a fuzzy expert system for buried pipeline corrosion...
TRANSCRIPT
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A fuzzy expert system for buried pipeline corrosion assessment based on
DCVG measurements
Alicia BEL1, François CASTILLON2, Laurence BOUDET3, Jean-Philippe POLI3,
Frédéric GRAIGNE2, Olivier CASULA1
1CEA Tech Aquitaine, 33600 PESSAC, FRANCE.
2TIGF, 64000 PAU, FRANCE.
3CEA, LIST, 91191 GIF SUR YVETTE CEDEX, FRANCE.
Abstract
Despite protective measures such as external coating and cathodic protection, regular in-line inspection
is essential to ensure buried steel pipelines integrity against corrosion. To inspect pipe sections
considered unpiggable, TIGF – which operates a 5000 km-long gas network in southwest France –
conducts DC-Voltage Gradient (DCVG) surveys, which locate coating defects using surface electrical
measurements. Every year, TIGF detects about 7000 coating defects and collects for each of them a set
of 26 features allowing for corrosion-deterioration risk assessment. However, identifying the most
critical cases requiring to be excavated is a complex task. In this work, we introduce a decision support
tool, based on a Fuzzy Expert System (FES) named ExpressIF, to evaluate coating defects criticality.
FES are artificial intelligence tools designed to reproduce human reasoning based on an inference
engine. The proposed system supports excavation decisions by evaluating 7 different types of
deterioration risk together with the severity level of a potential pipeline failure. It embeds more than 300
rules gathered from experts and fulfils operational needs through a dedicated interface. For each decision
made, references to the relevant rules are given, which therefore provides an efficient framework for
improving selection rules from excavation feedbacks. This tool is now in operation and has fully
contributed to the definition of the excavation campaign planned by TIGF for 2016.
Keywords
Fuzzy expert system; Fuzzy logic; Direct Current Voltage Gradient; Pipeline integrity
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Introduction
As gas transmission network operator in Southwest France, TIGF manages over
5000 km of high-pressure pipelines. One of TIGF’s constant concern is to ensure
infrastructure’s safety and reliability through rigorous maintenance programs. For that purpose,
instrumented in-line inspection is widely used to assess pipeline integrity from the inside and
accurately locates defects or corrosion threats. Unfortunately, due to the specific inspection
conditions required such as a minimum pipe diameter of 200 mm, intelligent pigging surveys
can only be carried out on a restricted part of the network. To complete the audit of its buried
pipelines and prevent high consequence failures, TIGF has put in place a monitoring policy
consisting in regular collection and analysis of DCVG – Direct Current Voltage Gradient –
measures along its grid.
A DCVG survey is designed to locate and size coating faults. With this mapping, costly
decisions have to be made regarding pipe excavations for visual examination. Considering that
thousands of coating defects are detected each year on a network such as TIGF’s, the need for
defects prioritization based on assessed threat severity emerges. However, the results of a
DCVG survey do not provide direct information about the pipeline condition and need to be
interpreted together with other factors.
Interpreting DCVG data remains a challenging task that requires a vast expertise on the
multiple factors and conditions that may affect pipeline integrity. In particular, one major focus
lies on predicting steel corrosion that can occur in case of ineffective or insufficient cathodic
protection. This threat is manifold: aside from usual corrosion, atypical corrosion threats
resulting from external influences (electric currents, bacteria,…) are cause of concern. That is
why all potential hazards have to be weighted in the light of multiple features, with reciprocal
influence. It seems clear that the help of an efficient decision-support tool that automates DCVG
analysis represents a significant gain.
In accordance with the methodology for External Corrosion Direct Assessment (ECDA)
exposed in the standard ANSI/NACE SP0502-2010 [1], several works propose to estimate
corrosion probabilities based on a Bayesian framework. In [2], collected pipeline parameters,
such as age, coating type or soil condition are used to segment the grid in homogeneous sections
and to estimate prior probabilities of failure for each section. These probabilities are updated
based on combined DCVG and Closed Interval Potential Survey (CIPS) results. Failure
frequencies estimates are a guide to program visual inspections of unpiggable pipelines. In a
similar fashion, statistical distributions of density and size of corrosion defects are estimated in
[3] to support excavation decisions. The results achieved show strong method’s effectiveness
but also underline the need for a significant number of prior excavation data to feed the
statistical model.
