Autonomous Corrosion Detection in Gas Pipelines: A Hybrid ...read.pudn.com/downloads504/sourcecode/others... · Autonomous Corrosion Detection in Gas Pipelines: A Hybrid-Fuzzy Classifier

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2650 IEEE TransacTIons on UlTrasonIcs, FErroElEcTrIcs, and FrEqUEncy conTrol, vol. 56, no. 12, dEcEmbEr 2009

Abstract—In this paper, a customized classifier is presented for the industry-practiced nondestructive evaluation (NDE) protocols using a hybrid-fuzzy inference system (FIS) to clas-sify the corrosion and distinguish it from the geometric defects or normal/healthy state of the steel pipes used in the gas/pe-troleum industry. The presented system is hybrid in the sense that it utilizes both soft computing through fuzzy set theory, as well as conventional parametric modeling through H∞ opti-mization methods. Due to significant uncertainty in the power spectral density of the noise in ultrasonic NDE procedures, the use of optimal H2 estimators for defect characterization is not so accurate. A more appropriate criterion is the H∞ norm of the estimation error spectrum which is based on minimiza-tion of the magnitude of this spectrum and hence produces more robust estimates. A hybrid feature set is developed in this work that corresponds to a) geometric features extracted directly from the raw ultrasonic A-scan data (which are the ultrasonic echo pulses in 1-Dtraveling inside the metal per-pendicular to its 2 surfaces) and b) mapped features from the impulse response of the estimated model of the defect wave-form under study. An experimental strategy is first outlined, through which the necessary data are collected as A-scans. Then, using the H∞ estimation approach, a parametric trans-fer function is obtained for each pulse. In this respect, each A-scan is treated as output from a defining function when a pure/healthy metal’s A-scan is used as its input. Three de-fining states are considered in the paper; healthy, corroded, and defective, where the defective class represents metal with artificial or other defects. The necessary features are then cal-culated and are then supplied to the fuzzy inference system as input to be used in the classification. The resulting system has shown excellent corrosion classification with very low misclas-sification and false alarm rates.

I. Introduction

nondestructive evaluation (ndE) of gas pipes is a crucial expertise in any natural gas distribution sys-

tem. The purpose of this paper is to present an intelligent analysis system for identification and detection of chang-ing material characteristics of the underground or sub-merged gas pipes. The specific emphasis is on the metal loss due to corrosion (corroded state) and is compared

with the normal metal condition (healthy state) as well as with any man-made artifacts (defective state) such as drill holes, rivets, etc. In this paper the presented ndE procedures correspond to locating these metal health con-ditions, measuring certain features such as size, shape, and orientation, and calculating a relational decision to classify the condition on the metallic sample under study with respect to its health. Here, “size” and “shape” do not refer to the conventional understanding of the terms—the sizing, orientation detection, and shape detection of the actual defects—rather, they refer to the features related to the waveforms associated with these defects. The features (discussed in sections IV and V) incorporate the relative-size information of the peaks in the a-scans as well as their orientations (positive or negative, or 1st peak or 2nd peak, etc.) and also the shape of the impulse responses of the deconvolved system (being of sharp or shallow fall/rise in shape).

In general, the industry practice for ndE is person-nel dependent. Human experts are required at almost every step of the ndE process to make judgments and decisions based upon the measurements. Human factors have been identified recently [1] as the principal elements affecting the reliability of nondestructive examinations. Hence, there is a need for standardization of quantita-tive measures to ensure uniformity in the understanding of defect signatures and accordingly making appropriate decisions. an expert system has been developed here for this purpose using a fuzzy inference system (FIs). The FIs manifests the idea of associating degrees of member-ships (called fuzzification) to the input values from the real-world application and thus clustering them into a subset of possibilities, selecting the related sections of these memberships based on a subjective heuristic rule-base into a decision hyper-surface, and finally re-con-verting (defuzzifying) this hyper-surface into real-world quantities through a centroidal measure. The rule-base utilizes the expert knowledge from the field of the ap-plication for proper relational ordering of the graded in-puts into subjective but meaningful logical statements to show the relationship between various membership de-grees. a completely autonomous defect classifier is still a distant target, but the presented work in this paper is a confident step in that direction, or at least, a good sup-porting tool for human decision makers.

Autonomous Corrosion Detection in Gas Pipelines: A Hybrid-Fuzzy Classifier

Approach Using Ultrasonic Nondestructive Evaluation Protocols

Uvais a. qidwai, Member, IEEE

manuscript received January 12, 2009; accepted september 6, 2009. qatar University, doha, qatar, provided support for the funded project (05007cs), which resulted in this work.

The author is with the department of computer science & Engineer-ing, qatar University, doha, qatar (e-mail: [email protected]).

digital object Identifier 10.1109/TUFFc.2009.1356

A. Corrosion

corrosion of metals in aqueous environments is almost always electrochemical in nature. It occurs when 2 or more electrochemical reactions take place on a metal surface. as a result, some of the properties of the metal or alloy change from a metallic state into a nonmetallic state. The most familiar and often used categorization of corrosion is probably the 8 forms presented in [2] and [3]. This clas-sification of corrosion was based on visual characteristics of the morphology of attack. In this paper, localized cor-rosion is used as the primary type of corrosion and its extreme forms that results in pitting and metal loss are studied. It was observed that the 3 states; corroded, defec-tive, and healthy, can be distinguished using the technique presented here.

