Characteristics Of Wear Debris Research Articles

Real-Time Ferrogram Segmentation of Wear Debris Using Multi-Level Feature Reused Unet.

The real-time monitoring and fault diagnosis of modern machinery and equipment impose higher demands on equipment maintenance, with the extraction of morphological characteristics of wear debris in lubricating oil emerging as a critical approach for real-time monitoring of wear, holding significant importance in the field. The online visual ferrograph (OLVF) technique serves as a representative method in this study. Various semantic segmentation approaches, such as DeepLabV3+, PSPNet, Segformer, Unet, and other models, are employed to process the oil wear particle image for conducting comparative experiments. In order to accurately segment the minute wear debris in oil abrasive images and mitigate the influence of reflection and bubbles, we propose a multi-level feature reused Unet (MFR Unet) that enhances the residual link strategy of Unet for improved identification of tiny wear debris in ferrograms, leading to superior segmentation results.

Study on wear debris characteristics of different polyethylene under reciprocating sliding and compound motion modes

The UHMWPE wear debris has become an important reason affecting the service life and clinical reliability of the prosthesis. In order to further reveal the wear debris characteristics, three kinds of UHMWPE materials including non-crosslinked UHMWPE (UPE), Crosslinked UHMWPE (XLPE) and E-vitamin doped UHMWPE (EXPE) were taken as research objects, and the quantity, size, morphology and distribution characteristics of wear debris under reciprocating sliding and compound motion modes were systematically studied. Among them, the ball-on-disk samples were adopted under reciprocating sliding, and the real ball-on-socket contact mode of artificial cervical disc prosthesis was adopted under compound motion. The tribological experiments were performed with the frequency of 2Hz under 50,000 cycles with newborn bovine serum solution. The results showed that the dominant wear mechanism was abrasive wear accompanied by fatigue cracks under reciprocating sliding mode, while adhesive wear accompanied by a few abrasive wear under compound motion mode. Compared with the quantity of wear debris under reciprocating sliding mode, the number of UHMWPE debris under composite motion mode was reduced by 28.8%, 31.4% and 37.1% for UPE, XLPE and EXPE debris, respectively. The debris results of the three different polymers were consistent in the quantity, size range, morphology and distribution, but different in the size distribution characteristics. The largest proportion of EXPE debris size occurred in the range of less than 1 μm, while the largest proportion of UPE and XLPE debris size ranges from 1 to 5 μm under reciprocating sliding mode. When the debris size was in the range of 1–5 μm under compound motion mode, it accounted for the largest proportion with a value of 55.7%, 66.7% and 74.0% for UPE, XLPE and EXPE debris, respectively. The UHMWPE debris morphology included six main types: spherical, flakes, rods, fibrillar, granular, and other types. Granular and “other types” debris were the dominant types and the sum proportion of these two types of debris for the three kinds of UHMWPE materials was all as high as 80%. Hence, the material type, pair shape, load and movement profiles may have more or less influence on the quantity, size, morphology and distribution characteristics of UHMWPE debris.

Performance and Wear Debris Characteristics of Karanja Biodiesel and Biolubricant as a Substitute in a Compression Ignition Engine

A dependency on non-renewable petroleum fuel and lubricant in Compression Ignition (CI) engines is concerning when considered with respect to both price and pollution. Alternative energy for CI engines extracted from plants is reliable, renewable, and infinitely more sustainable than fossil fuels. The CI engine's performance and wear analysis is carried out with biodiesel and biolubricant through Condition Based Monitoring (CBM). Biodegradable plant-derived biodiesel extracted from non-edible Karanja seed oil is used as a substitute in assessing the engine's combustion, performance and wear characteristics. An initial benchmark was established for the engine's performance with diesel and engine oil (SAE20W40), after which it was subjected to a long-term endurance test when fuelled with biodiesel blend 20BD as fuel under biolubricant blend 20BL as a lubricant. Lubricating oil samples are collected after 50, 75, 100, 125, and 150 hrs. Finally, the used oil analysis was conducted to analyse the wear debris of oil samples using Atomic Absorption Spectroscopy (AAS) technique. Results revealed lower wear debris detection in the case of biodiesel and biolubricant combination due to better lubricity of plant-derived oil than that of petroleum-derived oils. Finally, the Karanja biodiesel and biolubricant can be used as a substitute in CI engines without requiring engine alteration.

