Performance Deterioration Analysis of the Used Gear Oil

In the present paper, we have investigated the tribological properties and microstructure of the used gear oil theoretically and experimentally. The deterioration process of the in-service gear lubricant oils was also discussed. The viscosity and microstructure of oils running different mileages were analyzed by viscometer and Fourier Transform Infrared Spectroscopy (FTIR), respectively. In addition, the friction and wear behaviors of the friction pair of the GCr15 steel ball and disc were investigated using a ball on disc tribometer under different mileages’ gear oils lubrication conditions. These techniques give reproducible and reliable data with which to evaluate the severity of deterioration process of oils. The objective of this work is to understand the deterioration process of gear oil and analyze the influence of deterioration on the performance and microstructure of lubricant oils used in gear box. Possible explanations of deterioration process as well as its influence on friction and wear behaviors are also discussed. The results reveals the tribological properties of used oils depended on strongly the microstructure and its deterioration process of 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%.

Comparison-embedded evidence-CNN model for fuzzy assessment of wear severity using multi-dimensional surface images

Wear topography is a significant indicator of tribological behavior for the inspection of machine health conditions.An intelligent in-suit wear assessment method for random topography is here proposed.Three-dimension(3D)topography is employed to address the uncertainties in wear evaluation.Initially,3D topography reconstruction from a worn surface is accomplished with photometric stereo vision(PSV).Then,the wear features are identified by a contrastive learning-based extraction network(WSFE-Net)including the relative and temporal prior knowledge of wear mechanisms.Furthermore,the typical wear degrees including mild,moderate,and severe are evaluated by a wear severity assessment network(WSA-Net)for the probability and its associated uncertainty based on subjective logic.By integrating the evidence information from 2D and 3D-damage surfaces with Dempster–Shafer(D–S)evidence,the uncertainty of severity assessment results is further reduced.The proposed model could constrain the uncertainty below 0.066 in the wear degree evaluation of a continuous wear experiment,which reflects the high credibility of the evaluation result.

A finite-element-aided ultrasonic method for measuring central oil-film thickness in a roller-raceway tribo-pair

Roller bearings support heavy loads by riding on an ultra-thin oil film(between the roller and raceway),the thickness of which is critical as it reflects the lubrication performance.Ultrasonic interfacial reflection,which facilitates the non-destructive measurement of oil-film thickness,has been widely studied.However,insufficient spatial resolution around the rolling line contact zone remains a barrier despite the use of miniature piezoelectric transducers.In this study,a finite-element-aided method is utilized to simulate wave propagation through a three-layered structure of roller-oil-raceway under elastohydrodynamic lubrication(EHL)with nonlinear characteristics of the i)deformed curvature of the cylindrical roller and ii)nonuniform distribution of the fluid bulk modulus along the circumference of the oil layer being considered.A load and speed-dependent look-up table is then developed to establish an accurate relationship between the overall reflection coefficient(directly measured by an embedded ultrasonic transducer)and objective variable of the central oil-film thickness.The proposed finite-element-aided method is verified experimentally in a rollerraceway test rig with the ultrasonically measured oil-flm thickness corresponding to the values calculated using the EHLtheory.