B. Ultrasonic Inspection

Ultrasonic inspection is a nondestructive method in which beams of high-frequency mechanical waves are in-troduced into materials for the detection of surface and subsurface discontinuities in the material [4]. These me-chanical waves travel through the material with some loss of energy (attenuation) and are reflected at interfaces. The pulse-echo method, which is the most widely used ultra-sonic method, involves the detection of echoes produced when an ultrasonic pulse is reflected from a discontinuity or an interface of a test piece [5].

Ultrasonic inspection can be used for the in situ moni-toring of corrosion by measuring the thickness of the sample walls with ultrasonic thickness gauges. To obtain corrosion rate, a series of thickness measurements is made over an interval of time, and the metal loss per unit time is determined from the measurement samples. signal pro-cessing has become an integral part of the ultrasonic ndE procedures practiced in the industry today, and several different techniques have been applied to various practical aspects of these procedures. a good collection of several of these techniques is presented in [6]–[9].

The a-scans can be considered as stochastic wavelets and hence several signal processing algorithms can be ap-plied to these signals. With specific interest in feature-based classification, the presented work in this paper rests its foundations on the previous pioneering works utilizing several signal processing algorithms on the a-scans to iso-late distinguishing features from them [10]–[12]. However, very few of these techniques have been actually utilized in industrial applications. This is mainly due to 2 reasons; time and complexity of the algorithm and inferior per-formance compared with the human expert. Hence, the usual industry practice is to have better filtering tech-niques applied in real-time, but the human expert makes the classification decisions. The a-scan features fall into a very large and diverse set of calculations ranging from simple statistical measures to complicated model coeffi-cients [13]–[16]. some of the commonly used statistical features are: mean, variance, skewness, kurtosis, peak po-

sitions and amplitudes with respect to the reference signal, higher order moments, and correlation coefficients. other features that have been developed by different research-ers are time-frequency-based features, wavelet coefficients, and higher-order statistics-based model coefficients [17]–[21]. Whereas time-frequency and wavelet analysis tools are considered to be one of the best methods to handle non-linearities, the time and complexity can prevent these techniques to be used in near-real-time applications. This is mostly due to the feature extraction part which either requires a 2-d search space or a more involved coefficient space.

The specific application of ndE algorithm to oil and gas pipelines represents a more challenging domain be-cause of the large volume to be assessed and the magni-tude of risk involved in case of a wrong decision. Here, specific emphasis is laid on 2 aspects; welded joints, and corrosive metal loss that reduce the thickness of the pipe. several researchers have attempted to come up with newer techniques for detection and classification of defects and corrosive losses [22]–[25].

In spite of a significant amount of previous as well as current research efforts in automating the process of ul-trasonic ndE, the reality is that an automated defect classifier system in this domain is still not in existence. Practically, industries rely heavily on the experienced hu-man technicians to perform the ndE procedures. Highly trained and experienced personnel are still needed and greatly depended upon for making critical decisions con-cerning the existence, nature, and severity of the defects in the sample under study. Usually, human decision mak-ing depends highly on the heuristic-perceptional-base ob-tained as a result of training and experience. because hu-man intellect for making decisions is not very discrete, a mingled or fused decision approach is also needed to map the human expertise into the computer-understandable forms. most of human reasoning and concept formation is linked to the use of fuzzy rules. by providing a systematic framework for computing with fuzzy rules, the fuzzy logic greatly amplifies the power of human reasoning.

It may be noted at this point that fuzzy set theory used in this work can be replaced with any heuristic classifica-tion method as well. neuro-fuzzy classifier [26], [27] can be another choice reducing the subjectivity of the designer and giving more realistic mapping and emphasis on the ndT expert’s decision rules. In case of such a classifier, the presented work may be used as a fundamental frame-work or as an initial structure which is modified further as the training is performed using the neural networks.

II. Experimental setup

In this paper, the corrosion data are collected using ultrasonic sensors in the form of standard a-scans, which essentially are a 1-d plot of the ultrasonic echo as it goes through the metallic body. by standard, it is meant that the utilized a-scans are the same as commonly used by

2651qidwai: autonomous corrosion detection in gas pipelines

ndE professionals in the field and all the characteristics involved therein, such as db settings of the flaw meter, fre-quency ranges of the transducers etc., are also kept at the usually practiced levels. This approach is not very conven-tional compared with the electromagnetic methods. How-ever, it was observed during the course of experiments, that the data selectivity and, consequently, the classifica-tion is much more simplified using ultrasonic data. This is simply because of the fact that ultrasonic pulses provide a very narrow field of view, which effectively pass through a narrow channel in the metal, although the mechanical waves do spread quite rapidly. The phenomenon of a re-sulting thin pulse is valid within a thin layer of the propa-gation medium and hence can be used for metallic pipes with the previously mentioned objectives. To collect data in the form of a-scans, several samples were selected for the above mentioned subset of corrosions. The experimen-tal setup and samples used are shown in Fig. 1.

