Wear Particle Analysis Research Articles

Wear particle analysis—utilization of quantitative computer image analysis: A review

This paper provides a general overview of developments and progress in quantitative computer image analysis as applied to wear particle identification/classification technology, over the last two decades. Since many technical disciplines are involved in this ‘infant-stage’ technical area, an attempt is made to put into perspective mechanical failure prediction/diagnosis and prevention through quantitative wear particle morphological analysis. The problems experienced with applying conventional wear particle analysis methods in machinery condition monitoring, notably the employment of wear debris morphological diagnostic systems, revealed that it is not prudent to rely solely on human interpretation in the analysis of ‘filtergram’ slides. This has highlighted the need for improving the provision of ‘intelligent’ objective methods for performing this type of analysis. In this paper, some of the developments reported in the literature relating to progress made with wear particle image analysis are reported and examined as a basis for establishing improved methods of diagnostic analysis.

A study of information technology used in oil monitoring

Oil monitoring is referred to as wear particle debris analysis and physical analysis of lubricants' properties. Information technology, such as database technology, image processing, expert system, data fusion and multi-agent system theories, etc. makes oil-monitoring procedures more intelligent. This paper introduces the development of some information systems such as management database, computer-aided wear particle analysis software used in oil monitoring. The basic principles and models of information technologies used in this field are presented. The development of IT as an integral element of an oil analysis-based condition monitoring system has been carried out in the authors' laboratory since 1986. Initially, the principal application of IT was to improve the data management and processing procedures. The second use of IT was in the development of a wear particle image processing system. The third aspect of utilizing IT is artificial intelligence. Finally, the application of IT-based network techniques is implemented. The progress in the application of IT in the authors' laboratory shows that the combination of information technology and oil monitoring can increase the speed of oil analysis, manage the information conveniently and obtain analysis conclusion more precisely in relation to practical application.

Wear characteristics of injection-moulded unfilled and glass-filled nylon 6 spur gears

The tribological behaviour of polymer and polymer composite gears is reported in this paper. Gear tests were conducted using a power absorption-type gear test rig. Injection-moulded nylon 6 and glass-filled nylon 6 gears were tested at different speeds and loads. Gear rotational speed influences the tooth wear of unfilled and glass-filled nylon 6 injection-moulded gears due to the change in contact period of teeth in mesh. When the polymer-based gear drives a steel gear, the difference in the elastic moduli of the mating materials causes rotational lag during gear running. When the polymer gears transmit a higher torque, a higher amount of rotational lag causes an excessive wear in the dedendum region of the polymer gear. In the cases of both unfilled and glass-filled nylon 6 gears, at higher-torque test conditions, non-uniform wear on the gear tooth surface was observed. Glass-filled nylon 6 gears showed a better wear resistance than the unfilled nylon 6 gears due to the improved compressive strength, creep resistance, and elastic modulus. A detailed wear particle analysis was carried out to understand the gear wear mechanism. The contact stress and gear material hardness affect the gear wear mechanism.

Characteristics and morphology of wear particles from laboratory testing of disk brake materials

The geometrical characteristics and morphology of wear particles generated from brake materials are important for environmental and tribological reasons. Low- and high-speed, pin-on-disk friction and wear testing of a commercial truck brake pad material against cast iron was conducted in which wear debris was collected. The sliding speed was held constant either at 0.275 or at 5 m/s, and the nominal contact pressure was varied between 0.125 and 1.25 MPa in room temperature air. In low-speed experiments, some tests were conducted with the pin in continuous sliding contact and others in which the pin specimen was raised and lowered periodically. Laser scattering examination of wear debris revealed two distinct peaks in the plot of frequency versus the mean particle size. The first peak occurs around 350 nm and does not vary with respect to the pressure and the sliding speed. The location of the second peak varies between 2 and 15 μm, depending on the pressure and the sliding speed. Energy dispersive X-ray (EDX) analysis of wear particles revealed particles having a high concentration of carbon, silicon, aluminum, iron, oxygen, molybdenum, and sulfur. It was also found that the continuity or discontinuity of sliding contact affects the size distribution of wear particles. In general, when the motion was discontinuous, as is the case in a repeated braking action, smaller wear particles are generated.

