continued to allow discovery of very small leaks and to automatically recognize changing operating conditions (e.g., pump and valve settings).

Thank you for your attention

C 4 Anders Leak Detection Oct30 06

Documents

Transcript of C 4 Anders Leak Detection Oct30 06