The samples used were actual pieces of pipes from the local gas industry which were first tested radiographically to identify the healthy parts. Using a 5-mHz, 0° (compres-sional) ultrasonic sensor from Panametrics Inc. (Waltham, ma) with an effective bandwidth of 1 kHz, the data was collected as a-scans from the defects using Krautkramer Usm35X flaw detector (Krautkramer, Hürth, Germany) [28] along with the Ultradoc software Version 4.3 [29] that accompanies the equipment. once the data was collected, it was saved as screen shots in the memory of the flaw

detector. It is then connected to the Pc using an rs232 interface and specialized software, Ultradoc, provided by the Krautkramer Inc. some processing was done on the data formatting of the stored waveforms to obtain the ac-tual sample values. This processing was done in maTlab 7.0 (The mathWorks, natick, ma) using custom-devel-oped image-to-data transformations. some of the obtained waveforms are shown in Fig. 2. These are the original screen shots from the flaw meter and as per normal indus-try practice, the x-axis represents the depth of penetra-tion (from 0 to 12.5 mm with 2.5 mm divisions) and the y-axis corresponds to the normalized amplitude (the db numbers written on the top left corner are the only avail-able magnitude multipliers; the bigger the number, the weaker the signal is). Usually the y-axis grid markings are not known, because for ndT applications it is enough to have a normalized amplitude that can be compared with a threshold gate level (solid horizontal lines) to indicate the presence of a peak. The same description is applicable to Figs. 6 and 7(b).

III. H∞ deconvolution

To get a better picture of the state of metal, the pulse echo from the defining state function should be isolated as much as possible from the measurement system response signal. This motivates signal analysis in the impulse re-


Fig. 1. Experimental setup for data acquisition, (a) sample pipe and pulsar, (b) Pc interface, (c) sample showing the 3 classes: defect, corrosion, and healthy metal.

sponse domain. Here, a particular class with a specific ge-ometry is modeled as a linear time-invariant (lTI) system, such that each class is characterized by its corresponding “impulse response” [13].

let h(t) be the impulse response of a particular class (healthy, corroded, or defective) u(t) be the measured a-scan for the pure healthy metal, and y(t) be the measured ultrasonic pulse echo, then the output of this lTI system may be expressed as the following system as shown in Fig. 3 and in (1),

y t h t u t t( ) = ( ) Ä ( ) + ( )h , (1)

where η(t) is the additive output noise (can be Gaussian or non-Gaussian [13]) and ⊗ stands for time convolution. Estimation of h(t) in (1), given the measured pulse echo and the measurement system response, is also commonly known as system identification, filtering, or simply as de-convolution.

because the main purpose of the deconvolution algo-rithm is to obtain a model for the underlying impulse function that has performed the convolution with the in-put wave, the model thus obtained could be represented by several known methods. The autoregressive moving-average (arma) model is a very commonly used linear approximation model for a real system. The ease of usage of the coefficient-based model as well as the suitability of its structure for implementation as filter-banks makes it a good choice for near real-time applications. The only objection on such a model is that of order selection. This was done in the proposed work as a pre-processing step through brute-force selection of the order. several model orders were tried out first using a range of orders in a brute-force style of all combinations of number of orders for numerator and denominator coefficients. For each structure, the impulse response is then compared with the impulse response of a known sample response of the same class. Finally, the best fitting model was selected. The re-sults from such an experiment are shown in Table I.

In Table I, na indicates various denominator orders tried, from 1 to 10; nb represents various numerator orders used, from 1 to the denominator order for that iteration. This is done to keep the resulting transfer function proper (i.e., order of the numerator polynomial ≤ order of the de-nominator polynomial). Using the resulting transfer func-tion and the a-scan from a healthy metal as input signal, an estimated output is generated. The entries in the table


Fig. 2. obtained a-scans, (a) healthy metal, (b) corroded metal.

TablE I. results of Experiment to determine the best Fitting model.

nb

na

1 2 3 4 5 6 7 8 9 10

1 0.287 0.075 0.147 0.062 0.087 0.077 0.092 0.059 0.053 0.0712 0.068 0.052 0.174 0.172 0.115 0.128 0.145 0.108 0.0873 0.111 0.078 0.120 0.143 0.058 0.107 0.068 0.1384 0.060 0.058 0.168 0.139 0.173 0.530 1.0395 3.859 3.827

na = number of orders in the denominator; nb = number of orders in the numerator.

Fig. 3. The ultrasonic defect model.

represent the mean squared error (msE) between this es-timated signal and the actual recorded a-scan signal for a specific defect. only those values that are less than 5 are shown in the table. This is the reason why there are no en-tries corresponding to nb > 5 and other empty cells in the table. a similar analysis was conducted for various defect signals and in general, the order that was found to mostly produce the minimum error was na = 3, and nb = 2.