Wear evaluation methods for friction units of aircraft GTE oil systems: the lab-based methods

The problems before reliable wear evalution using traditional lab based methods, such as ferrography, x-ray and spectrography, are discussed. One of those problems is that only a subset of wear debris features is measured by each method, which does not allow one to determine wear type. Filter sample analysis technology must be developed, because oil sample is devoid of diagnostic information due to fine filtration. The solution to aforementioned problems is proposed in the form of a newly developing microwave plasma method.

Five million wear simulation and particle analysis of carbon-based nano-multilayer coatings titanium alloy femoral head

Objective: To analyze the wear debris characteristics ofcarbon-based nano- multilayer coatings on Ti(6)Al(4)V alloys and compared with the cobalt chromium molybdenum alloy (CoCrMo) femoral head to evaluate the friction and wear performance of the new coated femoral head. Methods: Three groups were set up in the wear simulation experiment according to the type of femoral head. Group A: imported Cobalt-Chromium-Molybdenum alloy femoral head (CoCrMo); group B: Titanium alloy femoral head (Ti(6)Al(4)V) with carbon-based nano-multilayer coatings; group C: domestic Cobalt-Chromium-Molybdenum alloy femoral head (CoCrMo). All heads were jointed with an ultra-high molecular weight polyethylene (UHMWPE) acetabular cup. Serum samples were collected and stored in the hip joint simulator. After the sample has been digested and diluted, it was filtered through 5 μm, 1.2 μm and 0.4 μm filters, and the filter paper was collected for testing. Scanning electron microscope (SEM) was used to randomly select regions on the filter to obtain images of wear debris. Energy dispersive X-ray spectroscopy (EDS) was used to determine the elemental type of the particle and to eliminate possible contamination. The composition and structure of the abrasive chips were measured using Fourier transform infrared spectrometer (FTIR). The parameters related to the wear debris includingparticle size, shape, number and volume were calculated. The differences in correlation parameters between the groups were compared to evaluate the friction and wear properties of the new coated joints. Results: The main component of the wear debris produced was UHMWPE, and the particle size was mostly below 1 μm. The submicron particle ratio of group B was 49.4%, which was significantly lower than that of the group A and C (75% and 60%, respectively; χ(2)=66.032, 31.754, both P<0.017). The shape was mainly round, and there was no statistical difference between the groups (χ(2)=0.590, P=0.744). The number of particles in group B was significantly less than that of group C on all filters (t=9.960, 8.019, 5.790, all P<0.01), and less than group A on the 0.4 μm filter (t=7.810, P=0.000). Conclusion: The frictional wear performance of the new carbon-based nano-multilayer coatings femoral head is significantly better than that of the domestic femoral head, and even partially exceeds the imported femoral head level, which helps to reduce the production of particles and prevent osteolysis and aseptic loosening induced by UHMWPE particles.

A numerical approach for predicting the remaining useful life of an aviation hydraulic pump based on monitoring abrasive debris generation

Abstract The effective operation of aviation hydraulic pumps is directly related to the safety of passengers. Accurate remaining useful life (RUL) prediction will help establish maintenance strategy, which will reduce the risks of passengers. Currently, the degradation processes developing in the aviation hydraulic pump cannot be accurately modeled, because of the complexity of the experiment and lack of data. To address the problem, we resort to a numerical approach in this paper, taking uncertainty into consideration. Wear debris features are simulated by Monte Carlo sampling from an abrasive debris generation model. The model describes the relationship between rough surfaces and abrasive debris at the microscale level. A partition-integration RUL prediction framework is proposed to apply the microscale model to the macroscale process of pump degradation. The proposed method is validated by experimental data. The results show that the model is able to cover extreme dry contact conditions and gives good results with small amounts of data.