In this work, we propose to develop a non-probabilistic model focused on experts’
experience. Our approach exploits fuzzy logic, a powerful concept introduced by Zadeh [4] to
model how humans deal with imprecise data. This strategy is chosen in [5] to assess coating
defects severity: 12 rules are defined to link 4 input parameters to a severity level. Each input
is described by 3 fuzzy sets and its relative importance is weighted. Yet, all parameters are
considered independent. In this study, we introduce complex rules to get closer to a real expert
analysis, while guaranteeing consistency over thousands of cases. Our solution is based on
CEA’s fuzzy expert system ExpressIF and is designed to facilitate model constant improvement
based on excavation outcomes.
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DCVG data acquisition and interpretation
Pipeline pigging is an effective way to accurately locate steel defects and metal loss.
Unfortunately, this method is inappropriate for some pipe configurations, such as small
diameter or multi-diameter pipes. Therefore, above ground inspection surveys are conducted to
gather information about the whole network condition. TIGF mainly uses the DCVG technique.
Principle of the DC Voltage Gradient technique
If the pipe is exposed at holidays in its protective coating, the current impressed by the
cathodic protection system will flow from the soil into the bare steel. It results in voltage
gradients in the soil surrounding the defect.
The DCVG method consists in pulsing the input current signal and detecting associated
voltage gradients in the soil above the pipeline, that betray the presence of a soil-metal interface.
To this end, an operator performs regular measures with a milli-voltmeter of the voltage drop
between two electrodes placed on the soil surface at a distance that remains constant (about 1,5
meters). As the operator approaches a coating defect, he observes an increasing pulsing signal.
This signal finally stabilizes then decreases as the defect is passed.
Each defect severity is characterized by its value of %IR, which is computed based on
DCVG measures. Then, thanks to calibrated references, the size of the steel surface exposed
can be estimated.
DCVG surveys
DCVG surveys return alerts on the pipeline protective coating. However, it does not
inform about the cathodic protection state, nor does it imply a real metal deterioration. To better
assess the risk of a pipeline defect, TIGF gathers additional data:
the pipeline specific features (age, type of coating,…),
the pipeline environment (presence of stray currents, soil resistivity, soil bacteria,…),
the history of the pipeline cathodic protection.
All of this information is carefully examined with multiple risks in mind. In addition to
mechanical attacks, a typology of different corrosion forms is considered: stray current
corrosion, corrosion caused by alternating current, corrosion under a disbonded shielding
coating, bacterial corrosion, high-voltage corrosion and insufficient cathodic protection.
Finally, the gravity of the consequences that a severe metal defect would have is a
crucial parameter that is also carefully taken into consideration. A pivotal factor is the pipeline
proximity from any public location or infrastructure.
Conducting a systematic analysis of the thousands of coating defects that are detected
each year by DCVG surveys is a real challenge. It requires to apply a wide expertise in a
consistent fashion to a large variety of configurations. In this context, a decision support tool
offers clear benefits.
Fuzzy expert systems
In this subsection, we give key features of fuzzy expert systems to allow the reader to
understand our motivation. Fuzzy expert systems are expert systems based on fuzzy logic.
Fuzzy logic can be viewed as an extension of boolean logic which permits to handle uncertainty:
whereas boolean logic considers only two values (true and false), fuzzy logic considers all the
real values between 0 (for false) and 1 (for true). It has been introduced by Zadeh [5] in order
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to get closer to the human perception and to avoid threshold effects of boolean logic. The
particularity of fuzzy expert systems is that rules can be written in a syntax which is close to
natural language, in the form:
IF premise THEN conclusion
where the premise is a fuzzy expression and the conclusion is an elementary fuzzy proposition.
