In recent decades, oil analysis laboratories have used

Transcription

1 Analyze dirt with precision Automated scanning electron microscopy of particles in lubricants can help you zero in on root causes By William R. Herguth and Guy Nadeau 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 timeconsuming 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 Catching the dirt equipped with automated feature analysis (AFA) software to characterize complex matrices of particles extracted onto a membrane filter from oil samples. Methods 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 dashboard Figure 1. Sample prepared on 0.8-micron polycarbonate filter for automated feature analysis (AFA). Figure 2. Scanning electron microscope automated feature analysis (SEM/AFA) control screen showing the progress of an analysis on a circular filter. 1

2 Spanning the dirt The four measurements Figure 3. Rotating chord algorithm (RCA) analysis uses 16 chord measurements to determine the size and shape of a particle. 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). 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). 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). This algorithm yields a wealth of information about the shape and size of each particle (Table 1 and Figure 4). The selective search function can focus on the smallest Figure 4. (clockwise from top left) Dmax is the length of the longest chord through the feature centroid; Dmin is the length of the shortest chord through the feature centroid; Dperp is the length of the chord perpendicular to Dmax; the perimeter is the distance around the particle. Table 1. Particle dimensions defined Variable Unit of measure Description Dave 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 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. particles of significance while ignoring smaller ones. The search and measure portion of the automated process is fast, analyzing as many as 33,000 particles per hour, more than nine per second. The process can be limited to measuring particle morphology and generalized criteria can differentiate the elemental 2

3 Table 2. Rules for the case: motor bearing Classification Rule % of total Stainless steel Fe > 30% and Cr > 5% composition of the particles simply by segregating the video signal intensity levels. This method answers general questions about heavier or denser compounds versus lighter ones, but it doesn t provide a definitive elemental identification. Combining X-ray spectroscopy with electron imaging yields a huge gain in the quality of the information. The X- ray signal generated by the target particle specifically identifies the particle s elemental composition, but it takes more time to acquire the data from each particle. Whereas measuring a typical 10-micron round particle can be completed in less than 10 milliseconds, analyzing the same particle using X-ray data collection averages around four seconds. Without X-ray analysis, collecting particle data on a sample having 1,000,000 particles per 100 ml takes approximately 10 minutes. Analyzing the same sample using X-ray data would require more than 11 hours. Because the X-ray data s absolute identification of chemical composition is such an important addition to the information quality, a compromise is generally arrived at to correlate a percentage of the sample data to the total. Analyzing 10% of the specimen in a random manner can be completed in about 100 minutes and the data can then be extrapolated to 100%. An additional benefit of slowing down the analysis process is that it can then record an image of every identified particle. 1.7 Iron Fe > 30% 33.9 Tin Sn > 30% 49.0 Silicates Si > 5% 1.4 Brass Cu+Zn+Sn > 35% 6.6 Sodium Na > 10% 2.6 Miscellaneous Remaining particles 4.8. The intensity of the signal is a function of the item s atomic number. Analyze the data The analyst is now ready to review and sort the data into classifications that describe the specimen s dominant characteristics. These classifications can be based on morphology, chemical composition or both. Four interactive software programs view the data, establish classification rules, apply the rules to the data to organize it into an interpretable structure, and view specific particles for more detailed imaging and analysis. This is the manual portion of the process, with the analyst applying specific knowledge of the specimen and customer requirements to arrive at the most appropriately descriptive and useful results. The better laboratories maintain an extensive knowledge base and the expertise for analyzing a wide range of lubrication products and their applications, and troubleshooting problems arising from lubrication issues. The lab should apply these skills to the analytical process. The rules and structure the analysts apply should be archived so they can be revisited later if you desire revisions to the methodology. Rules for particle characterization The rules for case study 1 are shown in Table 2. Scanning the filter determined that the majority of the particles had the elements described in the rule. Tin and iron were most dominant, but other particles and alloys were present. So, this case needed a rule that requested the number and size of particles and the elemental proportions. Build the report The final step in the process is to take the finished analysis and publish an understandable and useful report. The data is best presented in the form of graphs, tables, electron images and X-ray spectra that represent useful tools for making decisions. Look for reports that can be standardized for ongoing trend analysis and for comparing data from other similar equipment. Portions of the data also should be presented in spreadsheet format so you can insert it in your ongoing data analysis systems. Table 3 shows the sort of analysis performance you should seek. Case study 1: Motor bearing Location: Nuclear power plant Description of sample: Oil from motor inboard journal bearing Table 3: Specs to expect Particle detection efficiency Greater than 99% Particle sizing precision Particle sizing accuracy 0.25 or better 0.50 or better Occurrence of false positives Sizing rate Characterization rate Less than 1 per mm2 about 33,000 particles per hour about 1,800 particles per hour 3