Biological response to wear debris generated in carbon based composites as potential bearing surfaces for artificial hip joints.

UHMWPE wear particles have been implicated in osteolysis, implant loosening, and long-term failure of total hip arthroplasties in vivo. This study examined four carbon-based composite materials as alternatives for UHMWPE in joint bearings. These materials were HMU-CVD, SMS-CVD, P25-CVD, and CFR-PEEK. New bearing materials should satisfy certain criteria: they should have good wear properties that at least match UHMWPE, and produce wear particles with low levels of biological activity. Of the four materials tested in multidirectional pin-on-plate tribological tests, SMS-CVD, P25-CVD, and CFR-PEEK showed lower volumetric wear factors than UHMWPE. P25-CVD had the lowest wear factor of 0.54 +/- 0.34 x 10(-7) mm(3)/Nm. Analysis of P25-CVD wear particles by transmission electron microscopy showed that the debris was very small, with the vast majority of particles being under 100 nm in size, which was similar in size to metal wear particles. The P25-CVD particles were isolated and cultured with L929 fibroblasts and U937 monocytic cells to assess their effect on cell viability. P25-CVD particles were significantly less cytotoxic (p < 0.01, ANOVA) to both cell types than CoCr metal wear particles. This work suggests that carbon-carbon composite materials may have potential for use in total hip replacement bearings. Of the materials tested P25-CVD had the lowest wear factor, and produced very small wear debris that had minimal cytotoxic effect on L929 and U937 cells in vitro. Therefore carbon-carbon composites, such as P25-CVD, may be important in the development of next-generation implants with lower wear rates and reduced cytotoxic potential.

A Web-Based Remote Intelligent Expert System for Ferrography Diagnosis

Abstract. A ferrography expert system for wear particle analysi s can be an effective means of condition monitoring and fault diagnosis. The knowledge of the ferrography diagnosis expert system cannot be represented by certain rules because it is f uzzy and random, therefore an intelligent expert system is developed. A neural network with self -deleting nodes is employed in the intelligent expert system, and this overcomes to some degree the disadvantages of a traditional expert system and makes it possible to automatically acquire knowl edge by learning from samples and to realize entirely automatic processing, from wear particle rec ognition to wear diagnosis. Introduction Wear is one of the main factors causing breakdown and faults in mac hines. Therefore the ferrography technique for wear particle analysis can be an effective way for condition monitoring and fault diagnosis [1,2]. It is difficult for the ferrography tec hnique to be widely used because it mainly depends on operator’s experience. Therefore it is very useful to develop a ferrography diagnosis expert system. Some expert systems have been develope d in this field and led to some effective results. FAST, FAST PLUS [3,4], CASPA [5] and CAVE [6,7] are examples which employ traditional expert systems. It is difficult to define the knowledge of the ferrography diagnosis expert system by certain rules, because it is fuzz y and random. Some intelligent expert systems were studied [8,9], but they were still not fully automatic integrated systems. A web-based remote intelligent expert system for ferrogr aphy diagnosis (RIESFD), based on a neural network, fuzzy theory and Internet for wear particle ident ification. Wear diagnosis is presented in this paper. RIESFD overcomes to some degree the disadvanta ges of a traditional expert system, and it can automatically acquire knowledge by learning fr om samples and realize entirely automatic processing from wear particle identification to we ar diagnosis. It is very important for this expert system based on a neural network to be provided with enough samples for its learning, therefore it should be realized to collect all kinds of typical wear particle samples and share the knowledge between expert systems in different locations. The Internet is a good tool for the exchange and contact between different systems. Therefore the appl ication of a remote diagnosis course on Internet is the direction of the ferrography technology development. Basic Principle of RIESFD RIESFD is developed for full auto-identification of wear partic les and wear diagnosis. The system includes three steps: (1) Identify all wear particles on a ferrograph slice one by one, and acquire the information on the classification of wear particles at one time point. (2) Read all history information on wear particles from a database , and form the distribution of different kinds of wear particles with time. (3) Diagnose the wear fault on the basis of the above, and give the result. With the above idea, the system progress is shown as Fig. 1. The key techniques are wear particle identification and wear fault diagnosis, therefore a ne ural network is employed for this purpose. The wear particles are randomly produced during equipment running, and different wear particles can be produced at different states and different friction pai rs, therefore it is very important

The nature and dissemination of UHMWPE wear debris retrieved from periprosthetic tissue of THR.