For instance, IF temperature is cold THEN sweating is low is a well-formed fuzzy
rule, in which no mathematical operators appear. Contrary to classical expert systems, in fuzzy
expert systems, several rules linked to the same output can be activated simultaneously.
Fuzzy sets and linguistic variables
More formally, fuzzy logic is based on the concept of fuzzy sets. Let X be the universe
of discourse, i.e. a collection of objects denoted x. A fuzzy set A of X is totally characterized by
a function 𝜇𝐴: 𝑋 → [0,1], called membership function (MF). This allows to define a linguistic
variable [6] as a triplet (V, X, TV) such as:
V is the name of the variable (e.g. “temperature”),
X is the domain on which it is defined (e.g. ℝ or [0,10]),
TV = {T1, T2,…} is a finite collection of fuzzy sets called terms which qualify V (e.g.
“cold”, “hot”).
Each fuzzy set Ti of TV is associated with a name and is called linguistic term. Figure 1
shows a linguistic variable called “temperature” with three terms (“cold”, “medium”, “hot”)
and their membership functions, defined on [-20, 50] in degree Celsius. The absence of
mathematical operators is balanced by the fact linguistic variables link words in the rules with
numerical values.
Figure 1: Example of a linguistic variable "temperature" composed of 3 terms "cold",
"medium", "hot" and their respective functions
Fuzzy rules
An elementary fuzzy proposition is the definition of V is A from a linguistic variable
(V, XV, TV) where A is a term of TV. For instance, if we use the linguistic variable “temperature”
of Figure 1, a fuzzy proposition can be temperature is cold. It is evaluated from its
membership function 𝜇𝑐𝑜𝑙𝑑 : for a particular temperature t, the truth value of the proposition
temperature is cold is given by 𝜇𝑐𝑜𝑙𝑑(t). The truth value of a fuzzy proposition belongs
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to the interval [0, 1] in ℝ unlike classical logic where the truth value of a proposition would be
either false or true.
A fuzzy expression is a composition of a set of elementary fuzzy propositions or other
fuzzy expressions. The composition is done by the use of logical operators: negation (not),
conjunction (and) and disjunction (or), or more sophisticated operators (temporal, spatial,...).
Fuzzy propositions can be viewed as a special case of fuzzy expressions. For instance, let two
fuzzy propositions be respectively V is A using (V, XV, TV) and W is B using (W, XW, TW).
Thus, V is A and W is B and V is A or not W is B are fuzzy expressions. The truth
value of a fuzzy expression is given by the application of all the operators on the values of their
operands.
Fuzzy inference
Now the basics have been introduced, we can focus on the process of inference in
Mamdani fuzzy expert systems which is summarized in Figure 2.
Figure 2 : Overview of Mamdani fuzzy inference process
1. Fuzzification: it consists in the evaluation of each elementary fuzzy proposition in the
premise of the rules. It takes a crisp value (the value of the input in the real world) and
associates a fuzzy value regarding the membership function of the term of the linguistic
variable involved in the fuzzy proposition.
For instance, using the linguistic variable “temperature” of Figure 1 and its term “cold”,
for a temperature of -1°C, temperature is cold is evaluated at 1, i.e. -1°C is really
a cold temperature. For a temperature of 7°C, both temperature is cold and
temperature is medium equal 0.5.
2. Premises evaluation: premises are then computed with the application of the operators
according to the involved rules. The value of each premise is a fuzzy value also called
rule activation.
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For instance, considering Zadeh's operator (i.e. the max function) and a temperature of
-1°C, the value of the premise temperature is cold or temperature is
medium is max(1,0)=1.
3. Implication evaluation: the implication function is then applied on the conclusion of
each rule. The result is a fuzzy set describing the output.
For instance, if the premise of the rule has been evaluated to 0.75, let the implication
method be the min function, thus the output's fuzzy set is as shown in Figure 3.