Utilization of deconvolution based on H∞ tools [3], [31], [32] is carried out as part of the proposed technique in this work using real ultrasonic data. motivation for using the H∞ tools comes from the fact that, because of a changing environment around ultrasonic experimentation and lack of exact modeling of receiver, transmitter, and propaga-tion path impulse responses, it is not reasonable to as-sume, in many practical situations, exactly known inputs

and models of the transducers and the medium. specifi-cally, the noise is predominantly non-Gaussian which is one of the main requirements for almost all of the prac-tical H2 norm-based filters and estimators. These often unrealistic assumptions are essential in the development of least-square-based algorithms. H∞-norm-based filters and estimators are more robust to these variations because the only assumption made in this case is that the noise is bounded, thus eliminating the shortcomings arising from the a priori noise distribution requirements. In this paper, an existing algorithm for H∞ deconvolution is implemented to identify the defect impulse response. The H∞ identifica-tion scheme used here is essentially adopted from [13] and its algorithm is shown in Fig. 4. This technique has been previously tested with real a-scans and has been found to be very robust to noise as well as changing environmental


Fig. 4. Flowchart for H∞ deconvolution algorithm [13]. Here gk is the actual measurement data, fk is the reference signal, Ψk include input-output samples in a regressive fashion, θk is the vector of N unknown coefficients, k is the iteration count with each iteration corresponding to each sample set for input and output signals, G and P are non singular N × N matrices, λ represents the eigenvalue vector for P, γ and ξ are dynamic weights.

conditions [13], [33]. This is not the main work presented in this paper; however, the flowchart is reproduced in Fig. 4 for the sake of completeness.

The resulting identified system is in the form of an arma process and can be expressed as a transfer func-tion as follows:

H z

Y z

U z

b b z b z

a z a z a zn

n

n

bb

a

--

-

- -

- -( ) = ( )( )

=+ + +

+ + + +1

1

10 1

1

11

221

--n a,

(2)

where na and nb are the degrees of the polynomials in the denominator and numerator, respectively, and H, Y, and U represent the frequency domain representations of impulse response, measured output, and input from the ultrasonic sensor. The orders are to be supplied to the algorithms a priori and the algorithm iterates to identify the unknown coefficients ais and bis that best define the underlying system for the given input and output signals.

IV. Feature space

The actual understanding of the waveforms is obtained by the program through a set of measurable values, called features. To select a meaningful feature set, human heu-ristics were utilized. as mentioned in the abstract and the introduction, the usual industry practice in ndE is very much personnel-dependent. The ndE technicians look at the a-scan and based on their experiences they would classify the underlying defects. To automate this process, the heuristic perceptions of the human operators must be quantified into some measureable parameters (or features). The features selected for the work presented in this paper are, therefore, based on translating human un-derstanding into the mathematical quantities and is done here using 2 sources:

1. human heuristics from ndE professionals, and 2. deconvolved system’s impulse response.

A. Human Heuristics

To understand the mechanism of how a human expert would classify the metal health using a-scans, several ex-perienced ndE experts were interviewed from the local in-dustries in qatar, including qatar Petroleum, rasGas co. ltd., and GE-PII ltd. UK. In addition to these experts, several others all over the world were contacted with a similar questionnaire in which basic questions were asked given a certain waveform in the questionnaire. These wave-forms were also presented to the local experts in person and discussed. basically, the waveforms were the true a-scans for the classes of defects under study and are shown in Fig. 5. again, in all of the waveforms shown, actual screen-shots are reproduced in Fig. 5, which implies that the x-axis in all the cases represents the distance trav-

eled by the waveform in mm (not necessarily a time-based scale) and the y-axis represents the normalized magnitude of the wave. This is the case for Figs. 5(a)–(c),whereas the remaining waveforms do not show even the length values. However, as stated previously, this is the usual industry practice because the most important information needed is related to the shape and its spatial location.

The overall opinions are summarized in the following:

1. cracks usually form very sharp and very distinct peaks without any neighboring peaks.

2. In case of multiple cracks located very near each other, the same type of distinct peaks will repeat in close proximity but the first peak will have dominant amplitude.

3. slag and porosity form serrated peaks. This is due to several uneven reflections taking place in a close neighborhood with almost the same energy and hence the diffused reflected peaks represent the same amplitudes.

4. lack of fusion (loF) forms a variation of slopes in the rising and falling sides of the peaks. This is due to the difference of speed of the ultrasonic wave that happens in the void region where the weld was not done properly.

5. Usually, slag and porosity present very similar wave-forms and are extremely difficult to identify. The same is true for cracks and loF.

6. The loss of an echo at the same db scale (decibel strength settings of the flaw meter) but different loca-tion is caused by loss of ultrasonic wave energy from either roughness of area or major/minor nonartificial (i.e., not man-made) metal loss such as pitting, etc.

7. If db scale is increased at this new location, a very wide and rough echo will appear from this location. This means that at a lower db scale in the flaw meter, the corrosion waveform would appear as very small amplitude waveforms in the bottom only, something similar to a noise pattern. However, when the scale is changed, the overall shape is enhanced and the noise pattern becomes more detailed and starts to show more patterns in it. This is not similar to the effect of noise amplification in an oscilloscope. rather, the higher db scale settings in the flaw meter would en-able catching even very small reflections caused by a rough corroded surface.

8. amount of noise level signals (<25% of the peak) called the grass represents the existence of corrosion. This can be typically observed when a change of db scale by 20 occurs. The noise amplification will not affect the grass pattern if grass exists.

9. The change in position of echo at a new location represents a major loss of material at the base metal. The term base metal is usually used in context of welding when the molten metal is poured into the voids between 2 pieces of base metal. In this context, when ultrasonic inspection is performed, the reflec-


tion from the base of the weld (or root) represents the base metal.

