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8/2/2019 Reliability Assessment of Automated Eddy Current System for Turbine Blades
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RELIABILITY ASSESSMENT OF AUTOMATED EDDY CURRENT
SYSTEM FOR TURBINE BLADES
Yung-How Wu, Chu-Chung Hsiao (Materials Research Laboratories, Industrial Technology
Research Institutes, Taiwan)(yhwu01@itri.org.tw)
Abstract
This paper outlines our effort to develop an automated eddy current method assisted by a
self-aligned manipulator scanning along the disk rim of Westinghouse turbine. The
light-weighted manipulator was mainly designed for inspecting blade on L-2 stage disk
where root cracks were most frequently found during ISI. A mock-up with changeable
blades was used to assess the performance of this technique by statistical method.
Experimental results showed that the POD of this technique agreed well with that
originally proposed. The technique was also proved feasible in field trials.
Introduction
Blades and disk rim of turbine were frequently subjected to fatigue cracking and resulted in
unscheduled shutdown and even more a total failure of turbines. Hence, reliable routine
inspection is crucial to operation safety and efficiency of turbines operation. Although
various NDE techniques have been applied in ISI for this region for years, continue
improvement of the inspection techniques is still desired due to the critical role of steam
turbines and the inspection difficulties in this region. Among which, MT and manual ET
have been the favorite methods.[1-8]
There is no statistical data to refer to how small the crack size can be detected by the
current inspection methods. In principle, it was not difficult to detect 0.5mm crack with
fluorescent MT or manual ET methods. In practical cases, since the gap between disks was
quite narrow, it only allowed the inspector to observe the MT results from a distance or to
stretch his arm to scan the hand-held EC probes. Hence, MT will easily miscall the tight
and tiny cracks or those close to the edge of the serration. Inconsistency between MT and
ET results was always found especially for cracks smaller than 1 mm. To minimize the
human error, an automated EC inspection method was then developed as a supplement to
the current ISI. Crack as small as 0.5mm exposed on end face was set as the target to detect
with this method. And, Westinghouse type blade as shown in Fig. 1 was the inspection
target of this research.
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Airfoil-lead EdgeAirfoil-trail Edge
Fig. 1 Westinghouse turbine blade.
Auto Eddy Current Inspection Technique
The main goal of EC inspection automation was to develop a scanner-assisted method to
inspect blade root cracking on L-2 stage of Westinghouse steam turbine. The scanner was
designed to run on the extruded part of disk rim of Westinghouse steam turbine L-2 stage
as its rail, and use L-1 stage disk rim as its supporting face of the scanner. Accordingly, thescanner can run along the disk in circumferential direction between adjacent disks (as
shown in Fig. 2). The scanner could be stopped and locked automatically at proper position
of each blade to perform following inspection by scanning probe along the tangential of
blade root corners where cracks could be present as shown in Fig 3. After the inspection on
each blade, the scanner would be moved to next blade and all the blades will be inspected
in such a process.
Fig. 2 Automated scanner for blade crack inspection.
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Probe : UNIWEST US-686Frequency : 2.0 MHz
Gain X/Y : 15/15 dB
Phase : 183 deg.
Fig. 3 Different scanning routes (left) and relevant signal responses (right).
The probe was specially designed with a set of leaf spring to produce consistent contact
between probe and inspection area of the blade. The coil diameter of probe was of 3mm
with working frequency of 1-2 MHz that proved sensitive enough to obtain the result not
being interfered by accompanying unnecessary signal especially from corners of steeple.
Practical scanning route was adjusted to have the best performance of the inspection signal
during initial setup. Fig. 3 shows an example of signal on impedance plane from various
scanning routes on a real 13mm root crack. Lift-off signal in horizontal direction and crack
signal were almost perpendicular. When the scanning route running further from the
common tangent the crack signal pattern was thin and long which could be easily
differentiated between lift-off signal and noise. When the route running closer to the
groove edge the crack signal was influenced by the edge effect to deform, closer the probe
to the edge more obvious the influence of the edge effect until the crack signal was finally
replaced by the edge and lift-off signal totally.
The Reliability Assessment
Performance demonstration is a crucial procedure for any NDE method to meet theinspection requirements. It was normally carried out on real components or mock-up and,
sufficient number of tests is required to produce a quantitative and objective assessment.
In principle, when a NDE method or personnel is to inspect a defect of critical dimension
not all the defects of the same dimension could be detected. Similarly, repeated inspections
on a specific defect would not detect the defect every time. Although automated inspection
could avoid most of the human errors, statistic assessment is thus required to assure the
performance of the NDE method.
While assessing any NDT system and method statistically, various controllable and
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uncontrollable factors need to be defined. In our inspection method, the controllable factors
include eddy current excitation frequency/ Gain/ Phase/ Filter, probe, scanner initial
setting/ positioning, probe scanning position/ orientation/ speed, defect size/ position/
orientation, whereas main uncontrollable factors include the stability of eddy current
system, probe contact/ wear, circumferential movement of scanner/ positioning/ speed,
blade installation/ surface condition/ defect type and human factors.
