1998. Automatic Wear Particle Classification Using Neural Networks, Z. Peng and T.B. Kirk

8/6/2019 1998. Automatic Wear Particle Classification Using Neural Networks, Z. Peng and T.B. Kirk

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Tribology Letters 5 (1998) 249257 249

Automatic wear-particle classification using neural networks

Z. Peng and T.B. Kirk

Department of Mechanical and Materials Engineering, The University of Western Australia, Nedlands, WA 6907, Australia

Received 28 February 1998; accepted 27 June 1998

Although the study of wear debris can yield much information on the wear processes operating in machinery, the method has not been

widely applied in industry. The main reason is that the technique is currently time consuming and costly due to the lack of automatic

wear particle analysis and identification techniques. In this paper, six common types of metallic wear particles have been investigated

by studying three-dimensional images obtained from laser scanning confocal microscopy. Using selected numerical parameters, which

can characterise boundary morphology and surface topology of the wear particles, two neural network systems, i.e., a fuzzy Kohonen

neural network and a multi-layer perceptron with backpropagation learning rule, have been trained to classify the wear particles. The

study has shown that neural networks have the potential for dealing with classification tasks and can perform wear-particle classification

satisfactorily.

Keywords: wear-particle analysis, wear-debris classification, machine condition monitoring, fuzzy Kohonen neural network, multi-layer

perceptron

1. Introduction

Wear-particle analysis has been recognised as one of the

most effective means of machine condition monitoring be-

cause the morphology of wear debris is closely related to

the condition of loading, lubrication and wear processes in-

volving in a system. The technique, however, is still greatly

limited in industrial applications due to certain unresolved

issues associated with the study of wear particles. The main

obstacles in the study are: (1) the difficulty to obtain suit-

able images of wear particles for analysis, and the lack of

effective parameters to describe the proper features of wear

debris; (2) the lack of automatic wear-particle classification

systems, which can perform wear-particle classification ef-

ficiently and automatically, for industrial applications.

Wear particles are actually three-dimensional (3D) ob-

jects, containing both boundary morphology (such as size,

size distribution and the features of boundary profiles) and

surface topology (e.g., surface roughness and texture char-acterisations). To describe the characteristics of wear debris

effectively, it is first necessary to obtain reliable images of

wear particles, and then, to develop effective descriptors to

characterise the appropriate features of those images [2].

So far, few imaging systems can provide three-dimensional

structures of objects directly. Laser scanning confocal mi-

croscopy (LSCM) is a rapidly developing system having

the ability to acquire three-dimensional images with an ade-

quate resolution. In this study, it has been further developed

and used to scan the images of metallic wear particles [3,4].

It has been verified that both the boundary morphology and

surface topography of wear particles can be examined byanalysing the images obtained from the LSCM [35].

The next step in the study of wear debris is to describe

the features of the boundary and surface topology of wear

particles reliably and effectively. As a result of the limita-

tions of most commonly used imaging techniques, wear de-

bris have been mainly studied in two dimensions (2D) [8,9].

It has been found that 2D wear-particle analysis may iden-

tify some types of wear particles, such as particles which

have well-defined boundary profiles. However, it is diffi-

cult to separate laminar, fatigue-chunk and severe-sliding-

wear debris from each other because the major differences

between them are apparent in their surface topology. To

study wear debris thoroughly, both boundary morphology

and surface topology have been investigated in this study.

Furthermore, the appropriate parameters have been devel-

oped [5,10]. All these efforts aim to build the foundation

for developing an automatic wear-particle classification sys-

tem.

The development of automatic wear-particle classifica-

tion systems is another important step in the study of wear

particles, especially for identifying wear particles. Ap-

plication of such systems should significantly reduce theinspection time and the requirements for the inspectors

expertise [11,12]. Nowadays, neural networks have been

identified as a promising intellectual technique for solving

a large range of complex problems [13,14]. Meanwhile,

the pattern recognition area is a commonly studied topic in

neural networks. Therefore, many different architectures of

neural networks are available to handle this task with the

recent advances in learning techniques of neural networks.