10. The change in shape and position of the echo is caused by large localized pitting with many sharp edges (mechanical wave deflectors) or by a very rough inclusion in the metal.

11. The shape of a corrosion echo is rough because sound is traveling a different distance due to corrosion. The idea of roughness of the a-scan can be comprehend-

ed easily by comparing the a-scans of Fig. 5(a) and Fig. 6(b). Here the shape and width change caused by pitting depicts this difference in the traveled dis-tance.

12. The smooth echoes are mostly from flat bottomed holes or any machined surface.

13. an angular deflection would cause a delay in the echo’s travel time, causing the waveform peak to oc-cupy slightly wider base than normal.


Fig. 5. Various types of a-scans corresponding to (a) pitting, (b) light corrosion, (c) slag, (d) porosity, (e) crack, and (f) lack of fusion.

B. Impulse Response Features

When mapping from the heuristics to the numbers is started, the procedures for automatically doing this be-come very complex. specifically for the a-scans obtained directly from the flaw meter, there is a lot of overlap and confusion in the shape and some of the main characteristics as discussed above. This is where the deconvolution can become very useful. To model the ultrasonic pulse echoes for the deconvolution procedure, a clean signal was used as the system input. This was obtained using a calibration sample (called kidney) of the same metal as that of the actual sample under study and of the same thickness. The kidney sample used in the data acquisition phase is shown in Fig. 6(a) and the resulting signal is shown in Fig. 6(b). because the shape of the echoes from kidney resembles the impulse function, it becomes an ideal input source signal for the deconvolution process which only restricts the input to be persistently excited with the same number of modes as the unknowns to be identified.

Using this reference input with the measured signals from various types of samples with corrosion, defects, and maybe simple metal (healthy metal), the said deconvolu-tion scheme results in a transfer function which is the defining function for a healthy, corroded, or defective state of the metal. The healthy class is only a class of interest to the application and it is healthy relative to the corroded or defective samples. other than these differences, there might be several other possibilities of minor inclusions, very little corrosion, or even minor variations in the den-sity of the sample, but it will still be called healthy for 2 reasons:

1) compared with the other 2 classes it is much more workable and strong in condition, and

2) such minor things are within the tolerance boundar-ies of the industrial practice.

once the transfer function is obtained for a pair of input-output data, features are extracted from the im-pulse response waveform for this transfer function, which

is more clear and useful in classification processing than the ambiguous raw data. This is a third-order transfer function that utilizes 3 previous samples from the defining function output and one sample from the clean reference signal to predict the next output sample of the defect sig-nature. This implies that nb is 1 and na is 3. some of the resulting impulse response plots are shown in Fig. 7, along with the transfer functions obtained. In all of the plots in Fig. 7, the x-axis represents the sample index (k) and the y-axis represents the normalized amplitudes.

For the sake of visual comparison, Fig. 8 shows some more sample impulse responses. The following observa-tions were obtained from the impulse responses from sev-eral samples:

1) Healthy metal usually shows multiple significant peaks as well as negative peaks.

2) crack shows a very slow descent of the main peak wavelet.

3) Porosity shows a very well-formed and thin peak wavelet.

4) corrosion does represent some sluggish descent of the peak but represents diffused peaks as well.

C. Feature Mapping and List

To map these findings from both the geometric heuris-tics of the experts as well as the impulse-response-based visual features, some types of mapping operations are de-fined and are referred to as the main features used in this work. basically, these features are outlined by the proce-dures to encompass the heuristic features listed above. For instance, an amplitude to width ratio (aWr) can be used to distinguish between a thick and a thin peak wavelet as well as diffused wavelets, amplitudes of adjacent peaks can reflect on the serrated nature of the wavelets, slope ratio will distinguish the slow descent versus the steep descent of the impulse response, db values are used for the determination of the corrosion grass signals versus the noise signals, and the area under the grass will represent


Fig. 6. source of the input reference signal u(t), (a) 10-mm thick “kidney” calibrator block with the ultrasonic probe, and (b) waveform representing the calibration peaks according to the design of the block. only the first peak was used as the input reference signal.

the strength of the corrosion. These features are listed in the following:

F1) number of peaks in the impulse response (“nP”).F2) amplitude of the 1st peak (“1st peak”).F3) ratio between 2nd and 1st peaks (“Pr”).F4) amplitude to width ratio of the major wavelet (“ar”).

F5) ratio of the slopes of the 2 halves of the tail of the impulse response (“sr”).

F6) measurement db settings of the instrument (“db”).

F7) area under the grass (where grass level is below 25% of the peak wavelet).

F8) cross correlation value by convolving the base waveform from a sample with various corrosion or de-fect waveforms from the same sample. This is repre-sented mathematically as “cXy.”

The additional feature F8 which has not yet been dis-cussed was used in the feature list with the idea that cor-relation of the healthy metal signal with the reference sig-nal will be higher than the corroded or defective metal

signal. all of these features were calculated for 27 different a-scans (or parts of a-scans) and ranges were calculated for various types of feature values. The main emphasis in this paper is on the detection of significant corrosion in the sample and the system distinguishes healthy metal from a corroded one as well as any other type of defect. However, individual defects will not be isolated and a gen-eral class of defects is targeted. These details are discussed in the next section.