FECNN: A promising model for wear particle recognition

Wear particle classification is an important approach to analysing wear faults. However, it has always been a challenging task for automatic recognition of wear debris in complex ferrography images. To this end, a convolutional neural network (CNN) model called FECNN is proposed in this study to identify wear particles in complex ferrography images. Different from the architecture of ordinary CNN models, one-dimensional (1-D) convolutional operations and a dropout operation are incorporated into the FECNN model to make it more suitable for ferrography image processing. The 1-D convolutional operations reduce the number of model parameters, and the dropout operation makes the model for learning the incomplete features of wear debris. A large number of wear particles are obtained from mechanical parts made of steel to test the FECNN model. The results show that the FECNN model can automatically extract the features of wear particles from the ferrography images containing much background noise. Furthermore, the FECNN model obtains more accurate classification results than that of the previous method with a few training samples, which alleviates the workload of sample collection. The FECNN model’s powerful capability of feature extraction and wear particle classification indicates the FECNN model is a promising tool for wear particle recognition.

Online wear characterisation of rolling element bearing using wear particle morphological features

Rolling element bearings are commonly used in rotatory machines and their operation conditions need to be monitored to prevent catastrophic failures. This research aims at developing an effective online monitoring method to analyse wear severity and wear mechanisms of bearings. For this purpose, morphological features of wear debris from bearings, including quantity, colour, size and shape, are extracted from videos of moving particles carried in lubrication oil. Moving particles are detected and tracked based on the Gaussian background mixture model and the blob detection algorithm, and the target particles are separated from the image background by background subtraction. Then particle shape and dimensional features both in two- and three-dimensions are extracted from multiple images captured from different views. At the same time, the numbers of moving particles are determined based on the tracking results. The developed techniques for online particle feature extraction are applied to a rolling element bearing test rig. The particle quantity is used to indicate the trend of wear state evolution, while the colour, size and shape features of wear debris are used to classify particles into different types for wear mechanism analysis. The relationship between wear mechanisms and the trend of wear states in the time domain is established. Based on the gathered information and wear analyses, a comprehensive characterisation is obtained for online monitoring the wear state evolution as well as evaluating the wear severity and wear modes. The proposed method is able to provide wear mechanism information for root cause analysis and thus enhance the capabilities of existing online wear debris monitoring techniques.

The gearbox wears state monitoring and evaluation based on on-line wear debris features

The teeth of planetary gear trains are prone to wear and can be damaged. In traditional wear monitoring, the equipment needs to be disassembled, and the efficiency is low. The wear debris in lubricating oil can provide information about machine wear. On-line oil monitoring technology can easily recognize the change of wear debris concentration in the gearbox oil pan without the need for disassembling equipment. However, existing on-line monitoring systems are often based on wear debris concentration, providing limited information on particle shape and type, which are important for root cause analysis. In this study, some new features of wear debris were extracted for on-line wear monitoring in order to improve wear state evaluation and prediction using on-line approach.

Spur gear wear analysis as applied for tribological based predictive maintenance diagnostics

This paper describes an experimental investigation on a pair of spur gears in which wear and pitting were intentionally allowed to occur, namely, moisture corrosion pitting, acid-induced corrosion pitting, hard contaminant-related pitting and mechanical induced wear. A back to back spur gear test rig was used throughout 32 test series. The tests samples of wear debris were collected and assessed through the utilization of an optical microscope in order to correlate and compare the debris morphology to pitting and wear degradation of the worn gears. In addition, weight loss from all test gear pairs were assessed with utilization of statistical design. It was observed that there were significant distinctions between both weight losses, wear debris morphology generated from each induced pitting conditions and worn surface characteristics. It can be deduced that wear debris characteristics exhibited a direct relationship with different pitting and wear modes. Thus, it should be possible to detect and diagnose gear pitting and wear utilization of worn surfaces, generated wear debris and quantitative measurement such as weight loss.

