Abstract

Ferrograph analysis has been adopted over decades for determining the root causes of on-going wear faults. After decades of manual operation, this traditional technique is being driven by intelligent algorithms for automatic identification of wear debris. However, the accuracy and robustness of this algorithm remain marginalized when applied in industries due to various types and color blurry of particles. To address this issue, this paper introduces an automatic ferrograph analysis model with a segmentation method and a two-level classification strategy. In order to obtain wear particles from the color ferrograph image, an adaptive Otsu threshold is adopted in three channel images to solve the color blurry in particle segmentation. By grouping particle parameters into shape and morphology ones, a two-level identification strategy is proposed. The first one is to classify rubbing, cutting, and spherical particles, referring to the fuzzy approach degree of shape parameters. In the second level, the shape-close particles are classified with imperceptible textures and back propagation neural network (BPNN). These objective parameters are constructed by applying the principal component analysis into seven texture features and inputted into a BPNN-based model to classify fatigue and severe sliding particles. In order to train the BPNN, more than 100 ferrograph images are sampled together, whereby standard ferrograph analysis is performed on the particle identification. The performance of the identification exhibits an accuracy exceeding 90% for rubbing, cutting, and spherical particles, whereas about 80% accuracy has been registered for both severe sliding and fatigue particles.

References

1.
Kumar
,
M.
,
Shankar Mukherjee
,
P.
, and
Mohan Misra
,
N.
,
2013
, “
Advancement and Current Status of Wear Debris Analysis for Machine Condition Monitoring: A Review
,”
Ind. Lubrication Tribol.
,
65
(
1
), pp.
3
11
. 10.1108/00368791311292756
2.
Wang
,
S.
,
Wu
,
T.
,
Yang
,
L.
,
Kwok
,
N.
, and
Sarkodie-Gyan
,
T.
,
2019
, “
Three-Dimensional Reconstruction of Wear Particle Surface Based on Photometric Stereo
,”
Measurement
,
133
, pp.
350
360
. 10.1016/j.measurement.2018.10.032
3.
Peng
,
Y.
,
Cai
,
J.
,
Wu
,
T.
,
Cao
,
G.
,
Kwok
,
N.
,
Zhou
,
S.
, and
Peng
,
Z.
,
2019
, “
Online Wear Characterisation of Rolling Element Bearing Using Wear Particle Morphological Features
,”
Wear
,
430
, pp.
369
375
. 10.1016/j.wear.2019.05.005
4.
Peng
,
Y.
,
Cai
,
J.
,
Wu
,
T.
,
Cao
,
G.
,
Kwok
,
N.
,
Zhou
,
S.
, and
Peng
,
Z.
,
2019
, “
A Hybrid Convolutional Neural Network for Intelligent Wear Particle Classification
,”
Tribol. Int.
,
138
, pp.
166
173
. 10.1016/j.triboint.2019.05.029
5.
Stachowiak
,
G.
, and
Podsiadlo
,
P.
,
2006
, “
Towards the Development of An Automated Wear Particle Classification System
,”
Tribol. Int.
,
39
(
12
), pp.
1615
1623
. 10.1016/j.triboint.2006.01.019
6.
Feng
,
S.
,
Qiu
,
G.
,
Luo
,
J.
,
Han
,
L.
,
Mao
,
J.
, and
Zhang
,
Y.
,
2019
, “
A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography
,”
Sensors
,
19
(
3
), p.
723
. 10.3390/s19030723
7.
Wang
,
J. Q.
,
Yao
,
P. P.
,
Liu
,
W. L.
, and
Wang
,
X. L.
,
2016
, “
A Hybrid Method for the Segmentation of a Ferrograph Image Using Marker-Controlled Watershed and Grey Clustering
,”
Tribol. Trans.
,
59
(
3
), pp.
513
521
.
8.
Ma
,
M.
, and
Zhao
,
L.
,
2014
, “
Research on Image Processing Technology for Online Oil Monitoring System
,”
Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST)
,
New York
,
Oct. 14–17
.
9.
Wang
,
J.
,
Zhang
,
L.
,
Lu
,
F.
, and
Wang
,
X.
,
2014
, “
The Segmentation of Wear Particles in Ferrograph Images Based on an Improved Ant Colony Algorithm
,”
Wear
,
311
(
1–2
), pp.
123
129
. 10.1016/j.wear.2014.01.004
10.
Memon
,
Q. A.
, and
Laghari
,
M. S.
,
2005
, “
Self Organizing Analysis Platform for Wear Particle
,”
Trans. Eng. Comput. Technol.
,
6
, pp.
122
125
.
11.
Peng
,
P.
, and
Wang
,
J.
,
2019
, “
Wear Particle Classification Considering Particle Overlapping
,”
Wear
,
422
, pp.
119
127
. 10.1016/j.wear.2019.01.060
12.
Wang
,
J.
,
Bi
,
J.
,
Wang
,
L.
, and
Wang
,
X.
,
2018
, “
A Non-Reference Evaluation Method for Edge Detection of Wear Particles in Ferrograph Images
