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The purpose of this paper is to propose a method of qualitative ferrographic analysis by quantitative parameters of wear debris characteristics.

The 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.

The 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.

In 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.

The paper illustrates a more objective and precise ferrographic analysis method for wear condition monitoring.

The purpose of this paper is to identify smaller wear particles and improve the calculation speed, identify more abrasive particles and promote industrial applications.

This paper studies a new intelligent recognition method for equipment wear debris based on the YOLO V5S model released in June 2020. Nearly 800 ferrography pictures, 23 types of wear debris, about 5,000 wear debris were used to train and test the model. The new lightweight approach of wear debris recognition can be implemented in rapidly and automatically and also provide for the recognition of wear debris in the field of online wear monitoring.

An intelligent recognition method of wear debris in ferrography image based on the YOLO V5S model was designed. After the training, the GIoU values of the model converged steadily at about 0.02. The overall precision rate and recall rate reached 0.4 and 0.5, respectively. The overall MAP value of each type of wear debris was 40.5, which was close to the official recognition level of YOLO V5S in the MS COCO competition. The practicality of the model was approved. The intelligent recognition method of wear debris based on the YOLO V5S model can effectively reduce the sensitivity of wear debris size. It also has a good recognition effect on wear debris in different sizes and different scales. Compared with YOLOV. YOLOV, Mask R-CNN and other algorithms%2C, the intelligent recognition method based on the YOLO V5S model, have shown their own advantages in terms of the recognition effect of wear debris%2C the operation speed and the size of weight files. It also provides a new function for implementing accurate recognition of wear debris images collected by online and independent ferrography analysis devices.

To the best of the authors’ knowledge, the intelligent identification of wear debris based on the YOLO V5S network is proposed for the first time, and a large number of wear debris images are verified and applied.

The dependency on human expertise for analysis and interpretation is the main reason for wear debris analysis not being used in industry to its full potential and becoming one of the most powerful machine condition monitoring strategies. The dependency on human expertise makes the interpretation and result subjective in nature, costly and time consuming. The purpose of this paper is to review work being done to develop an automatic, reliable and objective wear particle classification system as a solution to the above problem. At the same time it also aims to discuss some common off line test methods being practiced for wear debris analysis.

Computer image analysis is a solution for some of the problems associated with the conventional techniques. First it is tried to efficiently describe the characteristics of computer images of different types of wear debris using a few numerical parameters. Then using some Artificial Intelligence tools, the wear particle classification system can be developed.

Many shape, size and surface parameters are discussed in the paper. Out of these, nine numerical parameters are selected to describe and distinguish six common type of wear debris. Once the type of debris is identified, the mode of wear and hence the machine condition can be assessed.

The present process of fault and condition monitoring of an equipment by wear debris analysis involves human judgment of debris formations. A set‐up standard for comparison of debris will enable the maintenance team to diagnose faults in a comparatively better way.

The aim of this paper is to discuss the difficulties in identifying wear particles and finding out the exact health of equipment, which, due to its subjective nature, is influenced by human errors. An objective method with certain standards for classification of wear particles compatible with an artificial intelligence system will yield some flawless results of wear debris analysis, which has not been attempted in the past as per available literature.

In real working condition, signal is highly disturbed and even drowned by noise, which extremely interferes in detecting results. Therefore, this paper aims to provide an effective de-noising method for the debris particle in lubricant so that the ultrasonic technique can be applied to the online debris particle detection.

For completing the online ultrasonic monitoring of oil wear debris, the research is made on some selected wear debris signals. It applies morphology component analysis (MCA) theory to de-noise signals. To overcome the potential weakness of MCA threshold process, it proposes fuzzy morphology component analysis (FMCA) by fuzzy threshold function.

According to simulated and experimental results, it eliminates most of the wear debris signal noises by using FMCA through the signal comparison. According to the comparison of simulation evaluation index, it has highest signal noise ratio, smallest root mean square error and largest similarity factor.

The rapid movement of the debris particles, as well as the lubricant temperature, may influence the measuring signals. Researchers are encouraged to solve these problems further.

This paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.

This paper provides a promising way of applying the MCA theory to de-noise signals. To avoid the potential weakness of the MCA threshold process, it proposes FMCA through fuzzy threshold function. The FMCA method has great obvious advantage in de-noising wear debris signals. It lays the foundation for online ultrasonic monitoring of lubrication wear debris.

In hot forging, a significant amount of forging force is used for overcoming frictional force at the die-billet interface. The high frictional force along with thermomechanical stress lead to wear, plastic deformation, mechanical fatigue and cracks, which reduce the service life of hot forging dies. Of all these different types of issues, wear is the predominant mode of failure in hot forging dies. This paper aims to describe mechanisms of wear transition in different loads at near forging temperature, occurring during sliding of chromium-based H11 tool steel specimens.

High temperature pin-on-disc tests are performed with pin specimens machined from bars of X38CrMoV5 steel, heat treated to surface hardness of 40-42 HRc. The disc is made of EN 31 steel with hardness of 60-62 HRc. Tests are performed at constant temperature of 500°C, and the normal load was varied from 20 to 70 N.

Scanning electron microscopy investigations on worn surface have revealed that wear is primarily due to abrasion and plastic deformation. The test results show an increasing trend in wear rate with increase in load up to 30 N, followed by a reversal in trend until 50 N. This transition in wear rate is caused by development of wear resistant layers, which are formed by compaction of wear debris particles on to the worn surfaces. These compact layers are found to be stable during load range from 40 and 50 N. However, with further increase in load, abrasive wear tracks are observed without any evidence of protective layers. As a result, there is an increase in wear rate with increase in loads above 50 N. In addition, plastic shearing was dominant over abrasive wear at this load regime.