Figure 3 : Result of the implication on a fuzzy set for an activation value of 0.75.
The membership function definition of the conclusion is shown by the dashed line.
It would have been inferred totally if the activation value has been equal to 1
4. Aggregation: the different resulting fuzzy sets of a same output are aggregated by an
aggregation function, for instance the max function for Mamdani systems, resulting in
a fuzzy set for each output.
As an example, Figure 4 shows the process of aggregation of two fuzzy sets with the
max aggregation function.
Figure 4 : Application of the max aggregation on two fuzzy sets
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5. Defuzzification: each output's fuzzy set is changed into a crisp value. There are several
defuzzification methods; the most known defuzzification method is the centroid of the
area under the membership function (the blue area of the aggregated fuzzy set in Figure
4).
The reader is invited to consult [7] [8] for further reading about fuzzy logic.
Fuzzy expert systems have proven to be useful in reasoning under uncertainty and to be
able to provide users a justification of the decisions.
ExpressIF, CEA’s fuzzy expert system
ExpressIF is a software composed of an API (Advanced Programing Interface) and a
GUI (Graphic User Interface) which allows to manipulate the API. The GUI (Figure 5) is based
on a drag-and-drop principle to parameter and to author rules which has been shown to be more
convenient to all kind of users than the other interfaces [9].
Figure 5 : Authoring rules with ExpressIF
ExpressIF has been thought to be used on static or dynamic data to address a large range
of problems and applications [10]. Beyond the innovation on the GUI, the expressivity of the
rules in ExpressIF benefits from different researches in order to bring new operators: for
instance, temporal relations (such as “before”, “persists”, “occurred”,…) [11] or spatio-
temporal relations (such as “crosses”, “moves”, “comes to”,…) [12].
Contrary to other fuzzy expert systems, ExpressIF has no limitation in terms of number
of inputs, outputs, linguistic variables, and of membership function shapes. Moreover, in order
to ensure that the system can interface easily with various information systems, specific
architectures were avoided. It was aimed to develop a software for data streams treatment on
normal CPU platforms.
ExpressIF is a tool of choice to model complex expertise in fields where model
intelligibility is key. In what follows, we describe its adaptation to DCVG defects selection.
Fuzzy expert system for DCVG analysis
In this section, we detail the two successive stages of the development of a fuzzy expert
system tool dedicated to the analysis of DCVG defects. First, we describe the generation of sets
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of rules that model TIGF’s expertise. Then, we introduce the user interface designed to
efficiently support excavation choices based on DCVG data.
Knowledge base specification
The knowledge base is a key component of an expert system that lists all the decision
rules for the task at hand.
To model TIGF’s knowledge on DCVG data analysis, more than 300 rules have been
defined. These rules allow for the assessment of 9 evaluation scores based on 26 input factors.
Each of the outputs is related to a risk whose appreciation can motivate an excavation decision:
mechanical attack,
insufficient cathodic protection,
stray current corrosion,
corrosion caused by alternating current,
corrosion under a disbanded shielding coating,
bacterial corrosion,
high-voltage corrosion,
pipeline damage history,
severity of the consequences of a metal defect.
For illustrative purposes, we introduce below some of the rules defined to estimate the
risk of corrosion due to alternating current.