In this study, two widely used neural networks have been

applied in classifying wear particles. A Kohonen neural

network [15,16] with fuzzy logic [17] has been first chosen

and used to classify wear particles because the Kohonenself-organising algorithms have a close relationship to the

field of pattern recognition known as cluster analysis. As

the classification task is not exactly the same as the cluster

J.C. Baltzer AG, Science Publishers


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250 Z. Peng, T.B. Kirk / Automatic wear-particle classification

analysis, which is normally an unsupervised process, the

used Kohonen neural networks do have certain drawbacks

when solving classification problems. Thus, the multi-layer

perceptron with learning capability [1820] has also beentrained to perform the classification task for comparison.

The details of training, testing and comparing these two

neural networks are presented in the following sections.

2. The three-dimensional study of metallic wear debris

2.1. Six types of metallic wear debris

The main issue in wear debris analysis is to thoroughly

describe the characteristics of the shape, size, surface

roughness and texture of wear debris using a few numerical

features. Before conducting the image analysis, obtainingreliable images of wear particles for both boundary and

surface study is always crucial for the whole procedure.

To obtain wear particles for the study, gearbox and bear-

ing tests [4] have been conducted to produce five common

types of wear debris, i.e, rubbing, cutting, laminar, fatigue-

chunk and severe-sliding-wear particles. A total of six of

the most common types of wear debris, which include the

above five types and spherical particles1, will be studied in

this paper. The reason why these six types of wear particles

have been chosen for this study is because they have been

widely accepted as the most common types of wear parti-

cles [6]. Meanwhile, the presence of these wear particles

in lubricants usually has a direct relationship with the wear

modes and machine condition.

Rubbing particles have been recognised as normal wear

debris generated from a normal sliding-wear process. These

thin and small flakes are often the broken parts of a unique

layer formed at a smooth slowly wearing surface. They

have similar sharp profiles to laminar particles, but in much

smaller sizes. Fatigue chunk particles are believed to re-

sult from the high stresses imposed by repeatedly loading

and unloading. They are formed when the material is re-

moved as a pit or spall opened up with the growth of cracks.

Fatigue-chunk debris usually have a smooth surface on the

loading side and rough surfaces in the three perpendicu-lar dimensions [6]. Severe sliding particles are associated

with excessive wear and surface stresses due to load and/or

speed. The parallel grooves or scratches on their surfaces

and straight edges make them identifiable. Cutting parti-

cles are usually as a result of one hard surface penetrating

another softer surface. There are two ways to generate

cutting particles. One is that a hard component becomes

misaligned, resulting in a sharp edge ploughing a softer

surface. The other source is the three-body abrasion wear

process, which means there are hard abrasive particles in

the lubrication system. Spherical wear particles are usually

generated in the bearing fatigue cracks from rolling bear-

ing fatigue. These particles resemble small balls. Rolling-

1 Note: since it is not easy to generate spherical particles from the test,

man-made spheres are used in this study.

bearing fatigue is not the only source of spherical parti-

cles [1]. It is believed that cavitation erosion, welding or

grinding processes generate spherical wear particles as well.

It is also found that these particles are generated from se-vere friction, and are associated with high temperatures.

In short, different types of wear particles have their distin-

guishable generation mechanisms. Accordingly, identifying

individual wear particles can help analysts and engineers to

understand the wear mode, and furthermore, to examine the

condition of a machine.

After the wear particles are separated from the oil sam-

ples using the filtergram method [7], a laser scanning confo-

cal microscope (LSCM) with a transmission sensor [4] has

been applied to acquiring 3D images of the wear debris.

Consequently, the wear particles have been studied and at-

tempted to classify into six types, i.e., rubbing, cutting,

spherical, laminar, fatigue-chunk and severe-sliding-wearparticles.

2.2. The study of boundary morphology

Based on the features of different types of wear parti-

cles, boundary morphology can be categorised according

to the attributes of outline shapes, edge details and size

distributions. Normally, the area and length in the major

dimension are parameters to describe size distributions of

wear debris; while roundness and the fibre ratio [35] (see

the definitions in the note of table 1) can characterise the

features of boundary profiles such as regular or irregularand circular or elongated outlines. Using the above numer-

ical parameters, cutting debris and spherical particles can

be distinguished from other types of wear particles by their

well-defined boundary characterisations. Rubbing debris

are usually a platelet with a smooth surface, and resemble

small-scaled laminar particles. By studying the different

size distributions, this kind of wear particle can be sepa-

rated from laminar, fatigue-chunk and severe-sliding-wear

particles, as rubbing wear particles generally have a smaller

area and size in major dimension than do the others. Rub-

bing particles can also be identified from cutting particles

and spherical wear particles in the image analysis by com-

paring the values of the fibre ratio and roundness, respec-tively. Thus, the used boundary parameters can separate

rubbing, spherical and cutting particles from other types of

debris.

