Manoj Kumar and Sorabh / Mechanica Confab
ISSN: 2320-2491
INSPECTION OF PIPELINES USING MFL TECHNIQUE
Manoj Kumar1 and Sorabh2
1
P.D.M College of Engineering, Bahadurgarh, India
Kumarmanoj787@gmail.com
2
Manav Rachna International University, Faridabad,
Sorabh012@gmail.com
Abstract
Non-destructive testing (NDT) is the inspection of sample for different physical defect without
altering them in aspect. Magnetic flux leakage (MFL) is the most widely used nondestructive evaluation (NDE) technique for operating gas and oil transmission Pipelines.
This technique involves the magnetization of the ferromagnetic pipe wall to its saturation
level and measuring the leaking magnetic field from metal loss or anomalies. Defect
detection using MFL is mature area of work, but obtaining the defect signature from the MFL
signal is an interesting problem to deal with.
Keywords: NDT, MFL, IPIG
1. Introduction
Oil and gas provides 60% of the world’s primary fuel requirement. Pipelines provides
the most effective way of transporting oil, gas and petrochemical though it. These are the
arteries of the oil and gas transportation system. Any fault in the pipeline causes huge loss of
energy and money, apart from these because pipe contains flammable gases and liquid hence
there is a chance of explosion due to the faults. To avoid these it is necessary to inspect the
pipeline regularly. These transmission pipelines are also exposed to a wide range of climatic
conditions, varying from low to very high temperature desert conditions. Certain phenomena,
such as corrosion cracking tend to occur in low population density areas, others, such as
mechanical damage, occur very often in high. Population density areas which results in losses
of human lives. The most common cause of pipeline failure is mechanical damage: denting or
gouging of the pipeline by a third party or local deformation created by rocks or ground
movement. In many cases these fail under load or are detected immediately. In other cases
they may remain undetected for years, with the local damage acting as sites for further
corrosion or cracking and potentially leading to a delayed failure. Various nondestructive
testing (NDT) techniques based on X-rays, magnetic particle, eddy current, magnetic flux
leakage, ultrasound and visual inspection are available for locating defects in pipeline. Now
with the widespread application and fast development of gas and oil pipeline networks all
over the world, the pipeline inspection technology has been used more extensively. Among
those for inspecting the pipeline made up of ferromagnetic material and having anomalies
like corrosion, metal loss, gouge and denting, Magnetic Flux Leakage (MFL) method is
widely used technique
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Manoj Kumar and Sorabh / Mechanica Confab
ISSN: 2320-2491
1.1. MFL Technique in Pipeline Inspection
This section discusses the basics for understanding of Magnetic Flux Leakage
techniques as applied in pipeline inspection. MFL techniques are based on magnetizing a
particular portion of a ferromagnetic material to its saturation level and finding the leaking
flux generated by imperfections. Because the technique generally needs no mechanical
contact with the part being evaluated and is amenable to automatic signal recognition
schemes, MFL techniques have the advantage of automated and high-speed inspection. The
performance of this method depends on variables including fluid flow velocity, variations in
the pipeline material, and pipeline operating pressure.
Magnetic Flux Leakage Inspection process is initiated by magnets which does the
saturation work. Sensors capture the leakage flux information to predict the presence of
anomaly in the magnetic field. Any change in the magnetic property of the material reduces
the ability to carry the field which affects the leakage if any defect is present. Hence the
interpretation of the leak signal plays a vital role, because only the leak signal is not sufficient
enough to estimate the shape and size of the defect. If there is a flaw it will show a leak signal
for sure, but it doesn’t tell about the severity level present. As an instance let us consider the
case of corrosion and one sharp defect having same volume of material removed. In the case
of corrosion remaining material will be able to carry magnetic field(not as good as solid
material) hence it will cause leakage but the amplitude of the signal will be less as compare to
sharp defect. The level of severity may be more in any of the case stated depending on the
various parameters such as depth, length and width of defect.
Figure 1: Magnetic Field Created in Pipeline by PIG, (a) without anomaly, (b) with
anomaly
ILI (Inline Inspection Instrument) tool requires the enough magnetizing force so that
it can cause the leakage from anomaly. Hence it requires that magnetic field should be
consistent, uniform and strong enough to cause a measurable amount of flux to leak out,
caused by anomalies. Uniformity and consistency of the magnetic field in the pipe wall is the
key to get accurate
Signals, it also enables us to make adequate comparisons among different signals
from various anomalies. Applied magnetic field intensity (H) and the flux density (B) are two
important variables that have intense affect on the magnetization. The relationship between
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Manoj Kumar and Sorabh / Mechanica Confab
ISSN: 2320-2491
these variables is non-linear in ferromagnetic materials which are known as B-H curve. The
ratio of flux density (B) and magnetic field intensity (H) is defined as the magnetic
permeability. Permeability is also defines the ease with which the material can be
magnetized. Materials such as low carbon steels (pipe material) have very high permeability.
