Dose-dependent effects of lanthanum chloride on wear particle-induced aseptic inflammation in a murine air-pouch model

To investigate the effects of local injection of different doses of lanthanum chloride （LaCl3） on aseptic inflammation in mice stimulated by wear particles from artificial joints, the particles were prepared by vacuum ball mill in vitro and air-pouch models were performed with 45 male BALB/c mice that were randomly divided into blank control group, wear particle group and wear parti- cle ＋ LaCl3 （0.1, 0.9 and 8.1 μmol） group. All animals were sacrificed and tissue specimens were harvested 7 days after treatment. Hematoxylin and eosin （H＆E） staining, enzyme-linked immunosorbent assay （ELISA）, reverse transcription-polymerase chain reac- tion （RT-PCR） and western blot were applied to observe inflammatory reaction and detect the expression of pro-inflammatory cyto- kines （TNF-et, IL-1β） and nuclear factor-κB （NF-κB） in mRNA and protein levels in air-pouch membrances. The results showed that wear particles could stimulate aseptic inflammation in vivo effectively; 0.9 μmol LaCl3 could significantly inhibit wear parti- cle-induced gene and protein expression of pro-inflammatory cytokines and NF-Id3 （P〈0.05）; 0.1 and 8. 1 μmol LaCl3 did not exert an inflammation-inhibiting effect and even caused adverse effects at 8.1 μmol. In conclusion, LaC13 played a protective role against wear particle-induced aseptic inflammation dose-dependently, which was involved in NF-κB related signaling pathways.

Morphological residual convolutional neural network(M-RCNN)for intelligent recognition of wear particles from artificial joints

Finding the correct category of wear particles is important to understand the tribological behavior.However,manual identification is tedious and time-consuming.We here propose an automatic morphological residual convolutional neural network(M-RCNN),exploiting the residual knowledge and morphological priors between various particle types.We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution.Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance.M-RCNN demonstrates a much higher accuracy(0.940)than the deep residual network(0.845)and support vector machine(0.821).This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints.

Wear particle-mediated expressions of pro-inflammatory cytokines, NF-κB and RANK were impacted by lanthanum chloride in RAW264.7 cells

To explore the impact of different concentrations of lanthanum chloride （LaC13） on critical components of wear particle-mediated signaling pathways in inflammation and osteoclastogenesis, RAW264.7 cells were naturally divided into eight groups and analyzed by CCK-8 assay, flow cytometry, ELISA, RT-PCR and western blot after treatments. The results showed that three concentrations of LaCI3 had no influence on viability of RAW264.7 cells and down-regulated receptor activator of nuclear factor rd3 （RANK） instead of macrophage colony-stimulating factor receptor （M-CSFR）. Additionally, 2.5 and 10 pmol/L LaC13 could signifi- cantly inhibit gene and protein levels of pro-inflammatory cytokines （tumor necrosis factor-or and interleukin-113, i.e., TNF-ct and IL-113） and NF-r,B/p65, but 100 pmol/L LaC13 did not exert an obvious inflammation-inhibiting effect, and even induced inflamma- tion. In conclusion, these findings demonstrated that LaC13 was able to suppress wear particle-induced inflammation and activation of NF-rd3 in a certain range of concentrations in vitro and mainly decrease the expression of RANK, but not M-CSFR, all of which were generally recognized to play a pivotal role in osteoclastogenesis.

Dimensional Description of On-line Wear Debris Images for Wear Characterization

As one of the most wear monitoring indicator, dimensional feature of individual particles has been studied mostly focusing on off-line analytical ferrograph. Recent development in on-line wear monitoring with wear debris images shows that merely wear debris concentration has been extracted from on-line ferrograph images. It remains a bottleneck of obtaining the dimension of on-line particles due to the low resolution, high contamination and particle’s chain pattern of an on-line image sample. In this work, statistical dimension of wear debris in on-line ferrograph images is investigated. A two-step procedure is proposed as follows. First, an on-line ferrograph image is decomposed into four component images with different frequencies. By doing this, the size of each component image is reduced by one fourth, which will increase the efficiency of subsequent processing. The low-frequency image is used for extracting the area of wear debris, and the high-frequency image is adopted for extracting contour. Second, a statistical equivalent circle dimension is constructed by equaling the overall wear debris in the image into equivalent circles referring to the extracted total area and premeter of overall wear debris. The equivalent circle dimension, reflecting the statistical dimension of larger wear debris in an on-line image, is verified by manual measurement. Consequently, two preliminary applications are carried out in gasoline engine bench tests of durability and running-in. Evidently, the equivalent circle dimension, together with the previously developed concentration index, index of particle coverage area （IPCA）, show good performances in characterizing engine wear conditions. The proposed dimensional indicator provides a new statistical feature of on-line wear particles for on-line wear monitoring. The new dimensional feature conveys profound information about wear severity.