Restoration of defocused ferrograph images using a large kernel convolutional neural network

Wear debris analysis is used for machine condition monitoring because it is capable of identifying wear related faults in machinery components. In particular, wear mechanisms can be explored based on debris shape and texture features. As current wear monitoring practices prefer rapid data interpretation, on-line ferrograph is favored where debris images are collected and processed on-site with an imaging apparatus. However, such images are often blurred due to lubricant contamination and limited optical field depth. This drawback makes the subsequent investigation of wear debris features a challenging task. To address this issue, a restoration method is developed to reduce the influence of out-of-focus blurring in on-line ferrograph images. Firstly, in order to model the out-of-focus blurring process, a degradation model is constructed with a convolutional neural network. Different from general deep learning structures, larger convolutional kernels are embedded in the network to more comprehensively characterize the blurring process. Next, training samples of different types of debris are collected in the laboratory. The neural network structure then is optimized to minimize the difference between restored images and their corresponding ground truths. The final restoration is conducted based on the trained network via a pixel-wise regression process. To evaluate the performance of the developed method, experiments are carried out with debris images collected from a gear-box test rig. Results have demonstrated that: (1) the proposed restoration strategy allows robust extractions of debris features in greater detail; (2) the developed method is more computationally efficient than traditional deblurring techniques, which enables it to be applicable in on-line machine health monitoring.

Segmentation of Online Ferrograph Images with Strong Interference Based on Uniform Discrete Curvelet Transformation.

Through real-time acquisition of the visual characteristics of wear debris in lube oil, an on-line visual ferrograph (OLVF) achieves online monitoring of equipment wear in practice. However, since a large number of bubbles can exist in lube oil and appear as a dynamically changing interference shadow in OLVF ferrograms, traditional algorithms may easily misidentify the interference shadow as wear debris, resulting in a large error in the extracted wear debris characteristic. Based on this possibility, a jam-proof uniform discrete curvelet transformation (UDCT)-based method for the binarization of wear debris images was proposed. Through multiscale analysis of the OLVF ferrograms using UDCT and nonlinear transformation of UDCT coefficients, low-frequency suppression and high-frequency denoising of wear debris images were conducted. Then, the Otsu algorithm was used to achieve binarization of wear debris images under strong interference influence.

The generation mechanism and morphological characterization of cutting debris based on the finite element method

Micro-cutting is a material removal mechanism with cutting debris generation, where the condition monitoring of the mechanism is important in a wear process. As a useful technique, wear debris analysis has been widely adopted for identifying the wear mechanisms in a running machine. However, there is no corresponding relationship between wear particle features and wear conditions. This paper investigates the generation mechanism of cutting debris by simulating a micro-cutting process. In particular, this study is to establish a quantitative relationship between their morphologies and cutting conditions. A three-dimensional model with a rigid pyramid sliding on the surface of #45 steel has been developed. The deformation and failure criteria of the steel are the Johnson–Cook and shear failure model, respectively. The conditions for producing cutting wear debris are studied and then the morphological features of wear debris are extracted. The main findings are as follows: (1) the critical conditions for cutting wear particle generation is jointly determined by the attacking angle and the cutting depth; (2) the width and curvature of cutting wear debris are mainly correlated with the normal load, which determines the indentation depth of the pyramid; (3) the curvature of cutting wear debris depends mainly on the sliding velocity. The understanding of the relationship between the cutting wear conditions and the morphologies of wear debris is useful for supporting debris-based wear monitoring.

Fractal dimension and size distribution characteristics of ultrahigh- molecular-weight polyethylene wear particles in condition of artificial knee joint simulator