The role of wear debris in provoking joint replacement failure through bone resorption is now supported by much research. This study presents the analysis of 104 tissue samples using laser diffraction wear particle analysis in conjunction with standard histologic methods. The number and volume distributions were correlated to a range of joint and patient parameters. The median particle diameter by number was 0.69 microm. No particles smaller than 0.113 microm were resolved. No variation in terms of particle distribution was found among joint types. The ability of particles to migrate away from their point of origin was found to be inversely proportional to their size. The numbers of particles per gram of tissue found in various regions around the prosthesis varied little. Further, the numbers of particles in tissue samples shown to have a chronic foreign-body reaction was > 1 x 10(9) particles/gram.

Computer vision techniques for wear debris analysis (CVTWDA)

Computer vision is a process to locate and recognise objects in digital images. The terms or debris come from the field of tribology. Tribology is the study of wear, friction and lubrication. Wear particles are produced in all machines containing moving parts in contact. This paper addresses the analysis of microscopic wear particles generated by wear mechanisms by using computer vision and image processing techniques. The aim is to develop an automated system to classify and identify wear particles in terms of their morphological and textural features and by using the information obtained to predict wear failure modes in engines and other machinery. An interactive control system, CAVE, is devised to process and store the quantitative information of particle size, shape and edge details. The identification and analysis of wear particles are effectively demonstrated by the devised system with the ability to make human-like, even better and objective diagnosis. This obviates the need for specialists and reliance on human visual inspection techniques.

Standardized analysis of UHMWPE wear particles from failed total joint arthroplasties.

Periprosthetic tissue obtained at revision surgery from eight total hip replacement cases was hydrolyzed, and polyethylene debris particles were isolated from each case. Individual particles were analyzed by scanning electron microscopy (SEM) and computerized image analysis in accordance with ASTM F1877-98, a standard for quantitative description of wear debris. For comparison, periprosthetic tissues from eight total knee revision and four total shoulder revision cases were processed and analyzed with identical methods. A total of 2599 hip, 4345 knee, and 1200 shoulder particles were analyzed. The morphologies of the isolated polyethylene particles from the total hip specimens were distinctly different from the total knee and total shoulder particles. The mean equivalent circle diameter (ECD) for hip particles was 0.694 microm +/- 0.005; knee particles measured 1.190 microm +/-0.009; and shoulder particles 1.183 microm +/- 0.017. The ECD was significantly different between hip particles and those from the shoulder and knee. The mean aspect ratio (AR) for the hip particles was 1.626 +/- 0.015, compared to the knee particles at 1.935 +/- 0.015 and shoulder particles at 2.082 +/- 0.033. The AR was statistically different among all three groups. Other descriptors from the ASTM standard, elongation (E), form factor (FF), and roundness (R) were all significantly different among the three groups of joints. This study demonstrates the utility of ASTM F1877-98 in differentiating wear debris particles from different sources.

Microstructural characterization of wear particles formed during tribological stressing of TiC and Ti(C,N) coatings

An attempt was undertaken to obtain a better understanding of the tribological properties of two wear-resistant coatings on tool steel by structural and microchemical analysis of wear particles using a transmission electron microscope. Coatings were deposited by physical vapor deposition and plasma-assisted chemical vapor deposition techniques and tribological properties were derived from reciprocating sliding tests of the coatings against alumina balls. Three types of wear particles were identified by electron diffraction and energy dispersive X-ray spectroscopy: nanocrystalline rutile (TiO2), nanocrystalline graphite and microcrystalline graphite. Low coefficients of friction, of the order of 0.2, were attributed to the formation of solid lubricant films of sub-stoichiometric TiO2-x Magneli phases and/or graphite.

