Fault diagnosis through Ferrography (Wear Particle Analysis)

The purpose of Plant Predictive Maintenance (PDM) programme is to improve Reliability of machineries through early detection and diagnosis of equipment problems, and degradation prior to equipment failure. Ferrography (Wear Particle Analysis) is one of the PDM techniques which allows detection, identification and evaluation of the degradation at the very incipient stage so that degradation is timely attended and mitigatory actions initiated. Ferrography is a Wear Particle Analysis technique based upon systematic collection and analysis of sample of lubricating oil from rotating and reciprocating machines. Ferrography analysis is conducted in 2 phases: Stage I – Quantitative, and Stage II – Qualitative. After Stage II analysis, recommendation is issued based on wear rating (Normal, Marginal, or Critical) so that operator can take timely action. Presently, 21 Nuclear Power Plants are operational in India and Forced Shutdown is a very costly affair. Lube oil of around 60 equipment from Indian Nuclear Power Plants is examined quarterly for Ferrography analysis, and failure of several equipment is avoided due to timely action. This paper will elaborate on the basic principles of Ferrography, and how systematic implementation of Ferrography has helped in avoiding forced failure of equipment, and hence prevent Forced Shutdown.

Vitamin E-infused highly cross-linked polyethylene did not reduce the number of in vivo wear particles in total knee arthroplasty

Aims Vitamin E-infused highly cross-linked polyethylene (E1) has recently been introduced in total knee arthroplasty (TKA). An in vitro wear simulator study showed that E1 reduced polyethylene wear. However there is no published information regarding in vivo wear. Previous reports suggest that newly introduced materials which reduce in vitro polyethylene wear do not necessarily reduce in vivo polyethylene wear. To assist in the evaluation of the newly introduced material before widespread use, we established an in vivo polyethylene wear particle analysis for TKA. The aim of this study was to compare in vivo polyethylene wear particle generation between E1 and conventional polyethylene (ArCom) in TKA. Methods A total of 34 knees undergoing TKA (17 each with ArCom or E1) were investigated. Except for the polyethylene insert material, the prostheses used for both groups were identical. Synovial fluid was obtained at a mean of 3.4 years (SD 1.3) postoperatively. The in vivo polyethylene wear particles were isolated from the synovial fluid using a previously validated method and examined by scanning electron microscopy. Results The total number of polyethylene wear particles obtained from the knees with E1 (mean 6.9, SD 4.0 × 107 counts/knee) was greater than that obtained from those with ArCom (mean 2.2, SD 2.6 × 107 counts/knee) (p = 0.001). The particle size (equivalent circle of diameter) from the knees with E1 was smaller (mean 0.5 μm, SD 0.1) than that of knees with ArCom (mean 1.5, SD 0.3 μm) (p = 0.001). The aspect ratio of particles from the knees with E1 (mean 1.3, SD 0.1) was smaller than that with ArCom (mean 1.4, SD 0.1) (p < 0.001 ). Conclusion This is the first report of in vivo wear particle analysis of E1. E1 polyethylene did not reduce the number of in vivo polyethylene wear particles compared with ArCom in early clinical stage. Further careful follow-up of newly introduced E1 for TKA should be carried out. Cite this article: Bone Joint J 2020;102-B(11):1527–1534.

Mechanical tests, wear simulation and wear particle analysis of carbon-based nanomultilayer coatings on Ti6Al4V alloys as hip prostheses

Carbon-based nanomultilayer coatings were deposited on medical-grade Ti6Al4V alloy using a magnetron sputtering technique under a graded bias voltage.

Quantitative wear particle analysis for osteoarthritis assessment

Osteoarthritis is a degenerative joint disease that affects millions of people worldwide. The aims of this study were (1) to quantitatively characterise the boundary and surface features of wear particles present in the synovial fluid of patients, (2) to select key numerical parameters that describe distinctive particle features and enable osteoarthritis assessment and (3) to develop a model to assess osteoarthritis conditions using comprehensive wear debris information. Discriminant analysis was used to statistically group particles based on differences in their numerical parameters. The analysis methods agreed with the clinical osteoarthritis grades in 63%, 50% and 61% of particles for no osteoarthritis, mild osteoarthritis and severe osteoarthritis, respectively. This study has revealed particle features specific to different osteoarthritis grades and provided further understanding of the cartilage degradation process through wear particle analysis – the technique that has the potential to be developed as an objective and minimally invasive method for osteoarthritis diagnosis.

An Experimental Investigation of Industrial Gearbox Condition Using Wear Particle Analysis Technique

In different lubricated machines as engines and gearboxes, the generated wear particles analysis is considered as an effective tool for condition monitoring of these machines. Wear particle analysis as a nondestructive evaluation technique is an effective method to determine the lubricating oil conditions within different lubricated machines, thus monitoring wear modes and imminent failures in these machines. Machine condition monitoring is a cost-effective and reliable system to predict mechanical behavior and efficiency of power plant systems. Qualitative, quantitative and morphological data could be obtained from the wear particle analysis through the periodically taken samples of the lubricant. Different methods are used to detect and analyze wear debris in the lubricant oil, such as ferrograhy, spectrometry, filtergram, particle counters and recently Laser oil analyzer and time-dependent limits monitor factors. The objective of the present work is to apply wear particle analysis technique for condition monitoring of an industrial gearbox transmission over one year period. This transmission belongs to one of the largest carpet manufacturing plant in the world. The chosen gearbox for condition monitoring was a new gearbox installed to the rug textile machine. The gearbox components are elasto-hydrodynamically lubricated with mineral-based oil. The function of the gearbox is to drive the motion (forward and backward) of the knife to cut the fibbers of the carpet during the operation. Periodic oil samples were taken and analyzed through spectrometric technique while selective samples were chosen to be analyzed through ferrography technique. Spectrometric and ferrographic analysis were used where quantitative and qualitative changes in the concentration and size distribution of different particles were analyzed and compared to baseline and limit values. In addition to the sampling process, the gearbox performance was also monitored through measuring the oil temperature that was recorded just after the oil sample intake. The oil temperature is an indication for the gearbox loading which in its turn indicates any failure if it occurs. Results were analyzed, discussed and correlated to the gearbox performance. Also, recommendations were given for better performance based on the investigation and justification of the relevant results.

Condition Monitoring of a Gear-Box Using Wear Particle Analysis Technique

Wear particle analysis as a nondestructive evaluation technique is an effective method to determine the lubricating oil conditions within different lubricated machines, thus monitoring wear modes and imminent failures in these machines, such as gear-boxes and engines. Ferrographic analysis of wear particles in lubricating oil could give complete information about ferrous and non-ferrous solid debris present in the oil sample. Spectrometric oil analysis could give a direct measure of elemental metal content in the oil such as Iron, Aluminum, Lead and Cupper. These techniques provide cheap, fast and easy methods to use predictive maintenance methods which can replace other conventional methods. The objective of the present study is to apply wear particle analysis technique for condition monitoring of an industrial gear-box transmission over two year’s period of time. This gear-box belongs to one of the machines of the Oriental Weavers Company (OWC), one of the largest carpet manufacturers in the world. Spectrometric and ferrographic analysis were used where quantitative and qualitative changes in the concentration and size distribution of different particles were analyzed and compared to baseline values.