The identification of laminar, fatigue-chunk and severe-

sliding-wear particles is usually more complicated than that

of rubbing, cutting and spherical particles because the for-

mer three debris may have various boundary profiles. In or-

der to identify those particles, the height aspect ratio (HAR:

major length to thickness) [4,5] has been chosen to describe

the volume contours of wear particles in three dimensions.

Fatigue-chunk debris generated from fatigue spalls, pitting

and breaking up from rolling contact fatigue, usually havelarger values of HAR than do laminar particles. HAR,

however, is difficult to assess accurately and cannot always

identify fatigue-chunk debris from laminar particles due to


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Z. Peng, T.B. Kirk / Automatic wear-particle classification 251

Table 1

Analysis results of wear particles.

Parameter a Types of wear particles

Rubbing Cutting Spherical L aminar Fatigue chunk Severe sliding

Area (m2) 38 230 659 2129 895 652Length (m) 9 40 14 58 39 41Roundness 0.52 0.13 0.89 0.63 0.53 0.46

Fibre ratio 1.46 13.53 0.84 3.18 2.48 2.97

Fractal dimension 1.095 1.045 1.038

HAR 0.103 0.332 0.174

Ra (m) 0.819 1.405 0.809Rq (m) 1.024 1.756 1.0112 0.535 0.556 0.265

a Area: area measurements of wear particles.

Length: length in the major dimension.

Roundness (4(area)/(length)2 ): it is sensitive to the elongation of a boundary profile. The roundness is equalto 1 for a circle and is less for any other shape.

Fibre ratio: it is approximately equal to the length of a fibre along its axis divided by its width. Fibre ratio isaspect ratio (length/width) if the center point is in the contour of a image.

Fractal dimension: numerical parameter used for characterisation of a boundary profile or curve. D = 1 dx/dy, where dx/dy is the gradient of a double log plot of dilation radius and perimeter.HAR (height aspect ratio): the height of a particle compared to its maximal planar dimension.

Ra: average roughness of a surface, Ra =1n

n1 |zi|, where n is the number of sampling points and Z is the

residual surface.

Rq: root mean square ofRa, Rq = (1n

n1 z

2i )

1/2.

2: the spectral moment analysis is a quantitative descriptor which can indicate the texture pattern.

the deformation of wear particles and different particle sur-

face orientations toward the microscope.

The boundary fractal dimension [5], therefore, is applied

to studying those wear particles because it can assess the

ruggedness of the edge features of wear particles. Although

the boundary fractal dimension can be used to assess the

general features of the different boundary morphologies of

the above particles, there is still overlap between the mea-

surements for laminar, fatigue-chunk and severe-sliding de-

bris. This is because individual particles may vary in profile

even though they belong to the same type, and also because

noise can affect the effectiveness of the boundary fractal di-

mension. Studies [4,5] have verified that analysing laminar,

fatigue-chunk and severe-sliding debris by studying only

the features of their boundary morphology is insufficient to

adequately classify them.

2.3. The study of surface topology

Surface analysis, which includes the study of surface

roughnesses and textures, is another important component

part in wear-particle analysis. Ra and Rq [35], whichare the arithmetic mean height and the root-mean-square

value of surface departure within the sampling area, respec-

tively, are commonly used surface parameters to describe

the height and amplitude distributions of wear particle sur-

faces. These two descriptors may identify smooth surfaces

from rough ones, but Ra and Rq only measure the aver-

age deviation of a surface profile in the vertical direction.They are not scale-independent parameters, which means

the measurement does rely on the scale length. Moreover,

Ra and Rq do not carry any information about slopes, sizes

of asperities and frequencies of their occurrence, so widely

different surfaces can give similar values of Ra and Rq.Since the conventional parameters cannot describe the fea-

tures of striations on a surface, other parameters have to be

developed to study surface textures of wear particles. This

is important because the surface texture is an also very im-

portant characteristic of wear particles, and it is crucial to

identify severe sliding particles from other types of debris.