Background: As a typical friction pair, the artificial joints also generate a large number of wear particles due to the wear movement, which has become the direct cause of the debris disease called in clinical. The wear debris with different morphology and size contains a large amount of information relating to the wear status, and has the typical fractal characteristics. These fractal characteristics with a certain degree of regularity are favorable to study the wear mechanism under the different wear conditions. The aim of this study is to evaluate the fractal dimension and size distribution characteristics of UHMWPE wear particles in condition of artificial knee joint simulator by the automatic extraction and fractal identification system of wear debris based on the improved radar graph method. Methods: The forged CoCrMo alloy (ISO5832-12) and ultra-high-molecular-weight polyethylene (UHMWPE, ISO5834-2) were selected as the artificial joint prosthesis materials. The knee joint simulator produced by University of Leeds (Simulation Solutions Ltd.) was used to realize the knee joint wear motion. The wear debris extraction was based on the standard of ISO17853. Scanning electron microscope (Hitachi, S3000N) was used to observe the morphology characteristics of wear debris. The automatic extraction and fractal identification system of wear debris based on the improved radar graph method was used to investigate the fractal characteristics and size distribution of UHMWPE wear particles. Results: The fractal characteristics of the single UHMWPE wear debris are very obvious. When the wear particles changes from the larger sized strip to the smaller sized near-sphere, the radar fractal dimension decreases continuously. The fractal dimension D of ideal spherical shaped wear debris is close to zero. The fractal characteristics of UHMWPE wear debris group directly reflects the transformation trend of wear mechanism. In the initial stage of wear, the strip and flaky wear debris with the larger fractal dimension occupy the largest proportion in total wear debris. The adhesive and ploughing wear are the main wear mechanisms, corresponding to the running-in stage of the wear movement. With the extension of wear running-cycles, the proportion of strip wear debris reduces and the percentages of flaky and blocky wear debris increase obviously. The wear mechanisms gradually change to the abrasive and fatigue spalling wear and the wear movement enters into the composite wear stage. When the friction pair enters the stable wear stage, the proportion of all kinds of wear debris has little change. But the number of small-sized wear debris increases due to the action of cyclic stress, which causes the fractal dimension of wear debris group to decrease to some extent. Conclusions: The fractal dimension and size distribution of wear debris with different profiles can be obtained by the automatic extraction and fractal identification system of wear debris based on the improved radar graph method. It has enough numerical gradients to distinguish the wear debris morphology and avoid the effects of errors. It is not only used for the shape extraction, calculation of fractal dimension and statistics of wear particle parameters for the single and group of wear debris, but also provides a new digital analysis tool for the identification and diagnosis of the artificial joint wear debris.

Wear state identification using dynamic features of wear debris for on-line purpose

Wear status identification including wear rate estimation and wear mechanism assessment can be performed using wear debris information. However, although on-line monitoring methods have distinctive advantages over off-line approaches, existing on-line monitoring methods provide limited features of wear particles and have difficulties characterising complex wear states. Most of them determine wear status based on changes in the wear rates, and the wear mechanisms are not taken into consideration. Therefore, comprehensive wear state identification is a bottleneck in real-time machine health monitoring for condition-based maintenance. In order to further advance on-line monitoring technology, this paper, in a case study format, presents a new approach for wear state characterisation using comprehensive wear debris features. For this purpose, wear experiments were carried out on a four-ball rig, and a particle imaging system was employed to capture videos of moving particles to acquire dynamic features. Based on this, wear particles were firstly counted to characterise wear rate. In this stage, a statistical clustering model was established using a mean-shift algorithm to categorise wear debris samples. A trend of wear state evolution was thus obtained. Secondly, the size, shape and colour of wear debris were extracted to identify particles into fatigue, sliding and oxides for wear mechanism analysis. The analysis results of wear mechanisms were related to the trend of the wear state. Correspondingly, a changing chart that contains the wear degree and wear mechanisms was drawn. Therefore, an on-line system has been developed to capture comprehensive particle information to assess the wear severity and mechanisms for in-depth wear analysis and full-life machine condition monitoring.

A microfluidic device for three-dimensional wear debris imaging in online condition monitoring

Three-dimensional morphologies of wear particles are important information sources for machine condition assessment and fault diagnosis. However, existing three-dimensional image acquisition systems, such as laser scanning confocal microscopy and atomic force microscopy, cannot be directly applied in condition-based maintenance of machines. In order to automatically acquire three-dimensional information of wear debris for online condition monitoring, a microfluidic device consisting of an oil flow channel and a video imaging system is developed. This paper focuses on the control of particle motions. A microchannel is designed to ensure the continuous rotation of particles such that their three-dimensional features can be captured. The relationships between running torque and channel height and particle size are analysed to determine the channel height. An infinite fluid field is considered to make sure that the particles rotate around the same axis to capture 360 degree views. Based on this, the cross section of the microchannel is determined at 5 mm × 0.2 mm (height × width) to capture the wear debris under 200 µm. A CMOS sensor is used to image the particles in multiple views and then three-dimensional features of wear debris (e.g. thickness, height aspect ratio and sphericity) are obtained. Two experiments were carried out to evaluate the performances of the designed system. The results demonstrate that (1) the microfluidic device is effective in capturing multiple view images of wear particles various in sizes and shapes; (2) spatial morphological characteristics of wear particles can be constructed using a sequence of multi-view images.

