Edge Detail Analysis of Wear Particles

Mohammad Shakeel Laghari

, Ahmed Hassan

and Mubashir Noman

College of Engineering, United Arab Emirates University, Al Ain, U.A.E.

Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, U.A.E.

Keywords: Contour Points Angle, Edge Details Analysis, Tribology, Wear Debris Classification.

Abstract: Tribology is the study of wear particles that are generated in all machines with interacting mechanical parts.

Particles are separated from the surfaces due to friction and relative motion. These microscopic particles vary

in certain characteristics of size, quantity, composition, and morphology. Wear particles or wear debris are

categorized by six morphological attributes of shape, edge details, texture, color, size, and thickness ratio.

Particles can be identified with the help of some or all of these attributes however, only edge details analysis

is considered in this paper. The objective is to classify these particles in a coherent way based on these

attributes and by using the acquired knowledge to predict wear failure modes in machinery. There are two

procedures described in this work; one is the angle calculation between equidistance points on the particle

boundary and the other the computation of centroids’ distance from the boundary points. These procedures

will classify particle edges as smooth, rough, straight, or spherical (curved).

1 INTRODUCTION

An important area of image processing and computer

vision deals with on-line or off-line visual inspection

systems that can assist the industry to improve the

economy of the operation, quality, and productivity

of the manufacturing machinery. Microscopic wear

particle analysis is included in such industrial

inspection systems. The particles that originate from

the surfaces of interacting mechanical parts are

accumulated in lubricating oil that carries necessary

information and knowledge regarding the physical

condition of the machinery (typically referred to as

condition monitoring). This acquired critical

knowledge is utilized by Tribologists to identify

known wear mechanisms that can anticipate wearing

failure modes in machines (Hunter, 1975, Xu, 1998,

Peng, 2001).

Analysts examine particles in a conventional way,

which includes particle quantity, size, and

composition. These three parameters are used to link

specific particle types to known wear modes and are

typically utilized to predict wear failures. For

example, an increase in particle size and/or quantity

indicates an abnormal behaviour of the machine as

https://orcid.org/0000-0002-4738-1571

https://orcid.org/0000-0002-7513-0243

well as composition indicates the origin of the wear

particle generation. Although the conventional

procedures can provide a fair judgment of a machine

operating condition, however, morphological

analysis is essential to bring consistency in wear

judgments.

Wear particle diagnostic queries when examined

by a number of experts in the field typically result in

conflicting wear judgments due to the privation of an

internationally defined standardization of terms used

to describe wear particles and their relationship to

originating wear processes. This created uncertainty

and difference of opinion among experts of the field

and therefore, an automated and robust analysis

approach is needed to develop a morphological-based

system.

The devised procedure described in this paper is

edge details analysis that allows systematic analysis

of wear debris by using one of the six morphological

attributes. The remaining morphological attributes

are particle size, shape, color, texture, and thickness

ratio. The procedures used for this investigation are

equidistance contour points angle and centroid

distance calculation. Expensive equipment failure

550

Laghari, M., Hassan, A. and Noman, M.

Edge Detail Analysis of Wear Particles.

DOI: 10.5220/0010584105500557

In Proceedings of the 16th International Conference on Software Technologies (ICSOFT 2021), pages 550-557

ISBN: 978-989-758-523-4

and/or loss of profitable production time can be

prevented by using the above-mentioned procedures.

2 WEAR PARTICLES

Tribology is the study of friction between surfaces,

their associated wears, and the lubrication that

contain these wears. Alternatively, it can said that the

field of Tribology is the study of wear particles (Jost,

1990).

Interacting mechanical parts of a machine

produces wear particles due to friction. A large

amount of wear particles is produced when a machine

is brought into operation for the first time. After a

certain initial run period, the production of wear

particles is reduced and reaches a steady state.

Alternately, not reaching a steady state is an

indication of an abnormal wear mechanism.

Several methods are used to separate wear

particles from lubricants to perform off-line

examination and analysis. One of the methods is the

use of various sizes of filters located at particular sites

in a machine. The particles collected from these

different size filters are spread on glass slides from

these filters are deposited on glass slides for auxiliary

analysis.

Ferrography is another method that uses

magnetism to hold and separate wear particles from

the lubricant. Ferro graphic slides or substrates are

prepared by inclining the slides at an angle and the

particle-contained lubricant is flown down the surface

holding the particles onto the slide. The arrangement

of particles on the slide is relative to their sizes (Li,

2017).

An additional method of separating wear particles

is the Magnetic Chip Detectors (MCD). This method

uses small removable units equipped with a powerful

permanent magnet and is located at suitable positions

in the machine. Particles are attracted to the units and

are wiped on a slide (Bowen, 1976, Cumming, 1989).