Histologic and quantitative wear particle analyses of tissue around cementless ceramic total knee prostheses

We histologically analyzed tissue biopsy specimens obtained from 4 cases of revised cementless ceramic knee prostheses. Particles were isolated from the specimens, and their size and composition were determined. Implants were removed for aseptic loosening at an average of 64 months. Histologically, polyethylene particles were seen in all cases. The concentration of the particles showed wide variation among cases. The concentration of the particles also showed a wide variation among different biopsy locations, suggesting that the particles were not distributed uniformly around the knee prostheses. Although we analyzed only 4 cases, our results show good correlation between radiographic evidence of geographic osteolysis and the concentration of wear particles. Multiple biopsy specimens may be necessary to characterize adequately particle migration around knee prostheses.

The use of color in wear debris analysis

A method and results of classification of metallic wear debris using their color features are presented. The color images of wear debris were used as initial data. The color features of the debris are specified by an HSI color model. Each particle is characterized by a set of statistical features derived from a distribution of HSI color model components. The initial feature set was optimized by principal component analysis. A multidimensional scaling procedure was used for definition of the classification plane. The selected features allow us to distinguish copper-based alloys, red and dark iron oxides and steel particles. A method was proposed of probabilistic decision of class assignment based on analysis of debris coordinates distribution in the classification plane. The obtained results have demonstrated the feasibility of using color features for automated wear particle analysis.

Analysis of Wear Particles in Lubricant for Non-Disassembling Inspection of EMU Gear Units

A method to predict gear failures without dismantling the gear units uses analysis of the wear particles in the gear oil by ferrography. It shows a relationship between typical gear failures and the features of such particles. Only a small sample of lubricant is needed to reach an accurate conclusion.

Wear-Debris Analysis in Expert Systems

The strategies of monitoring a machine's condition and carrying out operational equipment maintenance are changing, driven by the need for greater economic and time efficiency in modern industry. Nowadays, wear-debris analysis is becoming one of the key techniques used to screen for the presence of abnormal wear in many types of industrial machinery. It is required by industry to have a cost-effective and efficient wear-particle analysis technique to monitor the condition of a machine reliably. The project described here sought to develop a computerised smart system; that is, an expert system to interpret comprehensive data obtained from particle analysis, in order to assess wear modes and wear rates of gearboxes. This paper presents the development procedure, functions and advantages of the expert system.

Alumina ceramic bearings for hip endoprostheses: the Austrian experiences.

The current authors review clinical and retrieval experiences with hemispheric monolithic alumina ceramic sockets (Group 1), implanted between 1976 and 1979, and similar modular titanium sockets with alumina ceramic inlays (Group 2), implanted between 1990 and 1995. Both cementless sockets articulated with alumina ceramic femoral ball heads for total hip joint replacements. Clinical followup of patients with hemispheric monolithic alumina ceramic sockets (Group 1, 138 sockets) resulted in a total failure rate of 19.6% after 5 to 20 years. Radiologic analysis of eight stable sockets showed migration of 0.2 mm to 2.89 mm, but in four sockets at risk for late aseptic failure after an average followup of 12.5 years as much as 13.4 mm of migration was seen. Histologic evaluation revealed pseudosynovial membranes as thick as 1 mm with fine birefringent wear particles within mononuclear macrophages around two stable retrieved sockets. The membranes around four loose sockets were 6 to 10 mm thick and also heavily loaded with larger alumina wear particles. After 7 years followup clinical analysis of patients with modular titanium sockets with alumina ceramic inlays (Group 2, 30 sockets) resulted in four revisions, compared with one revision of 50 identical sockets (control group) with polyethylene instead of alumina ceramic inlays. Wear particle analyses in scanning electron microscopy showed significantly more particles (x 10(9) +/- standard deviation/g dry tissue) from the control group (4.26+/-6.38), compared with alumina ceramic bearings of Group 1 (0.70+/-0.79), and of Group 2 (1.62+/-2.13). The alumina particle sizes ranged between 0.13 and 78.38 microm. The mean annual linear wear of 38.8 microm was calculated for the bearings in Group 1, and of 26.94 microm for bearings in Group 2. These results support the good tribologic and biologic performance of alumina ceramic bearings for total hip arthroplasty.