V. Fuzzy Inference system

by quantifying the human perception according to the general understanding by the ndE expert of the informa-tion present in the measurements, a better classification is obtained that will assist the technicians to make a better decision concerning maintenance, material selection, and replacement. Fuzzy logic is probably the most suitable tool for this purpose. although it is a wonderful tool to map the imprecise or heuristically oriented information into quantitative data, it also maps the human decision making capabilities using a subjective rule base. This may


Fig. 7. obtained impulse responses and corresponding transfer functions, (a) corroded metal, (b) defective metal, and (c) healthy metal. (x-axis represents sample index and y-axis represents the impulse response amplitude.)

raise some issues related to the accuracy because it is based on less precise information, and the usefulness of the system on the whole may be questioned. actually, when limitations of ndE personnel in the field are consid-ered, these are considered subjective to the system under study. This means that the accuracy of the ndE process is limited by expertise of a single individual and by the classical limitations associated with human operators such as tiredness, physical and mental accuracy, and proper understanding of the problem.

The proposed FIs is shown in Fig. 9, and is composed of the following:

•Eight input membership descriptors (representing the feature space),

Three output membership descriptors (with 3 degrees •of membership: marginally, somewhat, and most likely for each of them), anda set of rules that connects various degrees of mem-•berships to the outputs.

A. Input and Output Memberships

For each feature input, a set of associations were de-fined as a function of Input-membership Functions. The way in which these memberships were assigned various degrees is outlined in the following:

1) all the input feature values are tabulated for the known output classes (healthy, corroded, and defec-tive) [refer to Table II].


Fig. 8. sample impulse responses for various types of metallic samples. (x-axis represents sample index and y-axis represents the impulse response amplitude.)

2) From the entries in the table, ranges of values are identified for the 3 output classes.

3) In this work, each membership class is broken down into 3 membership degrees. Each degree is given a specific meaningful name related to the type of fea-ture membership under consideration. For example the membership class “number of peaks (nP)” has values within 3 membership degrees of few, many, and more.

4) Each of these degrees can now be represented by a mathematical function that will map the input value of the feature with its functional weights to produce the fuzzified version of the input data.

5) In maTlab, there are 11 different types of built-in membership functions. Which function would give the best result was decided upon by a brute-force selection strategy in which the 5 most popular membership functions; triangular (called trimf func-tion in maTlab), Gaussian (gaussmf), trapezoidal (trapmf), z-function (zmf), and s-function (smf) were used. Each time a different combination was used for membership degrees and tested with the same rule base to check for the accuracy of classification.

6) The resulting membership functions are a combina-tion of various membership functions and are shown in Fig. 10.

7) The output variables correspond to the 3 degrees of membership representing 3 conditions of the metal under study. Each one of these memberships is evenly distributed triangular distributions corresponding to marginally, somewhat, and most likely degrees. Their numeric distribution is described by the following:

a) marginally: Full trimf between −40% to 40% with center based at 0%

b) somewhat: Full trimf between 10% to 90% with center based at 50%

c) most likely: Full trimf between 60% to 140% with center based at 100%

The naming convention as well as the order of the mem-bership functions are ad hoc in nature and are dependent upon the user’s understanding of the system. The x-axis in all of the curves shown in Fig. 10 represents the input values for each input membership function. The y-axis is from 0 to 1 representing the overall probabilistic space.

B. Rule Base

The set of rules is an intuitive collection of antecedents and their consequents which most humans will agree with. In this case, a set of 11 rules was established based on hu-man heuristics only. These rules are outlined as follows:

1) If (nP is few) and (1st-amp is large) and (peak-ratio is medium) and (ar is normal) and (slope-ratio is large) then (healthy is most-likely)(corroded is mar-ginally)(defective is marginally)

2) If (nP is many) and (1st-amp is medium) and (peak-ratio is medium) and (ar is corroded) and (slope-ratio is large) then (healthy is marginally)(corroded is most-likely)(defective is marginally)

3) If (nP is more) and (1st-amp is small) and (ar is defective) and (slope-ratio is medium) then (healthy is marginally)(corroded is marginally)(defective is most-likely)

4) If (ar is corroded) then (healthy is marginally)(cor-roded is most-likely)(defective is marginally)

5) If (nP is more) and (1st-amp is small) and (peak-ratio is small) and (ar is corroded) then (healthy is marginally)(corroded is most-likely)(defective is marginally)

6) If (nP is few) and (1st-amp is small) and (peak-ratio is small) and (ar is normal) and (slope-ratio is large) then (healthy is most-likely)(corroded is mar-ginally)(defective is marginally)

7) If (db is normal) and (ar is normal) and (Grass is normal) and (cxy is normal) then (healthy is most-likely)(corroded is marginally)(defective is marginally)


Fig. 9. The FIs structure for the defect classifier. The FIs has been developed using maTlab’s Fuzzy logic tool box [34].

8) If (db is defective) and (ar is defective) and (Grass is defective) and (cxy is defective) then (healthy is marginally)(corroded is marginally)(defective is most-likely)

9) If (db is corroded) and (ar is corroded) and (Grass is corroded) and (cxy is corroded) then (healthy is marginally)(corroded is most-likely)(defective is marginally)

10) If (db is defective) then (healthy is marginally)(cor-roded is marginally)(defective is most-likely)

11) If (db is normal) then (healthy is most-likely)(cor-roded is marginally)(defective is marginally)

rules 1, 2, 3, and 5 are applicable to the features from the impulse response of the deconvolved system, whereas the remaining 7 are related to the a-scan directly because they deal with the geometric features of the waveform.