It has been shown that two-dimensional fast Fourier

transform [21] and co-occurrence matrix [22] are effec-

tive methods for investigating surface patterns, i.e., isotropy

and anisotropy. The two-dimensional fast Fourier trans-

form, power spectrum and angular spectrum have been ap-

plied to studying surface textures of wear debris in this

series of studies [5,10]. The previous investigations have

shown that the techniques are able to separate isotropy from

anisotropy, and moreover, can describe different severitiesof anisotropic surfaces consistently with visual perception.

Spectral moment analysis (2) [21] is a quantitative de-scriptor which can indicate the texture pattern. A previous

study [5] has verified that 2 is effective in describing dif-ferent surface textures of wear particles.

2.4. The combination features of wear particles

Wear particles are three-dimensional objects, so neither

the study of boundary morphology nor the analysis of sur-

face topography can fully describe the features of six types

of metallic wear debris. The identification should rely onboth boundary and surface analysis. Table 1 shows the

criteria for characterising those wear particles and the av-

erage values of the parameters. The figures displayed in


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this table are the average values of all descriptors extracted

from more than 50 wear particles of each type. The de-

scriptors and the values are also the centroids of six types

of wear debris, which will be used as references to trainneural networks.

3. Neural networks for wear-particle classification

Although conventional computers possess tremendous

performance capabilities in many areas, they are subject

to deficiencies when they come to the tasks which humans

are able to deal with effortlessly perception and learn-

ing. To make computers perform similar tasks to human

beings, neural networks have been developed to emulate

brain cell complexes in order to attain a viable simula-

tion of complex human thought processes. Neural net-works [13,14] are comprised of many simple computing

elements and are models of the brains functional processes.

Pattern recognition and classification are common issues in

daily life, which usually require inspection by experts, and

experience. The wear-particle classification process is such

an example. Normally, it is a time-consuming procedure

as regards the requirement of considerable experience in

analysing the particle morphology, and accordingly, decid-

ing which type(s) it belongs to. Presently, with the rapid

development of neural networks, it has been known that

neural networks, such as Kohonen neural networks and the

multi-layer perceptron with the learning rule, can cope with

these kinds of real world problems satisfactorily.

3.1. Fuzzy Kohonen neural networks

Kohonen clustering networks (KCNs) belong to the most

widely used unsupervised schemes which can find the best

set of weights for hard clusters and classes in an itera-

tive and sequential manner. The structure of KCN consists

of two layers, i.e., input and output layer, shown in fig-

ure 1. Here, X = {x1, x2, . . . , xn} are the input featuresof unknown particles. Each output node has a prototype

or weight vector vi,t. The weight vector is adjusted during

learning according to the following update rule:

vi,t = vi,t + ik,t (xk vi,t1),

where is learning rate. Given a set of input vectors, theneurones in the output layer compete among themselves.

Consequentially, the winner, whose weight has the min-

imum distance from the input, updates its weights. The

process stops when all weight vectors stabilise.

Kohonen neural networks have several problems when

they are used to deal with classification tasks. The major

problem is that KCNs are heuristic procedures, and there-

fore, the termination is not based on optimising any model

of the process or its data. To remedy this, fuzzy logichas been introduced in KCN. It is believed that the inte-

gration of fuzzy c-means algorithms (FCM) and KCN is

one solution to address this drawback. In addition, KCN

Figure 1. Architecture of a fuzzy Kohonen neural network.

clustering is closely related to FCM, and FCM algorithms

are optimisation procedures [17]. Bezdek and Tsao [15]

have extended this idea to a new investigation called fuzzy

Kohonen clustering network (FKCN). The algorithm of the

FKCN is exhibited below [17]:

(1) Fix the number of types of classes c, || ||A and > 0some small positive constant.

(2) Initialise v0 = (v1,0, v2,0, . . . , vc,0) Rcp. Choose m0 >

1 and tmax = iterate limit.(3) For t = 1,2, . . . , tmax:

(a) compute

uik =

||xk vi||A/||xk vj ||A2/(m1)1

,

vi =

(uik)mxk/

(uik)

m,

ik,t = (uik)mt , mt = (m0 1)/tmax;

(b) update all (c) weight vectors {vi,t} with

vi,t = vi,t +

n

k=1

ik,t(xk vi,t1)

ns=1

is,t;

(c) compute Et = ||vt vt1||2 =

i ||vi,t vi,t1||2;

(d) if Et stop; else next t.The procedure is divided into training, labelling and clas-

sification when a FKCN is applied to perform wear-particle

classification. In the first stage, the network is trained by

using more than 150 examples which distribute evenly in

all classes. The neurones are then assigned to the names

of classes via the labelling process. After completion ofthe labelling process, the classification phase has been per-

formed on around 200 new particles. The accuracy rate

of the fuzzy Kohonen neural network for six types of the

particles is displayed in table 2 with the configuration of

the FKCN shown in table 3.