The POD has been found properly represented by POD(a) function.[9]
As shown in Fig. 4,
the most common used function was cumulative log normal distribution function or log
odds function and the parameters could be calculated by Maximum likelihood method.
Such a so-called a analysis method could allow fewer samples, says 30 samples is
reasonable. Of course more the sample number higher the confidence level and more the
assessment precision.
Fig. 4 POD curve.
Fig. 5 Example correlation between measured value and defect size a.a
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Basically, analysis method uses a measured value to correlate with real defect size
quantitatively as in Fig. 5. Such a relation requires that:
a a
(a)For any defect with length (or depth) a, the measured value is not unique andcould be modeled with a certain distribution function such as normal distribution.
a
(b)For any defect with length(or depth) a, the POD(a) could be represented by aprobability density function of as)(aga
=deca
a adagaPOD
)()( (1)
(c)The relation between and defect length (or depth) a was normally assumedcorrelated as
)(aga
++= )ln()ln( 10 aa (2)
where,
aln is the relative measured value of defect,
)ln(a is the relative value of defect,
is the random error and is a normal distribution,
)ln(10
a + is the regression of average of each distribution.
(d)Defect is considered present only if , and POD(a) may be written asdecaa >[ ]
[ ]
[
[ ])ln()ln(
)ln()ln(o
)ln()ln(
)(
10
10
aaProb
aabPr
aaProb
aaProbaPOD
dec
dec
dec
dec
>=
>++=
>=
>=
] (3)
Reliability Experiment
To meet the number of defects required for experiment, we used notches as artificial cracks
on blades for evaluation. Fig. 6 showed defect signal of notch and real crack were
recognizable. The only differences between them were phase and amplitude. The signal
characteristic as shown suggested that the notch can be used to simulate crack. To test the
mechanical performance of the scanning system, 9 pieces of cracked blades were used on a
mock up at each experiment as shown on Fig. 7. Scanner was installed on the mock up to
perform simulated scanning, each blade could be replaced easily according to the
requirement of random sampling. The cracks on the blades were simulated by EDM notch
with length between 0.3-2.5mm.
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Probe typeUNIWEST RPDS-12 differential
Parameter Settings Test sampleReal blade with EDM notch
Frequency 2.0 MHz
Bandwidth HF Scanningroot area of bladePreamplifier 6 dB
Gain Y/X 17dB / 17dB
Phase 119 deg
Dot Position Y / X 0 / 0
Filter LP80Hz / HP0.5Hz
Impedance response
Fig. 6 Signal response of 2mm0.3mm (lengthwidth) notch on sample 1.
Fig. 7 Mock-up with 9 blades interchangeable.
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Results
The flowchart of POD analysis was shown in Fig. 8. Relative dimensions (Di) of a certain
notch could be defined with reference to a specific sample with notch 2.5mm in length as
in Eqa (4). The signal amplitude(Ai) of a certain notch on impedance plane (Fig. 9) was
normalized by the maximum defect amplitude(A2.5) of the reference notch.
)5.2(5.2
mmA
AD ii = (4)
To read amplitude
of defect signal
To convert amplitude
to defect size
To use Range & Bartlett test
To verify the similarity of ln distribution
To use linear regression analysis
to find interce t ( 0 & slo e ( 1
To determine
2
21
2
2 )1(
=
sn
To calculate s
1
)(1
2
2
= =
n
xxs
n
i i
no
Retest
Discard
yes
To determine a valuedec
To determine POD curve by
=
aaaPOD dec
lnln1)( 10
Fig. 8 Flow chart of POD determination.
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Defect
Noise
Gain:10dB
Ai
Gain:7dB
A2.5
Fig. 9 To determine defect size from reference signal.
Two charts of distribution of various defect detections were shown in Fig. 10, then totransfer the individual signal by natural log. The range and variance of defect length were
first shown in table 1. Theoretically, all the distributions should be normal which could be
verified by the Bartlett test by confirming that the variance equivalence of various test data
was satisfied where the significance level was assumed as 0.01. Then critical value of
Bartlett distribution (bk(, n1, n2, n3,, nk)) was then found as
)7,13,14,14,14,14,01.0(6b
8014.076
)6410.0(7)8096.0(13)4)(8195.0(14=
++
(5)
The mixed estimate of sample variance was
kN
sns
ii
k
iP
= =
2
12)1(
676
)037.0(6)026.0(12)088.0080.0131.0169.0(13
+++++=
094.0= (6)
b wasfound as
2
)/(11212
2
12
1 ])...()()[(21
P
kNn
k
nn
s
sssb
k
= (7)
Since b > bk, we may assume the variance of all different defect size were equal which
suggested that all distribution were normal.