The design of these rules are intuitive or more correctly perceptive in nature. This can be seen from 3 perspec-tives:

1) none of the rules actually utilize any form of statis-tical or algebraic decision boundary. rather, they are simple relational statements to implement the logical links between the membership values and the output classes.

2) The components of the rule base are directly related to the way the membership degrees were defined. For instance, it is established from Table II that for a healthy metal, number of peaks should be very few (1 or 2), with a large peak amplitude, with a small peak width, and the descent slope is sharper than the ascent slope for the peak in the impulse response. Hence the intuitive way of designing a rule


TablE II. membership degrees selection for the Feature space.

F1 = number of Peaks (nP)

F2 = 1st amplitude (1st amp)

F3 = Peak ratio (Pr)

F4 = slope ratio (sr)

F mn mr s s s s md l s md l

H 10 15 −0.6 −0.6 −0.6 0.12 2.13 −0.6 1.5c 9 12 14 0.4 0.4 0.4 0.07 1.8 0.4 2.2d 18 3.8 1.14

F5 = decibel settings (db) F6 = amp ratio F7 = Grass F8 = correlation

n d c n d c n d c n d c

H 56–68 0.03–0.047 0.9–1.6 1c 64 0.02–0.03 0.5–0.76 0.5–0.76d 72–80 0.02–0.06 1–1.6 0.1–0.94

F = Few, mn = many, mr = more; s = small, md = medium, l = large; H = Healthy, c = corroded, d = defective, n = normal. Empty cells represent no data item in that range.

Fig. 10. Various components of the FIs. Input membership functions for (a) F1 = nP, (b) F2 = 1st amp, (c) F3 = Peak ratio, (d) F4 = slope ratio, (e) F5 = db, (f) F6 = ar, (g) F7 = Grass, (h) F8 = cxy, and (i) output membership function(s) healthy, defective, corroded. (x-axis represents membership input variable values and y-axis represents the probabilistic space between 0 and 1.)

for this classifier would be the truth value for all of these implications, as presented in rule#1 above.

3) obviously, there are other possibilities which were not exhaustively tested as part of the presented work. also included in this respect are emphasized features that drastically increase the level of confi-dence in the classifier. For example rule#11 cor-responds to a major emphasis on a single feature which is specifically related to only one class, i.e., healthy. This feature alone was found to be more favorable in detecting the healthy metal compared with the other classes and hence can be used as a simple rule by itself.

once these implications are established, an overall de-cision surface is precalculated. For each set of input val-ues, the centroid is calculated for the area of this decision surface that overlaps with the decision rules applicable to

the input memberships. The centroid value is an indicator of the degree to which the inputs correspond to the rule base and consequently provide a number that depicts the degree of output, i.e., to what extent is it calculated to be slag, porosity, crack, or lack of fusion. The resulting decision surfaces are multidimensional and cannot be dis-played as one hypersurface. However, some subset of deci-sion surfaces can be plotted and are shown in Fig. 11.

VI. results and conclusion

The proposed system was trained with 27 different samples with a mixture of healthy, corroded, and defec-tive types (healthy = 3, defective = 11, and corroded = 13 samples). The input features were calculated for all the input waveforms and then fed to the FIs to calculate the outputs. The FIs decisions were based on the range selec-


Fig. 11. decision surface for (a) healthy state for db and ar, (b) healthy state for sr and FP, (c) corroded state for Pr and nP, (d) corroded state for ar and Pr, (e) defective state for sr and FP, and (f) corroded state with Grass and db. Here, db is measurement decibels, ar is amp-ratio, Pr is peak-ratio, sr is slope-ratio, FP is 1st-amp, and nP is number of peaks. mc is marginally corroded.

tion such that the 3 classes were isolated as healthy, defec-tive, and corroded. The defuzzification resulted in a value for all 3 output memberships and the largest one was se-lected as the final class. The results of this training are shown in Table III. It is intriguing that although there is no concept of training the FIs similar to a neural network or genetic solution pool, the initial structure was formu-lated using known data set values which is being referred to as the training data. However, even with the training data set, only a set of range coordinates were known and actual classification was still done somewhat blindly.

To further confirm the functioning of the algorithm and the accuracy of the FIs, an entirely new set of 10 samples was obtained (healthy = 3, defective = 4, and corroded = 3 samples). The samples used are similar to the ones used in the training phase in terms of metal (steel), thickness (10 mm), and visually similar corroded surfaces as well as created defects. The results with this test data set are shown in Table IV.

The results of overall FIs classification with the said data sets can be expressed in numbers as follows:

correct corrosion classification 87%,•correct defect classification 100%,•correct healthy metal detection 94%•misclassification for defect or corrosion 3%•False alarms for defect or corrosion 5.4%•

according to the current industry practice, these fig-ures are quite acceptable and efforts are being made to

improve on the values by utilizing more rules and more membership degrees in the input features. In general, a misclassification rate of up to 10% and false alarm rate of no more than 20% is acceptable in the industry.