1.2. MFL Tool
Magnetic flux leakage tool magnetizes the pipelines hence it is necessary for the pipe
material to have the ferromagnetic property, most of the transmission pipelines are made up
of low carbon steel having high permeability which fulfils the requirement of being a
ferromagnetic material. MFL tools are of two types one is segmented with two or more
components joined by flexible connectors, or as a single piece where all the components are
contained within a single, rigid package. There are several kind of PIG available which serves
different purpose. Some are designed for cleaning purpose some searches for dents some PIG
provide an interface between two dissimilar products within the pipeline. Both kind of tool
incorporate several arrangements: drive system, magnetic system, sensor system (all sensors
are mounted circumferentially in multiple rows), data conditioning and recording system, and
power system. These are most widely used tool all over the world.
Figure 2: Instrumented Pipeline Inspection Gauge (IPIG)5
1.3. Actuating Mechanism
The main propelling force which runs the MFL tool is pressure of oil/gas which is
transmitted through the pipeline. It primarily runs due to the pressure difference between two
sides of the pig. Launching and retrieving the MFL tool requires a special system. This
launching and retrieving is done from the compressor stations or other easily accessible
locations. At the time of launching tool is to be forced against the fluid pressure while
because of that pressure it comes out of the pipeline.
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Manoj Kumar and Sorabh / Mechanica Confab
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Figure 3: Launching Mechanism of PIG
2. Literature Review
Empirical structure for characterizing metal loss defects from radial magnetic flux
leakage signal was studied by S. Saha at al.1 Methodology adopted for analysis of radial MFL
signal was discussed. The characterization of the defects is based on primary and secondary
parameters of the radial MFL signature. Primary parameters are axial and circumferential
spread and amplitude of the signature. A feature is detected by matching its pattern with one
of the known pattern in the defect or feature library. Different pipeline features such as valve,
sleeve, casing and thicker section are detected using different sets of rules, based on the
unique signature of these installations in the MFL data
Figure 4: Typical MFL Signatures, (a), Marker (b), Valve (c) and Weld (d) by the
Intensity of Gray Scale
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Manoj Kumar and Sorabh / Mechanica Confab
ISSN: 2320-2491
L. Clapha2 at al. studied on detection of mechanical damage using the magnetic flux
leakage technique. In experimental studies, mechanical damage was simulated using a tool
and die press to produce dents of varying depths in plate samples. MFL measurements were
made before and after selective stress-relieving heat treatments. These annealing treatments
enabled the stress and geometry components of the MFL signal to be separated. In general,
geometry effects scale with dent depth and tend to dominate in deep dents, while stress
contribution to the MFL signals is relatively constant and is more significant for shallow
dents. The influence of other parameters such as flux density and topside/bottom side
inspection was also quantified.
The work by A.A. Carvalho3 evaluates the use of artificial neural networks (ANNs)
for pattern recognition of magnetic flux leakage (MFL) signals in weld joints of pipelines
obtained by intelligent pig. Initially the ANNs were used to distinguish the pattern signals
with non-defect (ND) and signals with defects (D) along of the weld bead. In the next step the
ANNs were applied to classify signal patterns with three types of defects in the weld joint:
external corrosion (EC), internal corrosion (IC) and lack of penetration (LP).
3. Conclusions
The inspection of pipelines by MFL technique was discussed in this paper. A study of
different papers related to inspection techniques was also made. It is found that advancements
in MFL technique have made the data analysis from inspection tool very easy.
References
1. Ivanov P.A., Zhang Z. ,Yeoh C.H., Udpa L., Sun Y., Udpa S.S., Lord W., “Magnetic flux
leakage modeling for mechanical damage in transmission pipelines”, Magnetics, IEEE
Transactions on , vol.34, no.5, pp.3020-3023, Sep 1998.
2. Clapham, L. and Atherton, D.L., 1999, "Magnetic Flux Leakage Inspection of Oil and Gas
Pipelines" Proceedings of the Canadian Institute of Metallurgists Conference, Calgary,
Canada.
3. Clapham, L., Babbar, V., Rubinshteyn, A. “Proceedings of IPC2008 7th International
Pipeline Conference September 29-October 3, 2008, Calgary, Alberta, Canada.
4. A.A. Carvalho, J.M.A. Rebello, L.V.S. Sagrilo, C.S. Camerini, I.V.J. Miranda, “MFL
signals and artificial neural networks applied to detection and classification of pipe weld
defects”, NDT&E International, Volume 39, Issue 8, December 2006, Pages 661-667.
5. Collaboration for Nondestructive Testing. NDT Course Material - MPI, from
http://www.ndt-ed.org/EducationResources/Community
College/MagParticle/cc_mpi_index.htm
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