,”
Mech. Syst. Signal Process.
,
100
, pp.
863
876
. 10.1016/j.ymssp.2017.08.014
13.
Yuan
,
W.
,
Chin
,
K.
,
Hua
,
M.
,
Dong
,
G.
, and
Wang
,
C.
,
2016
, “
Shape Classification of Wear Particles by Image Boundary Analysis Using Machine Learning Algorithms
,”
Mech. Syst. Signal Process.
,
72
, pp.
346
358
. 10.1016/j.ymssp.2015.10.013
14.
Wu
,
T.
,
Peng
,
Y.
,
Sheng
,
C.
, and
Wu
,
J.
,
2014
, “
Intelligent Identification of Wear Mechanism Via On-Line Ferrograph Images
,”
Chin. J. Mech. Eng.
,
27
(
2
), pp.
411
417
. 10.3901/CJME.2014.02.411
15.
Wang
,
J.
, and
Wang
,
X.
,
2013
, “
A Wear Particle Identification Method by Combining Principal Component Analysis and Grey Relational Analysis
,”
Wear
,
304
(
1–2
), pp.
96
102
. 10.1016/j.wear.2013.04.021
16.
Xu
,
B.
,
Wen
,
G.
,
Zhang
,
Z.
, and
Chen
,
F.
,
2018
, “
Wear Particle Classification Using Genetic Programming Evolved Features
,”
Lubrication Sci.
,
30
(
5
), pp.
229
246
. 10.1002/ls.1411
17.
Wang
,
S.
,
Wu
,
T.
,
Shao
,
T.
, and
Peng
,
Z.
,
2019
, “
Integrated Model of BP Neural Network and Cnn Algorithm for Automatic Wear Debris Classification
,”
Wear
,
426
, pp.
1761
1770
. 10.1016/j.wear.2018.12.087
18.
Tian
,
Y.
,
Wang
,
J.
,
Peng
,
Z.
, and
Jiang
,
X.
,
2012
, “
A New Approach to Numerical Characterisation of Wear Particle Surfaces in Three-Dimensions for Wear Study
,”
Wear
,
282
, pp.
59
68
. 10.1016/j.wear.2012.02.002
19.
Stachowiak
,
G. P.
,
Stachowiak
,
G. W.
, and
Podsiadlo
,
P.
,
2008
, “
Automated Classification of Wear Particles Based on Their Surface Texture and Shape Features
,”
Tribol. Int.
,
41
(
1
), pp.
34
43
. 10.1016/j.triboint.2007.04.004
20.
Peng
,
Z.
, and
Kirk
,
T.
,
1998
, “
Computer Image Analysis of Wear Particles in Three-dimensions for Machine Condition Monitoring
,”
Wear
,
223
(
1–2
), pp.
157
166
. 10.1016/S0043-1648(98)00280-4
21.
Otsu
,
N.
,
1979
, “
A Threshold Selection Method From Gray-Level Histograms
,”
IEEE Trans. Syst. Man Cybernet.
,
9
(
1
), pp.
62
66
. 10.1109/TSMC.1979.4310076
22.
Peng
,
Y.
,
Wu
,
T.
,
Wang
,
S.
, and
Peng
,
Z.
,
2015
, “
Oxidation Wear Monitoring Based on the Color Extraction of On-Line Wear Debris
,”
Wear
,
332
, pp.
1151
1157
. 10.1016/j.wear.2014.12.047
23.
Tan
,
K. S.
,
Lim
,
W. H.
, and
Isa
,
N. A. M.
,
2013
, “
Novel Initialization Scheme for Fuzzy C-means Algorithm on Color Image Segmentation
,”
Appl. Soft Comput.
,
13
(
4
), pp.
1832
1852
. 10.1016/j.asoc.2012.12.022
24.
Peng
,
Z.
, and
Kirk
,
T.
,
1998
, “
Automatic Wear-Particle Classification Using Neural Networks
,”
Tribol. Lett.
,
5
(
4
), pp.
249
257
. 10.1023/A:1019126732337
25.
Müller
,
T.
,
Pór
,
A.
, and
Sereni
,
J.-S.
,
2008
, “
Lower Bounding the Boundary of a Graph in Terms of Its Maximum Or Minimum Degree
,”
Discrete Math.
,
308
(
24
), pp.
6581
6583
. 10.1016/j.disc.2007.12.038
26.
Bharati
,
M. H.
,
Liu
,
J. J.
, and
MacGregor
,
J. F.
,
2004
, “
Image Texture Analysis: Methods and Comparisons
,”
Chemom. Intell. Lab. Syst.
,
72
(
1
), pp.
57
71
. 10.1016/j.chemolab.2004.02.005
27.
Tamura
,
H.
,
Mori
,
S.
, and
Yamawaki
,
T.
,
1978
, “
Textural Features Corresponding to Visual Perception
,”
IEEE Trans. Syst. Man Cybernet.
,
8
(
6
), pp.
460
473
. 10.1109/TSMC.1978.4309999
28.
Fung
,
C.-P.
, and
Kang
,
P.-C.
,
2005
, “
Multi-Response Optimization in Friction Properties of PBT Composites Using Taguchi Method and Principle Component Analysis
,”
J. Mater. Process. Technol.
,
170
(
3
), pp.
602
610
. 10.1016/j.jmatprotec.2005.06.040
29.
Pan
,
L. K.
,
Wang
,
C. C.
,
Wei
,
S. L.
, and
Sher
,
H. F.
,
2007
, “
Optimizing Multiple Quality Characteristics Via Taguchi Method-Based Grey Analysis
,”
J. Mater. Process. Technol.
,
182
(
1–3
), pp.
107
116
. 10.1016/j.jmatprotec.2006.07.015
30.
Wong
,
W. K.
,
Yuen
,
C.
,
Fan
,
D.
,
Chan
,
L.
, and
Fung
,
E.
,
2009
, “
Stitching Defect Detection and Classification Using Wavelet Transform and BP Neural Network
,”
Expert Syst. Appl.
,
36
(
2
), pp.
3845
3856
. 10.1016/j.eswa.2008.02.066
31.
Zhao
,
B.
,
Li
,
X.
,
Lu
,
X.
, and
Wang
,
Z.
,
2018
, “
A CNN-RNN architecture for multi-label weather recognition
,”
Neurocomputing
,
322
, pp.
47
57
. 10.1016/j.neucom.2018.09.048
You do not currently have access to this content.