It can be seen from table 2 that the classification result

of the FKCN has a relatively high error rate, especially for

rubbing, spherical and fatigue-chunk particles. The main

reason is due to the unsupervised algorithm of the network.

Since some wear particles are already known as certain

types, they are actually good examples to train neural net-

works. For this reason, a supervised neural network namedas the multi-layer perceptron with backpropagation learning

rule has been used to perform the classification task in this

study.


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Table 2

Classification results in the neural networks.

Types of particles

Rubbing Spherical Cutting Laminar Fatigue chunk Severe sliding Average

FKCN (% accuracy) 62% 56% 93% 83% 60% 97% 75%

MLP (% accuracy) 99% 100% 98% 93% 92% 89% 95%

Table 3

The configuration of the FKCN.

Neurons of Neurons of Number of Exponent Exponent Training Convergence

the input layer the output layer features step initialization threshold

40 6 9 2 0.02 Random matrix 0.001

3.2. Multi-layer perceptron neural networks

Multi-layer perceptron (MLP) with backpropagation

learning rule is an entirely supervised neural network. The

multi-layer perceptron structure has provided a potential al-

ternative not only to traditional pattern recognition systems,

but also to common non-linear adaptive filters [19]. A MLP

is a widely used network due to three different aspects. The

first is that it has the ability to implement Boolean logic

functions. The second potential is the ability to partition

the pattern space for classification problems, and the last

one is the ability to implement non-linear transformations

for functional approximation problems [20]. Here, a MLP

is used to perform an automatic classification task for wearparticles.

The MLP, shown in figure 2, usually consists of simple,

non-linear, and processing units arranged in several layers.

The first layer (input layer) receives signals and propagates

them through the network to the last layer, which is also

denoted as the output layer. The individual perceptrons in

the network are called neurones or nodes. The connection

between different layers relies on nodes. Ordinarily, the

number of neurones in the input layer equals to the number

of parameters which are used to describe the characteristics

of wear debris. In this study, nine neurones are set for the

input layer. The multiple nodes in the output layer typically

correspond to multiple classes for the multi-class pattern

recognition problem. So the output layer (last layer) has a

total of six neurones for the six types of wear debris. The

hidden layer nodes help the MLP to solve the classification

problem by combining simple functional units.

A MLP is usually applied in three phases. In the first

phase, the network is trained by learning from examples,

which are employed to adapt the connection weights be-

tween the neurones. As it is a supervised learning process,

the examples must contain the desired results. The train-

ing is achieved by mapping the effect of the weights ontothe error signal. As a result of the training procedure, the

global sum of the error between the actual output and the

desired output is minimised over the entire training set and

Figure 2. Architecture of a typical multi-layer perceptron.

all the output nodes. If the error energy is defined as [18]:

E=

1

N

Ni=1

( actual output desired output )2

1/2,

where N = number of output neurones. The algorithmupdates the weight at each training cycle by the following

formula:

W= W+ W,

where W is a weight of a neuron input, and W =

E/W, is the learning rate. The training processstops when the error energy E is small enough. Follow-ing the training process is the test, which is to check if

the trained network meets the prevailing requirements. If it

does, the network can be applied for the actual classification

tasks (the third phase).

In this study, in order to decide upon the configuration

of a neural network for wear-particle classification, whose

features are characterised by nine criteria (see table 1), one

and two hidden layers with different number of neurones

for each layer have been tried. After experimentation, the

two-hidden-layer MLP with momentum learning method

[1820] has been chosen for this study. The learning rateis 0.001 in the network, and the transfer functions are lin-

ear, taugh, sigmoid and sigmoid for the first layer, the first

and second hidden layer and the output layer, respectively.


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Table 4

The configurations of the MLP.