The main reasons for the misclassification and false alarms are listed below:

1) The rule base needs to be fine-tuned for more bal-anced coverage of various combinations of the mem-bership values.

2) not enough single or dual relation rules to bring more emphasis to one class or the other.

3) nonuniform data sampling procedure because that is still human dependent.

In general, the proposed technique has demonstrated the algorithmic structure and its feasibility for making hu-man-like decisions for defect classifications. The mapping of human understanding took place at the stage when the apparent shape features were converted into actual mea-surable features and a simple rule base which is organized into the form of human perceptive thinking. This proce-dure combines the heuristic reasoning from several experts and produces a much superior accumulative knowledge base with computerized precision to be able to make bet-ter decisions, avoiding the limitations caused by subjec-tive decisions taken by human personnel. To the best of our available resources, we found no such automated sys-tem exists in the industry at the moment. The presented work can enhance the decision-making process of ndE technicians and can help them in making intelligent deci-sions in terms of removing false-positive detections. This might eventually result in significant savings in mainte-nance cost. Further extensions of the work are underway with rasGas co. ltd., qatar; GE-PII co. ltd., UK; and qatar University on parallel collaborative projects for im-plementing this technique in real-time pigging operations (i.e., pipeline inspections for oil and gas industry).

an important observation from the presented FIs is the fact that the actual intelligence of the system can be pre-calculated and stored as look-up tables. This makes it ideal for implementation in embedded settings such as FPGa-based hardware or dedicated controller boards for ranking and classifying the coarse data being obtained dur-


TablE III. comparison of results for Training Part.

FIs output class

normal corroded defective True FIs

0.82 0.50 0.50 1 10.81 0.50 0.50 1 10.81 0.50 0.50 1 10.81 0.50 0.50 1 10.81 0.50 0.50 1 10.81 0.50 0.50 1 10.81 0.50 0.50 1 10.81 0.50 0.50 1 10.81 0.50 0.50 1 10.81 0.50 0.50 1 10.81 0.50 0.50 1 10.81 0.50 0.50 1 10.81 0.50 0.50 1 10.50 0.82 0.50 2 20.50 0.82 0.50 2 20.50 0.83 0.50 2 20.50 0.83 0.50 2 20.50 0.83 0.50 2 20.50 0.81 0.50 2 20.50 0.81 0.50 2 20.50 0.81 0.50 2 20.82 0.81 0.50 2 1 ⊗0.81 0.81 0.50 2 20.50 0.50 0.50 2 1 ⊗0.50 0.50 0.83 3 30.50 0.50 0.81 3 30.50 0.50 0.85 3 3

TablE IV. comparison of results for Testing Part.

FIs output class

normal corroded defective True FIs

0.8367 0.5 0.5 1 10.5 0.8388 0.5 2 20.5 0.8244 0.5 2 20.8196 0.5 0.8312 3 30.8196 0.5 0.837 3 30.5 0.8388 0.5 2 20.8196 0.5 0.8459 1 3 ⊗0.8196 0.5 0.8272 3 30.5 0.8071 0.5 2 20.8478 0.5 0.5 1 1

ing the inspection run. The use of fuzzy inference system not only reduces the real-time calculations to few com-parisons and a look-up table, it also embeds in the whole structure the perceptive intelligence of multiple human operators. This may enhance the accuracy of the defect classification methodology in existing ndE methods. The main strength of the proposed technique lies in the fact that the decision surfaces are based upon heuristic rules which are based upon the experiences of several ndE ex-perts with a diversity of backgrounds and experiences. by mapping the various expertise from several experts on one platform, a superior level of intelligence and knowledge of the underlying problem was achieved which may not have been attained otherwise by an individual expert.

acknowledgments

acknowledgments are due to several individuals in companies like rasGas co. ltd., qatar; and GE-PII co. ltd., UK; PEmco Inspection co., doha, qatar; offshore operations Engineering – qatar Petroleum; and Inspec-tion corrosion Engineering services, doha, qatar, for their expert opinions on the samples.

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Uvais Qidwai (m’96) received his Ph.d. from the University of massachusetts-dartmouth in 2001 from the Electrical and computer Engineer-ing department. He worked at the Electrical En-gineering and computer science department at Tulane University in new orleans, la, as assis-tant Professor, and in-charge of the robotics lab from June 2001 to June 2005. He joined the com-puter science and Engineering department at qa-tar University in fall 2005 as assistant Professor.


He is the head of the departmental research committee and a member of the research committees at qatar University level. His present interests in research include robotics, image enhancement & understanding for machine vision applications, fuzzy computations, signal processing and interfacing, expert system for testing pipelines, and intelligent algorithms for medical informatics. While in qatar, he is actively collaborating with local oil and gas industry in research projects related to intelligent sys-tem design for industrial problems such as nondestructive testing and evaluation, robotic inspection systems, and enhancement in the existing instrumentation and control systems. dr. qidwai has participated in several government and industry funded projects in United states, saudi arabia, qatar, the UaE, and Pakistan. He has been the keynote speaker at several regional IEEE conferences and has worked in organizing com-mittees of many International conferences. dr. qidwai is a regular re-viewer for many IEEE journals and related conferences and has pub-lished more than 55 papers in reputable journals and conferences.


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