4 Table 4. Motor bearing debris Class Total < >100 SS Fe 1, Sn 2, Si Brass Na Misc Total 5, , ,608 1, Table 5. Gearbox debris composition by size classification Class Total <4 4 - <6 6 - < < < < <100 >100 Fe, Cr Iron-rich 1, Al Si, Mg Si-rich Silicates Misc. Na Misc All particles 2, Background: This motor is a critical asset that is sampled and analyzed monthly for metals, particle count and physical properties. The particle count for this motor bearing oil had been consistently higher than other motors of the same make and model (ISO Code 4406 = 21/18/14 versus 16/14/10). Emission spectroscopy revealed no indication of silicon or wear metals. The sample was subjected to SEM/AFA analysis because the root cause investigation needed to determine what these particles were and their sizes. SEM Observing conditions: The specimen was observed under vacuum at 15 kev beam energy to perform AFA on a nonconductive substrate. Working distance was approximately 16 mm to facilitate X-ray spectroscopy. General observations: Approximately 9.5% of the surface area of the filter was analyzed with the automated process, which identified and classified 5,618 particles by elemental composition and size (Table 4). This extrapolates to 1,182,740 particles per 100 ml, which is approximately 6.67% lower than indicated by HL-1185 particle count. The difference is attributable to the way the two processes account for particles smaller than 4 micron. Most of the debris in the sample is tin and iron. Tin, the dominant material, ranges from nearly pure tin to various brass-like alloys (copper, zinc and lead). The irondominant particles are mainly a low-chromium alloy and oxides, however, about 5% of the iron particles contain more than 5% chromium. The particles were classified by chemical composition to sort out the most frequently appearing types. Summary of results: This data from the nuclear power plant shows the journal bearing was wearing, producing large Babbitt and iron wear particles. The data suggests the bearing was wiped from too thin an oil film. 4

5 Table 6. Gearbox debris composition by particle size Particle # Dave Dmax Dmin Dperp Aspect Area Perimeter Primary Element Secondary element % of primary % of secondary Fe Si Mg Ca Fe Si Mg Si Si Mg Fe Fe Si Fe Case study 2: Gear box Location: Coal-fired power station Description of sample: Gear oil from pulverizer gear box Sample preparation: 20 ml of thoroughly mixed oil was diluted with heptane and drawn through a 0.45-micron nitrocellulose filter, followed by a thorough rinsing with heptane. Analysis parameters: 100% of the exposed filter area was searched to find particles of 4 micron and larger using 15-keV beam energy, vacuum and a backscatter element detector. The analysis is done in four steps. Summary of findings: A total of 2,083 particles in eight elemental composition categories were identified on the filter (Table 5). Mining the data Table 6 shows only the key findings for a typical analysis. With 285 particles counted in 41 different categories of particle characteristics, there is far too much data to present it all here,. However, this table illustrates that the analyst can sort by variables of interest. In this case, the data is sorted on Dmax (descending). It s clear the particles are: a. Slightly oblong or rectangular (average aspect value around 2). b. The particles are primarily iron and silicates. c. The average size is 85 micron. Summary of results: This analysis showed that gear wear was caused by three-body abrasion from contaminating silicates. The SEM/AFA analysis for size and elemental composition is superior to conventional particle analysis methods. The method described can save you dollars and needless speculation. William R. Herguth is president and Guy Nadeau is senior SEM/EDS analyst at Herguth Laboratories, Inc., Vallejo, Calif. They can be contacted at (800) More resources at Interpreting oil analysis Get comfortable with oil analysis Lube simplification Rethinking lubrication management Solid lube coatings Bearings roll with solid lube coatings Nanotech-based solid lubricant Harnessing nanotechnology Lube standards Lube specs Root causes of fluid degradation Lubricant RCA For more, search using the keywords lubrication, oil and analysis. 5