Layers Input Hidden1 Hidden2 Ouput

Number of neurons 9 6 10 6

Transfer function Linear Taugh Sigmoid Sigmoid

Training data Format of data Scaled to [01]

Presentation order Random

Learning rate 0.001 0.9 1.0

Learning parameters Momentum 0.001 0.9 1.0

Decay rate 0.001 0.9 1.0

Learning method Backprop with momentum

Learning strategy Single step (delta)

RMS training error Less than 0.001

No. epochs Dividable by 10000

Table 5

Analysis results of the wear particles (shown in figure 3).

Parameter Wear particles (figure 3)

(a) (b) (c1) (d) (e) (f)

Area (m2) 38.37 83.92 616.68 268.96 669.21 439.93Length (m) 9.61 27.86 14.57 25.23 37.55 39.48Roundness 0.53 0.14 0.93 0.54 0.54 0.36

Fibre ratio 1.65 17.09 1.32 0.99 2.40 2.64

Fractal dimension 1.121 1.091 1.078 1.033 1.143 1.024

HAR 0.207 0.181 0.371 0.141 0.375

Ra (m) 0.444 0.620 * 1.411 0.747 1.694Rq (m) 0.535 0.778 1.764 0.946 2.2102 0.623 0.367 0.627 0.702 0.253

* The surface analysis of the sphere is voided because of the difficulty to acquire information on the

surface.

Because the parameters for describing wear particles are

actually multi-dimensional features, all features have been

scaled to the range [01] before the training, testing and

classification. The details of the optimal configuration are

shown in table 4.

More than 200 examples have been used to train the

MLP with almost uniform distribution for six types. The

final accuracy rate of the classification is shown in table 2.

It is shown, from table 2, that the accuracy rate of the

MLP is much higher than that of the fuzzy Kohonen neural

network. This is due to the different algorithms of thosetwo kinds of neural networks. It has been verified that the

MLP, which has a supervised learning algorithm, is more

flexible in learning from examples. Therefore, it appears to

be more suitable to perform the classification task for wear

particles.

4. Classification examples

Figures 3 (a)(f) show LSCM images of six wear parti-

cles generated from the gearbox and bearing tests. Those

particles are represented as test examples for both the fuzzy

Kohonen neural network and the multi-layer perceptron.The boundary morphology analyses have been performed

on the particle boundaries provided by the maximum bright-

ness images, while surface analyses have been conducted

on the height-encoded images of those particles [5] (which

are not demonstrated in this paper). The brief definitions of

the maximum brightness image and the height-encoded im-

age are as follows: the maximum brightness image records

the maximum pixel value for each xy location from all theimages in the stack; the height-encoded image contains the

information on the image level which the maximum pixel

comes from. It is necessary to clarify that the images dis-

played in figure 3(c) are only the profiles of spheres due to

the difficulty to obtain completely surface information from

this kind of particle. The analysis results of figures 3(a)(f)are shown in table 5.

After the FKCN and the MLP were trained and tested,

the above six particles have been classified using the FKCN

and the MLP individually. The classification results of

those neural networks are presented in tables 6 and 7, re-

spectively.

The classification results from both the FKCN and the

MLP show that figures 3(a), (b) and (c1) are rubbing, cut-

ting and spherical particles respectively, this being consis-

tent with human inspection. Comparing the memberships

in tables 6 and 7, it is evident that the MLP has output

higher memberships for figure 3(a) to the type of rubbingparticles and figure 3(c1) to the spherical debris than do

the FKCN. As figures 3(a) and (c1) are actually a typi-

cal rubbing particle and spherical particle, respectively, the


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(a) (b)

(c) (d)

(e) (f)

Figure 3. Images of wear particles obtained from laser scanning confocal microscopy for neural network classification test. Note: figure 3(c) are not

particles generated from normal wear process, but it can show the same characteristics of spherical particles.


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Table 6

Classification results in the fuzzy Kohonen neural network.

Wear particle Membership of classification

(figure 3)

Rubbing Cutting Spherical Fatigue chunk Laminar Severe sliding

(a) 0.432 0.018 0.159 0.355 0.031 0.001(b) 0.000 1.000 0.000 0.000 0.000 0.000

(c1) 0.212 0.038 0.372 0.204 0.121 0.050(d) 0.016 0.000 0.000 0.981 0.000 0.021(e) 0.662 0.035 0.004 0.078 0.221 0.000(f) 0.000 0.000 0.000 0.000 0.000 1.000

Table 7

Classification results in the multi-layer perceptron.