The study on wear behaviour of H11 hot forging steel at 20 to 70 N will be an input to the research in hot forming industries.

It is a challenging task to analysis oxide wear particles when they are stuck together with other types of wear particles in complex ferrography images. Hence, this paper aims to propose a method of ferrography image segmentation to analysis oxide wear debris in complex ferrography images.

First, ferrography images are segmented with watershed transform. Then, two region merging rules are proposed to improve the initial segmentation results. Finally, the features of each particle are extracted to detect and assess the oxide wear particles.

The results show that the proposed method outperforms other methods of ferrography image segmentation, and the overlapping wear particles in complex ferrography images can be well separated. Moreover, the features of each separated wear particles can be easily extracted to analysis the oxide wear particles.

The proposed method provides a useful approach for the automatic detection and assessment of oxide wear particles in complex ferrography images.

The colours, edges and position information of wear debris are considered in the proposed method to improve the segmentation result. Moreover, the proposed method can not only detect oxide wear particles in ferrography images but also evaluate oxide wear severity in ferrography images.

Fretting wear exists widely in the field of matching mechanical parts whereas previous research studies mostly focus on the point contact through a ball-plate tribometer. This paper aims to study the influence of wear debris on the fretting wear characteristics of the nitrided medium carbon steel under line contact condition at elevated temperature.

Fretting wear behavior of the nitrided medium carbon steel was experimentally investigated under line contact condition at elevated temperature and different normal loads without lubrication. Wear loss, worn surface and wear debris were studied to analyze the wear mechanism of nitrided steel.

The results showed that surface hardness of the medium carbon steel was notably improved because of the generation of a 230 µm nitrided case. Wear loss increased with the normal load, which was associated with the damage of a thin solid film formed by the wear debris, consisting of iron oxides and chromium oxide rather than only iron or iron oxides. The wear debris became partially amorphous and spherical because it was trapped within the contact interface and was ground, rolled, oxidized under line contact conditions. The spherical wear debris acted as a third body and formed a lubricating film between the contact faces. This lubricating film helped to stabilize the friction coefficient and reduced the wear rate, which further caused the acceleration of wear volume to gradually decrease. The wear mechanisms of the nitrided steel were oxidation wear, abrasive wear and fatigue spalling of the oxide layer.

The findings are helpful to understand the fretting wear behavior of the friction pair under line contact and enrich the fretting tribology theory.

The purpose of this paper is to show the development of an image processing-based portable equipment for an automatic wear debris analysis. It can analyze both the qualitative and quantitative features of machine wear debris: size, quantity, size distribution, shape, surface texture and material composition via color.

It comprises hardware and software components which can take debris in near real-time from a machine oil sump and process it for features diagnosis. This processing provides the information of the basic features on the user screen which can further be used for machine component health diagnosis.

The developed system has the capacity to replace the existing off-line methods due to its cost effectiveness and simplicity in operation. The system is able to analyze debris basic quantitative and qualitative features greater than 50 micron and less than 300 micron.

Wear debris basic features analysis tool is developed and discussed. The portable and near real-time analysis offered by the discussed work can be more technically effective as compared to the existing off-line and online techniques.

This study aims to understand the multiaxial fretting fatigue, wear and fracture characteristics of 35CrMoA steel under the elliptical loading path.

By keeping the contact pressure and torsional shear cyclic stress amplitude unchanged; the axial cyclic stress amplitude varied from 650 MPa to 850 MPa. The fretting fatigue test was carried out on MTS809 testing machine, and the axial cyclic strain response and fatigue life of the material were analyzed. The fretting zone and fracture surface morphology were observed by scanning electron microscope. The composition of wear debris was detected by energy dispersive X-ray spectrometer.

In this study, with the increase of axial stress amplitude, 35CrMoA steel will be continuously softened, and the cyclic softening degree increases. The fretting fatigue life decreases unevenly. The fretting scars in the stick region are elongated in the axial direction. The area of fracture crack propagation zone decreases. In addition, the results indicate that wear debris in the slip region is spherical and has higher oxygen content.

There were few literatures about the multiaxial fretting fatigue behavior of 35CrMoA steel, and most scholars focused on the contact pressure. This paper reveals the effect of axial cyclic stress on fretting fatigue and wear of 35CrMoA steel under the elliptical loading path.

The probability distribution of major length and aspect ratio (major length/minor length) of wear debris collected from gear oil used in planetary gear drive were analysed and modelled. The paper aims to find an appropriate probability distribution model to forecast the kind of wear particles at different running hour of the machine.

Used gear oil of the planetary gear box of a slab caster was drained out and charged with a fresh oil of grade (EP-460). Six chronological oil samples were collected at different time interval between 480 and 1,992 h of machine running. The oil samples were filtered to separate wear particles, and microscopic study of wear debris was carried out at 100X magnification. Statistical modelling of wear debris distribution was done using Weibull and exponential probability distribution model. A comparison was studied among actual, Weibull and exponential probability distribution of major length and aspect ratio of wear particles.

Distribution of major length of wear particle was found to be closer to the exponential probability density function, whereas Weibull probability density function fitted better to distribution of aspect ratio of wear particle.

The potential of the developed model can be used to analyse the distribution of major length and aspect ratio of wear debris present in planetary gear box of slab caster machine.