Wear particles are inspected by two approaches of

quantitative and morphological. Quantitative analysis

is the most common, objective, and fast method of

measurement because only particle size and quantity

are considered. However, the information it provides

is unreliable and may result in uncertainty.

An optical microscope is used to perform off-line

morphological analysis. The information collected

from the six attributes in this analysis can be used to

make reliable wear judgments and predict wear

failure modes. This analysis also helps to identify the

origin of the generated wear.

The particles are classified into several types that

are dependent on the relational between their

compositional and morphological properties and

formation conditions. There are approximately 29

different types of wear particles where each particle

gives a different indication about the machine

operating condition. A few examples of wear

particles are rubbing wear, cutting wear, severe

sliding, wear, fatigue wear, pitting wear, etc.

(Albidewi, 1993, Anderson, 1991).

3 LITERATURE REVIEW

Raadnui presented a survey of wear particles analysis

techniques that are based on certain characteristics

features including shape factors, edge or curvature

details, surface texture, size or quantity, Fourier

parameters, fractal dimension, etc. (Raadnui, 2005).

Laghari investigated the particle profile by using

shape parameters, size, and edge details of the wear

debris (Laghari, 2003). He concluded that shape

parameters combined with edge detail features could

provide clear distinctions between the types of

particles.

Goncalves et al. proposed a system for

segmentation of wear particles from the microscopic

images and performed shape analysis of the particles

to group them according to their size, aspect ratio, and

edge roundness factor (Goncalves, 2008).

Laghari et al. proposed a “knowledge-based wear

particle analysis” system to identify different types of

wear debris by using edge details and surface texture

features (Laghari, 2007). The authors used the Ferret

centric diameter method to determine the

characteristics of the edge details and texture

properties of coarseness, homogeneity, and

periodicity for classification purposes.

Peng et al. proposes a method for segmenting

Ferrography image to analysis oxide wear particles in

intricate images (Peng 2019). A watershed transform

is initially used to segment particle images and then

segmentation results are improved by two region

merging rules. In the final phase, the features

including the edge details are achieved to detect and

analyse the oxide wear particles.

Laghari et al. devised an automated image

analysis system for the classification of wear debris

(Laghari, 2010). The system extracts shape and edge

details of the particles and stores the extracted

information in a database. The system then performs

further analysis to identify different types of wear

debris.

Edge Detail Analysis of Wear Particles

551

Wang et al. investigated an objective evaluation

of wear particle edge detection by using a newly

devised non-reference method (Wang, 2018). The

method describes three components that are put

together for a broad index of edge evaluation. The

three components are the rebuilding based similarity

sub-index between the two images of original and the

remodelled, the indication of the true or false degree

of the edge pixels based on the confidence degree

sub-index, and the determination of the direction

consistency and width uniformity of the edges by

using the edge form sub-index. The authors have

performed two experiments to demonstrate the

validity of the proposed method.

Laghari and Ahmed proposed a system to analyse

the wear particles' edge profile (Laghari, 2009).

Particle profile features were extracted by using the

chain code method and change in boundary angles

was used to analyse the curvature of the boundary.

4 METHODOLOGY

To analyse the edge detail characteristics, wear

particle images are captures from the field of view of

the Leica DMS300 Zoom imaging system supporting

the LAS X (Leica Application Suite X) and 2D

Analysis software. Further analysis on particle

images is performed, by first converting to binary

images using a simple global thresholding algorithm.

The acquired binary image is then filtered based on

the connected component area to retain the largest

connected component. Next, the orientation of the

particle is calculated, and the image is rotated so that

the major axis of the particle is aligned with the

horizontal axis. Then, the morphological opening

operation is performed to remove the noise and thin

protrusions thereby removing small variations in the

particle contours. In the next section, the procedures

of the edge details characteristics are described in

detail.

4.1 Equidistant Contour Points Angles

The contour points of particles are extracted from

stored images in such a manner that a pointer is

moved clockwise on the particle image perimeter

starting from a fixed coordinate which is typically the

top-left coordinate. Three equidistant consecutive

points are selected and the angle between them is

computed as shown in Figure 1. To compute the angle

between three points, vectors AC and AB are

calculated. The angles of α and β for each vector are

then calculated concerning the horizontal axis defined

as ‘x’. The final angle between the vectors is the sum

of α and β.

This process of calculating angle is performed for

each boundary point. The computed angle is

converted to the range from zero to 360 degrees.