Wear Fault Diagnosis of Machinery Based on Neural Networks and Gray Relationships

In this paper, the regular characteristic of -wear particles related to fault type of machines based on condition monitoring of reciprocal machinery is discussed. The typical -wear particles spectrum is established according to the equipment structure , friction and wear rule and the characteristic of 'wear particles; The identification technology of wear particles is proposed based on neural networks and a gray relationship ; an intelligent wear particles identification system is designed. The diagnosis example shows that this system can promote the accuracy and the speed of wear particles identification.

The Production and Fracture Characteristrics of Particles in Radial Sliding Bearing

Based on the running characteristics,the experimental results show that wear particles appear on the normal running stage.The fractural characteristics of wear particles were investigated, it is found there exists a relation between the wear characters and bear conditions.

An On-line Ferrograph for Monitoring Machine Wear

In order to improve an on-line ferrograph, this paper simulates a three-dimensional magnetic field distribution of an electromagnet, builds a sinking motion model of a wear particle, and investigates the motion law of wear particles under two different conditions. Both numeric results and experimental results show that the on-line ferrograph is capable of monitoring machine wear conditions by measuring the concentration and size distribution of wear particles in lubricating oil.

Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor

Ferrograph-based wear debris analysis(WDA)provides significant information for wear fault analysis of mechanical equipment.After decades of offline application,this conventional technology is being driven by the online ferrograph sensor for real-time wear state monitoring.However,online ferrography has been greatly limited by the low imaging quality and segmentation accuracy of particle chains when analyzing degraded lubricant oils in practical applications.To address this issue,an integrated optimization method is developed that focuses on two aspects:the structural re-design of the online ferrograph sensor and the intelligent segmentation of particle chains.For enhancing the imaging quality of wear particles,the magnetic pole of the online ferrograph sensor is optimized to enable the imaging system directly observe wear particles without penetrating oils.Furthermore,a light source simulation model is established based on the light intensity distribution theory,and the LED installation parameters are determined for particle illumination uniformity in the online ferrograph sensor.On this basis,a Mask-RCNN-based segmentation model of particle chains is constructed by specifically establishing the region of interest(ROI)generation layer and the ROI align layer for the irregular particle morphology.With these measures,a new online ferrograph sensor is designed to enhance the image acquisition and information extraction of wear particles.For verification,the developed sensor is tested to collect particle images from different degraded oils,and the images are further handled with the Mask-RCNN-based model for particle feature extraction.Experimental results reveal that the optimized online ferrography can capture clear particle images even in highly-degraded lubricant oils,and the illumination uniformity reaches 90%in its imaging field.Most importantly,the statistical accuracy of wear particles has been improved from 67.2%to 94.1%.

LVQ神经网络分类器在润滑油磨粒识别上的应用

A technique for wear particle identification using computer vision system is described. The computer vision system employs LVQ Neural Networks as classifier to recognize the surface texture of wear particles in lubricating oil and determine the conditions of machines. The recognition process includes four stages:(1)capturing image from ferrographies containing wear particles;(2) digitising the image and extracting features;(3) learning the training data selected from the feature data set;(4) identifying the wear particles and generating the result report of machine condition classification. To verify the technique proposed here, the recognition results of several typical classes of wear particles generated at the sliding and rolling surfaces in a diesel engine are presented.

A Development of A Computer aided Ferrographic System

In this paper the authors suggest a computer aided system for processing data and images of ferrographic analysis by the authors. The system consists of seven modules and five databases. There is a typical wear particle library in the system. Its applications state that the analytical speed increases with this system and more information can be obtained by using this system.

Microstructure and Tribological Behavior of a TiO_2/hBN Composite Ceramic Coating Formed via Micro-arc Oxidation of Ti–6Al–4V Alloy

A composite coating containing hexagonal boron nitride（hBN） particles and titanium oxide（TiO_2） was formed on the surface of Ti-6Al-4V alloy via micro-arc oxidation（MAO）. The effect of quantity of the hBN-particles added into electrolyte on microstructure, composition, and wear behavior of the resulting composite coatings was investigated. Microstructure, phase composition, and tribological behavior of the resulting MAO coatings were evaluated via scanning electron microscopy, X-ray diffraction, and ball-on-disc abrasive tests. The results reveal that the TiO_2/hBN composite coating consisting of rutile TiO_2, anatase TiO_2, and an hBN phase was less porous than particle-free coating. Furthermore, the presence of hBN particles in the MAO coating produced an improved anti-friction property. The composite coating produced in the electrolyte containing 2 g/L of hBN particles exhibited the best wear resistance.The outer loose layer of the MAO coatings was removed by a mechanical polishing process, which led to a significant improvement in the wear resistance and anti-friction properties of the MAO coatings and highlighted an essential lubricating role of hBN particles in the composite coatings. However, wear mechanism of the MAO coatings was not relevant to the presence of hBN particles, where fatigue wear dominated the anti-fraction properties of the MAO coatings with and without hBN particles.