Scale-invariant analysis of wear particle morphology—a preliminary study

The importance of wear particle characterization is continuously growing, as the need for prediction and monitoring of wear increases. Accurate analysis of wear particles can, however, be limited by problems associated with particle characterization, especially of the wear particles' surface morphology. Since the shape and surface topography of wear particles often exhibit a fractal nature, fractal (scale-invariant) methods are, therefore, used in their characterization. However, the methods used to date ignore the fact that all fractal objects can be described by a small set of mathematical rules; although finding those rules which describe a particular fractal image is a difficult problem. No general solution exists to date and this paper attempts to redress this problem. A new analysis method, ‘scale-invariant analysis’, which is based on a partitioned iterated function system (PIFS), is proposed for the characterization of wear particle morphology. PIFS is a collection of contractive affine transformations. Each affine transformation transforms one part of a wear particle image onto another part of the same image. PIFSs were constructed for both computer generated and SEM images of wear particles. Results obtained in this study clearly demonstrate that the morphology of wear particles can effectively be characterized using the PIFS method.

Wear particle classification in a fuzzy grey system

The analysis and identification of wear particles for machine condition monitoring is usually conducted by experienced inspectors, and, thus, the process is usually very time-consuming. To overcome this obstacle, grey system theory has been applied in this study. The theory of grey systems is a new technique to perform prediction, relational analysis and decision making in many areas. In this paper, the theory of grey relational grades has been used to classify six types of metallic wear debris whose three-dimensional images are acquired from laser scanning confocal microscopy. Their boundary morphology and surface topology are then described by certain numerical parameters. Since the parameters have different levels of significance for different types of wear debris for particle identification, weighting factors of the parameters have been taken into consideration. To determine the weighting factors for the study, fuzzy logic has been applied. This study has demonstrated that a grey system combined with fuzzy logic can be used to classify wear particles satisfactorily.

Condition monitoring of steam turbine-generator through contamination analysis of used lubricating oil

The objective of this paper is to present a systematic analysis of wear particles contained in used lubricant of steam turbine-generator of a thermal power station. The turbogenerator was condition-monitored over a period of two years through wear debris and particulate contamination analysis of the oil. Various sophisticated techniques such as automatic particle counter, ferrography, inductively coupled plasma atomic emission spectrometry (ICPAES), scanning electron microscopy and energy dispersive X-ray analysis (SEM/EDAX) have been employed to extract the relevant information about the health of the machine. Eventually, a correlation of different techniques of wear debris monitoring on the basis of current investigation ascertains the significance of the collective approach of various techniques to avoid catastrophic breakdowns and expensive component replacements.

The Study of Three-Dimensional Analysis Techniques and Automatic Classification Systems for Wear Particles

Although the morphology of wear debris generated in a machine has a direct relationship to wear processes and machine condition, studying wear particles for machine condition monitoring has not been widely applied in Industry as it is time consuming and requires certain expertise of analysts. To overcome these obstacles, automatic wear particle analysis and identification systems need to be developed. In this paper, laser scanning confocal microscopy has been used to obtain three-dimensional images of metallic wear particles. An analysis system has been developed and applied to study the boundary morphology and surface topography of the wear debris. After conducting the image analysis procedure and selecting critical criteria from dozens of available parameters, neural networks and grey systems have been investigated to classify unknown patterns using the numerical descriptors. It is demonstrated that the combination of the image analysis system and automatic classification systems has provided an automatic package for wear particle study which may be applied to industrial applications in the future.

Computer image analysis of wear particles in three-dimensions for machine condition monitoring

Six common types of metallic wear particles, i.e., cutting, spherical, rubbing, laminar, fatigue chunk and severe sliding particles, have been studied in this paper based on the features of their boundary morphology and surface topography. Certain numerical descriptors, such as area, fibre ratio, height aspect ratio and fractal dimension, have been chosen to describe the characteristics of the boundary profiles of the wear debris. Since boundary parameters are insufficient to distinguish certain types of wear debris, several surface parameters extracted from the amplitude and spatial properties of the particles have been applied to investigate the surface roughnesses and textures of laminar, fatigue chunk and severe sliding wear debris. The study shows that classifying wear debris by studying the combined properties of their boundary profiles, surface roughnesses and textures can identify these six types of wear particles. Furthermore, nine parameters have been selected as criteria for wear particle analysis, and can be used to develop automatic computer wear particle analysis systems based on numerical descriptors.