Morphological Feature Extraction Based on Multiview Images for Wear Debris Analysis in On-line Fluid Monitoring

ABSTRACTWear state is an important indicator of machinery operation condition that reveals whether faults have developed and maintenance should be scheduled. Among the available techniques, vision-based on-line monitoring of wear particles in the lubricant circuit is preferred, where three-dimensional particle characterizations can be obtained for wear mode analysis. This article presents the application of an imaging system that captures wear particles in lubricant flow and the development of image processing procedures for multiview feature extraction. In particular, a framework including background subtraction, object segmentation, and debris tracking was adopted. Particle features were then used in a comprehensive morphological description of wear debris. Experiments showed that the system is able to produce a feasible and reliable indication of wear debris characteristics for machine condition monitoring.

人工関節における表面テクスチャリングの効果

The reduction of tissue reactions in an artificial joint composed of an ultra-high molecular weight polyethylene sliding on a Co-Cr-Mo alloy, was investigated. To control the specific wear rate and morphological features of wear debris of the polyethylene, micro slurry-jet erosion was used as a precision machining method for the bearing surface of a Co-Cr-Mo alloy. This method constitutes a type of wet blasting technique, which uses alumina particles as an abrasive medium, along with compressed air and water, to create textured surfaces. Pin-on-disc wear tests, involving multidirectional sliding motion, resulted in a decrease in the polyethylene wear and changes in the morphological features. Primary human peripheral blood mononuclear phagocytes were incubated with the debris, and the wear debris generated on the textured surface changed the secretion of the IL-6 proinflammatory cytokines. We concluded that appropriate surface geometry contributed to prolong the service life of artificial joints.

A Study and Development of Frictional Materials Containing Fly Ash for Motorcycle Clutch Production

In this work, the properties of frictional materials for motorcycle clutch with fly ash replacements of some original substances were studied aiming to reduce the raw materials cost. The fly ash was obtained from the domestic BLCP power plant in Rayong, Thailand. Two replacement schemes were studied;1) all of the original ingredients comprising the clutch was replaced with 15-35 %wt. fly ash and 2) only the original ingredients with similar physical and chemical properties were replaced with 40-55 %wt. fly ash. The mechanical properties, the friction coefficient and the wear performance were then studied. In addition, micro-structural characterizations of the contact surface and the wear debris were carried out. It was found that the fly ash replacement to a certain level increased the hardness resulting in a decrease in the surface wear. The specimens with fly ash replacement were found to have smoother surface leading to a lower static friction coefficient. The dynamic friction coefficient was altered according to the wear debris characteristics. The prototypes with the optimum amount of fly ash, i.e. giving minimum cost with superior or similar properties to the original product, for each scheme was produced and tested on a dynamometer. The prototypes for the 1st and 2nd schemes were found to offer the reduction in the wear rates of approx. 22 to 56% with a reduction in the cost of raw materials of approx. 15 and 23%, respectively. Severe damage of the prototypes was observed slightly sooner than that found for the original product.

Qualitative ferrographic analysis method by quantitative parameters of wear debris characteristics

PurposeThe purpose of this paper is to propose a method of qualitative ferrographic analysis by quantitative parameters of wear debris characteristics.Design/methodology/approachThe amount of the wear debris needed for analysis on the ferrogram made by rotary ferrograph is discussed based on the theory of debris group. Quantitative parameters are constituted to express the characteristics of wear debris group, and correlation coefficients are employed to establish the relationship between wear debris and wear condition. The reliability of the method was verified by wear test experiments and ferrographic analysis.FindingsThe wear condition of machines should be determined by studying all the debris together as a group rather than by focusing on individual debris. In the proposed method, the qualitative analysis result is obtained by synthetic analysis of quantitative parameters of wear debris characteristics using a computer program, which makes the judgment of the wear system condition more objective and precise.Research limitations/implicationsIn the procedure of wear condition monitoring by the proposed method, because the weight factors and correlation coefficients introduced in this paper are determined according to the experiences deriving from practice among mining machinery, further rectifications may be needed if they are applied to other industrial field.Originality/valueThe paper illustrates a more objective and precise ferrographic analysis method for wear condition monitoring.