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January - February 2014 33
Benefits of SEM/EDX
For years, wear debris and oil testing have been performed
using methods such as ferrography and particle counters in
conjunction with ICP or rotating disk electrode (RDE).
Information gained from these technologies has been
helpful in diagnosing engine or gearbox wear, but users
still struggle with knowing how to interpret data from
the laboratory. SEM/EDX offers an alternative to tradi-
tional wear debris and particle analysis, and can now
be used by a technician with minimal training or a
limited scientific background. It can take the place of
two or three traditional oil testing methods by allowing
the user to report both particle and chemical compo-
sition data. The data can then be segmented based on
user-defined classes, and clear classifications of wear
debris particles can be studied for immediate feedback
on gearbox or engine conditions. SEM/EDX can also
complement your current oil testing program by
offering more intuitive data to diagnose a suspected
issue when more traditional testing raises warning
signs but is not conclusive.
Regardless of your testing program, it is important to
keep in mind that establishing historical baselines and
running consistent testing protocols will provide histor-
ical wear profiles that can give timely indicators of the
changes in the status and rate of wear.
About the Author
Susan Benes is the product marketing manager at FEI. She can be
reached via email at susan.benes@fei.com.
rULE CLASS
>=2 >=4 >=6 >=7 >=10 >=14 >=50
100Cr6 233 466 224 120 72 22 0
16MnCr5 733 1,466 766 376 238 86 12
Al-Zn 2 4 2 2 2 0 0
Cr 11 22 16 10 6 0 0
Cr Coating 7 14 6 4 4 4 2
Cr-Ni-Mo 3 6 2 2 0 0 0
Fe-Cu 27 54 40 30 26 4 0
High Alloy Steel 260 520 204 82 34 12 2
High Ca 9 18 12 4 4 0 0
Low Alloy Steel 386 772 324 148 82 36 8
Low Fe 493 986 408 190 88 20 4
Lubricants 204 408 114 54 16 2 0
Ni 11 22 2 0 0 0 0
Steel 359 718 388 200 128 40 12
Zn Coating 20 40 12 6 6 0 0
Zn Misc. 908 1,816 526 220 106 24 6
Zn-Cr Coating 10 20 4 0 0 0 0
Zn-P Coating 74 148 36 6 2 0 0
Table 6. Results for sample 2 showing classification by size
and rule class
