LPIT C-Scan Enabled Characterization of MFL Signals using Artificial Neural Networks

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20 Οκτ 2013 (πριν από 4 χρόνια και 8 μήνες)

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RTD Proposal Ver 1


Scan Enabled Characterization of MFL Signals using Artificial
Neural Networks

A Proposal Submitted To

Munendra Tomar

ExxonMobil Research & Engineering


Shreekanth Mandayam, Ph. D.

College of Engineering

Rowan University

201 Mullic
a Hill Road

Glassboro, NJ 08028

Executive Summary

Rowan University will design, develop and test a system for
magnetic flux
leakage signals using artificial neural networks

The networks will be trained using LPIT C
signatures and w
ill be used to predict 3
D profiles of pipe
wall corrosion anomalies.

RTD Proposal Ver 1


Project Description

The overall objective of a desired signal characterization algorithm is illustrated in Figure 1. The
matrix consisting of magnetic flux leakage (MFL) signal ampl

must be transformed
provide an equivalent matrix indicative of the corrosion profile

elements of this matrix have
amplitudes corresponding to the percentage of pipe
wall loss at a specified location. Artificial
neural networks are ideal mechan
isms for performing such matrix
matrix signal

A “brute
force” approach employing artificial neural networks for effecting MFL signal
transformations to predict corrosion profiles is illustrated in Figure 2 (a). The artificial neural
work operates in three phases. During the “training” phase, the network receives inputs that
are examples of MFL signals. The corresponding desired output for each input MFL signal is

these desired outputs are corrosion profile matrices that ar
e obtained following LPIT
laser C
scans of the pipe
wall. The “synaptic weights” of the neural network are determined at
this stage.
Following a successful training algorithm, that is indicated by minimizing specified
goals, the network is ready for
the “validation” phase. MFL signals, with known corrosion
profiles, but have not been previously introduced to the network are presented for analysis. The
network predictions are compared with the desired output. The network is said to be successfully
ned if it is able to perform this task without appreciable error. In the third, or “test” phase, the
network is deployed for analyzing unknown MFL signals.

There is a significant problem with this “brute
force” approach for training, validating and

this signal characterization neural network. In order to successfully train the network so
that it is able to analyze new MFL signals, a comprehensively large training data set is required.
Network training that employs a matrix
matrix transformation as d
escribed is very sensitive to
small variations in signal amplitudes, thus rendering prediction results for unknown signals

A two
pronged alternate strategy can be envisioned for analyzing MFL signals, especially if
scans are available
for a set of corrosion anomalies. The heart of this algorithm is the
determination of corrosion
profile information contained within the MFL signature that is
common with the same information contained in the LPIT C
scan. Also, one can determine
profile information that is missing in the MFL signature, but is available in the LPIT
scan. Results obtained from a pair of artificial neural networks that can be trained to predict

RTD Proposal Ver 1


these two separate sets of information can be combined to accurately

and confidently estimate
the true corrosion
profile. The following paragraphs describe this strategy in more detail.