Analyzing lubricant particles helps zero in on root causes

Automated scanning electron microscopy of particles in lubricants can help you zero in on root causes.

By William R. Herguth and Guy Nadeau

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In recent decades, oil analysis laboratories have used automatic particle counters to determine the number and size of particles in many oil-lubricated systems. Once one knows how many and how large, there remains the question of their source. The logical next step is to investigate the particle’s analysis or extract them onto a filter membrane and view them under magnification.

In theory, characterizing particles by shape and elemental composition is useful for determining their source and developing a cost-effective action plan to rectify the mechanical problem that released the particles into the lubricant. However, this approach has two difficulties. First, chemical analysis often is unreliable for identifying larger particles and, second, it’s not possible to make a positive identification of the particle’s elemental composition with optical magnification.

In some cases, scanning electron microscope (SEM) analysis can better characterize the particles, but it’s a time-consuming analysis and it relies on the operator’s judgment about which areas of the sample to investigate.

Technology for sizing and identifying particles eliminates these weaknesses. Many of the better oil analysis laboratories now use an automated electron beam particle analyzer equipped with automated feature analysis (AFA) software to characterize complex matrices of particles extracted onto a membrane filter from oil samples.


The scanning electron microscope and its automated feature analysis function combine the high magnification capabilities of an electron microscope and the supporting simultaneous energy-dispersive X-ray measurement system with automated searching and recording of data for future reference. The analysis is done in four steps:

  1. Sample preparation
  2. Automated data acquisition
  3. Data analysis and configuration
  4. Processing the results into a useful report

Getting the sample

Preparation requires that a representative oil sample be thoroughly homogenized and diluted with a suitable solvent. The analyst will need either to have prior knowledge about the sample or to screen it to determine the best volume to use (usually 1 ml. to 3 ml.) to optimize the capability of the automated functions. Then, the diluted sample is drawn through a low-contrast filter of appropriate pore size, such as an 0.8-micron polycarbonate filter (Figure 1).

Figure 1

Figure 1. Sample prepared on 0.8-micron polycarbonate filter for automated feature analysis (AFA).

Extra care is taken in handling, indexing and storing the filter so that, if desired, the specimen can be reanalyzed at a later date and the same core automated data can be used to search out and further analyze particles.

Getting the data

Automated data acquisition exploits the fact that high-energy electrons, called backscatter electrons, scattering off heavier elements tend to have a higher backscatter coefficients than when scattering off lighter elements. Electron microscopes produce an image of an item by detecting contrast in signal intensity between adjacent pixels in a raster image. Unlike a digital camera, SEMs operate sequentially, scanning rows of pixels and stopping at each pixel to acquire its individual signal (Figure 2).

 Figure 2

Figure 2. Scanning electron microscope automated feature analysis (SEM/AFA) control screen showing the progress of an analysis on a circular filter.

The intensity of the signal is a function of the item’s atomic number. The analyst adjusts the sensitivity to differentiate the lightest particle to be identified from the background filter membrane.

The system automatically and rapidly scans the selected area of the filter and pauses at each particle it detects. During the pause, it shifts to its highest resolution to perform a “rotating chord algorithm” based on 16 straight-line scans across the particle, with scans separated by approximately 11° (Figure 3).

Figure 3

Figure 3. Rotating chord algorithm (RCA) analysis uses 16 chord measurements to determine the size and shape of a particle.

This algorithm yields a wealth of information about the shape and size of each particle (Table 1 and Figure 4).

Table 1. Particle dimensions defined
Variable   Unit of measure Description




micron The average length of the 16 chords through the particle’s centroid.
Dmax micron The length of the longest chord through the feature’s centroid.
Dmin micron The length of the shortest chord through the feature’s centroid.
Dperp micron The length of the chord perpendicular to the longest chord.
Aspect ratio The ratio of Dmax to Dperp
Area square microns The area of the feature
Perimeter micron The perimeter of the feature as measured from one chord end to the next.
Orientation degrees The orientation of the longest chord. Zero is at noon and the angle increases clockwise.

Figure 4

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