Distribution of vs. was found as shown in Fig. 11. By regression, it was
found that intercept (
aln )ln(a
0 )= - 0.0299 and slope (1 )= 0.8784. The freedom () for 6 group of
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different defect size was 5. To assume confidence level = 95% and set = 0.025, it was
found that
832.122
21
=
and 037.0
832.12
)094.0)(16()1(2
21
22 =
=
=
Psn
(8)
Furthermore, to assume
69.0)5.0ln()ln( ==deca (9)
It was found that
75.08784.0
)0299.0(69.0)(ln
1
0 =
=
=
dec
a(10)
and,
042.08784.0
037.0
1
===
(11)
The POD curve could be drawn by finding POD(a) of every defect size as shown in Fig.
12.
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Table 1 Experimental data of signal response method.
Defect number 1 2 3 4 5 6Defect Length
(mm)0.3 0.5 1.0 1.5 2.0 2.5
Defect Length
ln a-1.20 -0.69 0.00 0.41 0.69 0.92
0.22 -1.52 0.22 -1.52 0.75 -0.29 0.78 -0.25 1.56 0.45 1.47 0.38
0.22 -1.52 0.25 -1.39 0.84 -0.17 0.81 -0.21 1.78 0.58 1.53 0.43
0.22 -1.52 0.28 -1.27 0.91 -0.10 0.84 -0.17 1.78 0.58 1.63 0.68
0.25 -1.39 0.31 -1.16 0.94 -0.06 0.97 -0.03 1.81 0.59 1.97 0.68
0.28 -1.27 0.34 -1.07 1.03 0.03 1.00 0.00 1.88 0.63 1.97 0.68
0.31 -1.16 0.38 -0.98 1.06 0.06 1.06 0.06 1.88 0.63 2.28 0.82
0.38 -0.98 0.41 -0.90 1.16 0.15 1.22 0.20 1.94 0.66 2.50 0.92
0.47 -0.76 0.44 -0.83 1.28 0.25 1.25 0.22 1.97 0.68
0.50 -0.69 0.47 -0.76 1.47 0.38 1.28 0.25 2.06 0.72
0.53 -0.63 0.50 -0.69 1.53 0.43 1.47 0.38 2.19 0.78
0.56 -0.58 0.53 -0.63 1.59 0.47 1.53 0.43 2.28 0.82
0.59 -0.52 0.59 -0.52 1.63 0.49 1.59 0.47 2.59 0.95
0.59 -0.52 0.63 -0.47 1.66 0.50 1.78 0.58 2.81 1.03
Measured
Length
(mm)
Measured
Length
ln a
0.59 -0.52 0.75 -0.29 1.75 0.56 1.97 0.68
Full Range 0.37 1.00 0.53 1.23 1.00 0.27 1.19 0.93 1.25 0.58 1.03 0.54
Variance (s2
) .024 .169 .024 .131 .117 .080 .138 .088 .122 .026 .152 .037
Variance ( )2
ps
094.0
70
037.6026.12088.13080.13131.13169.13
)1(1
2
2
=
+++++=
==
kN
sns
k
i
ii
p
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0
1
2
3
4
5
6
0~0.15
0.3~
0.45
0.6~0.75
0.9~
1.05
1.2~
1.35
1.5~
1.65
1.8~
1.95
2.1~
2.25
2.4~2.55
2.7~
2.85
Measured Length (mm)
Frequency Actual Length0.5mm
0
1
2
3
4
5
0~0.15
0.3~
0.45
0.6~0.75
0.9~
1.05
1.2~
1.35
1.5~
1.65
1.8~
1.95
2.1~
2.25
2.4~2.55
2.7~
2.85
Measured Length (mm)
Frequency
Actual Length2.0mm
Fig. 10 Measured value distribution of various defects.
-2
-1.5
-1
-0.5
0
0.51
1.5
-1.5 -1 -0.5 0 0.5 1 1.5
aln
aln
y=0.8784x-0.0299
R2=0.7792
Fig. 11 Correlation between measured value and defect size a.
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Defect Length (mm)
Fig. 12 POD curve of automated EC inspection.
Conclusion
The signal response analysis was used to establish the practical reliability analysis method
for this automated eddy current inspection system. The probability of detection curve was
shown as a unit step function which indicated the quality of the system was good enough.
When defect length was greater than 0.5mm the POD was almost 100%, in another word
the defect greater than 0.5mm in length could be detected reliably, which was compliant
with the originally proposed target. However, the result was obtained from a mock up in
a well-controlled experimental environment then further test should be carried out in the
field. Through the result this method could be verified whether or not it could be an
alternative of the current inspection method.
Acknowledgement
The authors gratefully acknowledge all their colleagues in Taipower Company in
supporting this program.
References
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