34
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July - August 2021
|
www
.
machinerylubrication.com
Scanning Electron Microscopy
for Wear Debris Analysis
Oil analysis deploys
various types of instru-
ments, technologies,
standards and tech-
niques, all of which help provide a
perspective on the health of the
lubricant and the machine. e oil
analysis data either targets specific
properties of the lubricant, quanti-
fies the level of various contaminants
or directly measures any evidence
of mechanical wear through wear
debris analysis. Many concerns arise
anytime solid debris (particulates) of
any kind accumulate in the lubricant
within a machine.
On the one hand, if the solid
debris is contamination-related,
such as dirt or product debris
entering the oil, this is a proactive
(pre-failure) indicator that machine
wear could occur as the debris
becomes abrasive and disrupts the
film thickness. While some amount
of contaminant is expected, keeping
this controlled is critical. On the
other hand, if it's wear debris
related, composed of metals such as
iron or copper, this is a predictive
(progressive failure) indicator that
machine wear is already occur-
ring, and action should be strongly
considered if a functional failure is
to be avoided.
Monitoring for debris can be
done in many ways. For example,
daily visual inspection of the
machine sight glasses or IIoT sensors
can be the first indicator of abnormal
conditions. Routine oil analysis can
include tests to quantitatively look
for particles in different ways, such as
total particle size distribution (ISO
particle counts), specific elements
Factor: A7M
Learn More:
noria.com/ascend/
Factor:
A7M – Troubleshooting
and Root Cause Analysis
Level:
Management
and Training(M)
Stage:
Condition Monitoring,
Lubricant Analysis and
Troubleshooting
About:
Taking the right
approach to trouble-
shooting and root cause
analysis is critical for
diagnosing problems
quickly and finding
sustainable solutions.
CONDITION MONITORING, LUBRICANT ANALYSIS AND TROUBLESHOOTING
More about this
ASCEND
™
Factor
Placing the Prepared Debris Specimen into the SEM
Bennett Fitch, Noria Corporation
|
Laura Waldman, University of Tulsa