Then, a derivative of the angle vector is computed to

analyse sharp changes in the particle contour. The

boundary of the particle does not retain its original

shape when the binary particle image is rotated or

threshold, e.g., it can produce zigzag patterns or

roughness in the particle contour. Therefore, an angle

threshold value of 25 degrees is selected to be

considered as the straight edge. The derivative angle

vector is then threshold and several straight-line

segments are counted. Consequently, the length of

each line segment and the percentage of rough and

straight regions is also calculated. Alternatively,

angles difference above 25 degrees is represented as

peaks.

Figure 1: Angle calculation between three points.

4.2 Centroid & Threshold Centroid

Distance

After computing the angle vector of the contour

points, the centroid of the particle is calculated. The

distance of each point from the centroid to the particle

edge points is calculated and saved into another

vector. To analyse the circular nature of the particle,

the distance vector is the threshold by using multiple

minimum distances i.e., if the difference between the

distance of the nearest point and any other point is less

than a certain value then those points are considered

equidistant from the center point. For a perfectly

circular object, the distance vector and threshold

distance-vector will be the same. To analyse the

straight nature of the particle edge, the centroid

distance will have a linear trend for the straight edge

regions.

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552

Figure 2: Smooth edged particle 1.

5 EXPERIMENTATION &

DISCUSSION

Equidistant contour points angle and centroid

distance vectors perform the analysis of the boundary

points. The analysis of the boundary points is

performed by equidistant contour point angle and by

centroid distance vectors. Contour point angles are

computed by using three consecutive points, and

three equidistant points with distances equal to one,

two, three, four, and five pixels. Moreover,

experiments are also conducted by using strides of

one, two, three, four, and five, respectively.

Figure 3: Smooth edged particle 2.

For example, angles are computed for three

consecutive points with strides of one to five, and the

same procedure is used for equidistant consecutive

points. It was observed that equidistant consecutive

points with distances equal to three, four, and five

give better results in recognizing the edge

characteristics of a particle. Alternatively, the stride

of one is better as it does not reduce the number of

data points and preserves the integrity of the contour.

The analysis of the contour angle indicates that

particles having smooth edges results in having few

abrupt changes in the angle vector as shown in the

first two charts of Figures 2 and 3. The derivative of

the angle vector contains few impulses or peaks as

seen in both charts. The edge points angle show small

Edge Detail Analysis of Wear Particles

553

Figure 4: Rough edged particle 1.

angle changes because of the smoothness of the

edges. Comparison of centroid distances between

both charts is also fairly smooth.

Alternatively, rough edges result in sharp changes

in the angle vector shown in the next two charts of

Figures 4 and 5, respectively. The derivative of the

angle vector contains plenty of impulses or peaks

whereas the centroid distance charts show the obvious

roughness of the edges. The threshold angle

derivative charts of rough edged particles are not that

significant for consideration.

On the other hand, the straight edge regions of the

particles have fewer peaks in the angle vector.

Similarly, for curved particles, there is no abrupt

change in angle and the difference between the angles

Figure 5: Rough edged particle 2.

of consecutive points is small, therefore, a curved

region of the particle also does not contain many

angle impulses.

In the case of centroid distance, the variation

between the distances of boundary points is very

small for spherical or curved edged particles. That is

why the spherical particles have the same value for

the threshold distance vector as shown by a horizontal

line of the fourth chart in Figures 6 and 7,

respectively. The trend of the smooth and straight

edges is linear i.e. it is either increasing or decreasing

linearly. Conversely, rough edges have an irregular

pattern that is certainly due to the serrated nature of

the particle contour.

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Figure 6: Spherical (round edged) particle 1.

It is also possible to classify the spherical particles

based on their centroid distances whereas smooth and

rough particles can be separated by measuring the

abrupt changes in the angle vector.

The difference between large and small straight

edges can be detected utilizing varying linear patterns

in distance and angle vectors. For large straight edges,

the angle vector has large horizontal line segments in

the second charts of Figures 8 and 9 respectively, and

vice-versa. Similarly, particles having large straight

edges have increased or decreasing linear line

segments as shown in the third charts of both Figures.

Figure 7: Spherical (round edged) particle 2.

6 CONCLUSIONS

In this paper, edge detail analysis is performed for the

automated classification of different types of wear

debris. It is concluded that contour or edge details of

the particles provide significant information about the

characteristics of the particles and this information

helps determine the type and severity of the wear

debris. Moreover, edge detail information can be

combined with other morphological attributes to

make a more robust system for wear particles

classification and identification.

Edge Detail Analysis of Wear Particles

555

Figure 8: Straight edged particle 1.

ACKNOWLEDGEMENTS

The authors would like to express their appreciation

to the UAEU Program for Advanced Research

(31N321-UPAR-2-2017) and Faculty of Engineering

at UAEU for their financial support.

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