Wear particle Membership of classification

(figure 3)Rubbing Cutting Spherical Fatigue chunk Laminar Severe sliding

(a) 0.995 0.001 0.000 0.001 0.009 0.000(b) 0.002 0.989 0.000 0.000 0.001 0.003

(c1) 0.000 0.000 0.992 0.001 0.001 0.000(d) 0.008 0.000 0.004 0.986 0.006 0.021(e) 0.007 0.010 0.001 0.065 0.843 0.002(f) 0.001 0.002 0.000 0.020 0.000 0.958

classification result of the multi-layer perceptron is more re-

liable and reasonable for these two test particles. Both the

FKCN and the MLP have classified figure 3(d) as a fatigue-

chunk particle with the membership being greater than 0.9.

For the particle shown in figure 3(e), the classification re-

sult of the FKCN is a rubbing particle, while the MLP has

classified it as a laminar particle. Since laminar particles

have similar boundary morphology and surface topography

to that of rubbing wear debris but in the different range of

size, the size features in table 5 have demonstrated that the

particle in figure 3(e) is a laminar particle. So the MLP has

given a correct classification of figure 3(e). The last exam-

ple displayed in figure 3(f) is a particle with scratches on

the surface. The FKCN and the MLP identify this particle

as a severe-sliding-wear particle concurrently.

Studying the six examples in figure 3, it has been indi-

cated that the multi-layer perceptron can handle the wear-

particle classification task satisfactorily. Moreover, the per-

formance of the MLP is much better than that of the FKCNin wear-particle identification. The test has verified that the

trained MLP is ready for performing wear-particle classifi-

cation tasks.

5. Discussion

The study has further confirmed that three-dimensional

image analysis of wear particles is necessary for examin-

ing the distinguishable characteristics and identifying the

six types of wear debris. Comparing the average values of

the six types of wear particles in table 1 and six examples

in table 5, it is clear that studying boundary morphologyis sufficient to distinguish rubbing, cutting and spherical

particles. Since laminar, fatigue-chunk and severe-sliding-

wear particles may have similar size distributions, and their

shape features are not so distinguishable, it is not easy to

separate these wear debris by analysing only their shape

and size features. It has been verified in the series stud-

ies that three-dimensional analysis, combining the study of

boundary morphology and surface topology, is an effective

and reliable method for investigating these wear debris.This paper is the investigation of developing an auto-

matic wear particle analysis and classification system. To

fully develop such a system for industrial applications, more

work needs to be done. It may include the development

of an automatic image acquiring system, which can per-

form 3D imaging without or with far less human involve-

ment. In addition, more information, related to the issue

of machine condition monitoring, needs to be added in the

system. They may include the corresponding wear mecha-

nisms, possible machine condition related to the identified

types of wear debris, and the suggested maintenance proce-

dures based on the detected condition of a machine. As a

result, the detected machine condition and possible mainte-nance suggestions can be output as references for engineers.

Besides these, other types of wear particles may need to be

studied and added to the system if it is necessary.

6. Conclusions

Two widely used neural networks, i.e., the fuzzy Ko-

honen neural network and the multi-layer perceptron with

backpropagation learning rule, have been explored to clas-

sify six types of wear particles using numerical descriptors.

This study has shown that both of those neural networks cangenerally capture the characteristics of those wear particles

once trained using the given examples. Thereafter, they can

perform automatic wear-particle classification tasks. Since


9/9


the multi-layer perceptron has ability to learn from exam-

ples flexibly in a supervised manner, the performance ac-

curacy is much higher than the fuzzy Konohen neural net-

work. It has been verified in this investigation that the

MLP is potential of classifying wear particles after the

suitable configurations of the neural network are decided,

and enough and reliable learning examples are presented

to the training process. This study is a further investi-

gation of developing automatic wear-particle classification

techniques using neural networks for machine condition

monitoring. Meanwhile, the research has demonstrated the

neural network classification system can produce reason-

able results for wear-debris identification.

Acknowledgement

The authors would like to acknowledge the contribution

of Mr. Franz Detro from Management Intelligenter Tech-

nologien GmbH, Germany for his valuable discussion and

suggestion on deciding the suitable configurations of neural

networks for wear-particle classification.

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Documents

1998. Automatic Wear Particle Classification Using Neural Networks, Z. Peng and T.B. Kirk