First, we need to define the 3 following linguistic variables as inputs for our rules:
Type of coating 𝑽coating (Figure 6):
o defined on 𝑋coating = {Unknown, Unknown tape, Wax tape, Bitumen tape,
PE tape – factory, PE tape – one site, Coal-tar pitch,
Bituminous, Epoxy, 2-layers PE, 3-layers PE, PP}
o characterized by the fuzzy set 𝑇𝑉coating= {sensitive to stray currents}
Coating holiday surface 𝑽surface (Figure 7):
o defined (in cm²) on 𝑋surface = [0, 100]
o characterized by the fuzzy sets 𝑇𝑉surface= {small, medium, large}
AC voltage 𝑽AC voltage (Figure 8):
o defined (in V) on 𝑋AC voltage = [0, 30]
o characterized by the fuzzy sets 𝑇𝑉AC voltage= {low, medium, high, very high}
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Figure 6: Fuzzy set characterizing the coating type
Figure 7: Fuzzy sets characterizing the defect surface
Figure 8: Fuzzy sets characterizing AC voltage
Un
kno
wn
Un
kno
wn
tap
e
Wax
tap
e
Bit
um
en t
ape
PE
tap
e –
fact
ory
PE
tap
e –
on
sit
e
Co
al-t
ar p
itch
Bit
um
ino
us
Epo
xy
2-l
ayer
s P
E
3-l
ayer
s P
E
PP
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We also define a linguistic variable for the estimated output:
Risk of corrosion caused by alternating current 𝑽AC corrosion risk (Figure 9):
o defined on 𝑋AC corrosion risk = [-20, 120]
o characterized by the fuzzy sets 𝑇𝑉AC ccorrosion risk= {null, very low, low, medium,
high, very high}
Figure 9: fuzzy sets characterizing the risk of AC corrosion
Based on these variables, we can specify logical rules using natural language. The
decision model is therefore easily understandable. Here are some examples of the created rules:
1. IF coating type is NOT sensitive to stray currents THEN risk is
null
2. IF coating type is sensitive to stray currents AND surface is large
AND AC voltage is low
THEN risk is very low
3. IF coating type is sensitive to stray currents AND surface is medium
AND AC voltage is very high
THEN risk is high
User interface
The rule sets coupled with ExpressIF inference engine constitute the core of the
decision-support system. Their design require major contributions of the field experts. From
this point, the knowledge incorporated into the system can be interrogated by anyone via
requests on real coating defect cases.
In order to adapt the system to the specific task of DCVG defects priorization, a
dedicated interface is developed. The main implemented feature is a synthetic view of all the
DCVG defects loaded for analysis that are displayed with 9 visual indicators. Each indicator
stands for an assessed risk. The list can be sorted by these scores so that the most critical cases
stand out in a straightforward manner.
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Another feature worth mentioning is the possibility to display the rules applied for
evaluating each risk. The ranking is therefore transparent. This greatly simplifies feedback
integration in a virtuous circle of knowledge improvement.
Figure 10: Dedicated user interface with a synthetic view of the risks
Preliminary results
The fuzzy expert system and its user interface are fully operational and have been used
to support TIGF excavation decisions in 2016. The system shows remarkable performance in
terms of computation time: it only takes a few seconds on a standard machine to analyze over
7 000 coating defects.
Moreover, a study conducted on about 40 excavation feedbacks has already helped to
identify several axes for rules improvement. Future work will focus on fueling this virtuous
circle thanks to additional excavation feedbacks.
Conclusion
DCVG surveys provide crucial information to fight against pipeline corrosion. It
appears as a necessary complement to in-line inspection. Nevertheless, interpreting DCVG data
is a critical task: while ensuring the network integrity is a key priority, useless excavations are
costly in terms of time, money and effort. TIGF has set up efficient data management protocols
to gather any information that could guide this analysis. Yet, the processing of this data is a
matter for experts who are required to sort thousands of specific cases while taking into
consideration multiple factors influence.
In this work, we propose to address this issue by means of a support decision tool based
on CEA’s fuzzy expert system, ExpressIF. This solution offers the advantage of providing an
intelligible model on which the experts keep control. Unlike a black box model, any decision
made by the system can be easily understood and improved through feedbacks.
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The development of this tool has required the definition of more than 300 rules applied
to 26 input variables. The resulting system is capable of assessing in just seconds the severity
of thousands of coating defects. For each defect, a map of risks is produced based on 9
independent scores that allows to identify quickly cases requiring further inspection. The rule
sets implemented initiate a positive feedback loop based on excavation results. Further work
will focus on the exploitation of excavation outcomes to improve the model.
References
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