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Wear debris analysis for condition monitoring of gears
Tribology for Energy Conservation / D. Dowson et al. (Editors) © 1998 Elsevier Science B.V. All rights reserved.
431
Wear debris analysis for condition monitoring of gears J. Sugimura a, M. Takesue b and Y. Yamamoto a a Department of Energy and Mechanical Engineering, Kyushu University 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-81, Japan b Lubricants Research Laboratory, Idemitsu Kosan Co., Ltd. 24-4 Anesakikaigan, Ichihara-shi, Chiba 299-01, Japan
A technique of wear debris image analysis was applied to monitoring of wear of spur gears. Six feature parameters were determined for a number of wear debris collected at different stages of run. In a pitting test under a constant load, initial running-in wear was followed by steady state wear and finally the fatigue failure. The progress of wear was characterised by wear debris features; the increase in the average diameter and roundness was indicative of fatigue wear that lead to severe pitting. In the tests in which load was increased in steps, relatively dark and large rough particles were generated at latter stages, which could be a sign of the fatal scuffing failure to occur by further increase in load. Quantitative examination was made by the averages of the feature parameter and by classification of the debris according to the parameter values.
1. I N T R O D U C T I O N G e a r s s u f f e r from a wide v a r i a t i o n of tribological failures [1,2], because they are subject to r e p e a t e d c o n t a c t u n d e r transient conditions. Among these the most destructive failures are fatigue and s c u f f i n g of tooth surfaces, and it is often the problem that sign of their occurrence is hardly detectable before they occur. While the mechanisms of the failures have yet to be investigated, it is very important to establish a method for properly m o n i t o r i n g conditions of gear surfaces in operation in order to prevent such fatal damages. The most popular technique of monitoring contact conditions is to analyse wear debris contained in oils using e.g. ferrography [3]. This kind of techniques, however, has disadvantages that in many cases they only give qualitative information, and that experienced persons who knows well about the machines are required in order to give reliable diagnosis. One way of overcoming these disadvantages is to aid the determination of particle features and diagnosis by computers. Many investigators have introduced computer image technologies
for measuring shapes and surface features of wear debris [4-7] and constructing database and diagnostic tools in computers [8]. The authors developed a method to predict lubrication and wear conditions of sliding surfaces based on computer image analysis of wear debris [9-13]. The technique (i) describes features of wear debris using several feature parameters, (ii) sorts relations between these features and sliding c o n d i t i o n s using neural n e t w o r k s , and (iii) identifies debris from unknown conditions based on (i) and (ii). This works fairly well for debris generated under well-defined sliding conditions as in laboratory wear tests. This paper describes the application of the technique to monitoring wear of spur gears. In addition to c o n v e n t i o n a l p a r a m e t e r studies, classification of wear debris are made. The results are compared with ferrographic results.
2. W E A R D E B R I S A N A L Y S I S
F o u r basic p a r a m e t e r s [ I 0 ] are used for describing morphological and optical features of wear debris c o l l e c t e d on m e m b r a n e filters.
432
.:~i~: ~i
shape but only represents complexity of contour shape. The term 'roundness' will be used for the modified roundness in what follows. Reflectivity R of a particle is defined by
Periphery P
~
R = (D/D0) '/Y R0
ProjectionAreaA
where D is the grey level averaged over the particle's reflection image, D Oand R 0 is the grey level and reflectivity of a reference surface, and T is an exponent that describes the nonlinearity in output of the camera [ 10]. The camera currently used has "/of 0.568. A white membrane filter is used as a reference surface with R 0 of unity under the light source adjusted to give D Oof 200. Fibre ratio F is for describing thin and fibrous particle and written as
~ Averagegrey level D
Figure I. Dimensions of wear debris These are representative diameter D, elongation 0, modified roundness ~ ' , and reflectivity R. In a d d i t i o n , fiber ratio F and c o n t r a s t C are introduced. Figure 1 schematically shows a wear debris and its dimensions. The representative diameter D is defined as square root of projected area A of a particle. E l o n g a t i o n ~ is the aspect ratio L / W where particle width W is the minimum Feret diameter and the length L is taken perpendicular to that, Modified roundness ~ ' is defined as a square of the ratio of the perimeter P to a perimeter P l of an ellipse having the same area and the same aspect ratio [ I0]. Formally, (1) = p2/Pel2 where Pel - 4 l ] - ~
E( I 1 - ; l
'
(2,
and a function E(k) is the elliptic integral of the second kind given by E(k) =
~/1-k2 sin 2 0 dO
(4)
(3)
This is the extension of roundness which uses a perimeter of a circle instead of an ellipse. The advantage of using an ellipse is that the deduced roundness value is not affected by elongated
F = L '2 / A (5) where L' is the fibre length measured along the fibre. This has been used by Kirk et al. [7] to detect and measure cutting wear debris. S u r f a c e s of wear debris e x h i b i t v a r i o u s patterns of brightness and colour due to their surface roughness and/or chemical reactants formed on metallic surfaces. Such patterns are called textures. As the simplest form of surface textural d e s c r i p t o r s [14], the p r e s e n t study introduces contrast C for a binary image. This is defined as a ratio of the number of those pair of neighbouring pixels that have different grey levels, to the number of all possible pairs based on 4-neighbours. It does not distinguish between the four d i r e c t i o n s , w h i c h is p r a c t i c a l l y advantageous in the present study because wear debris lie randomly on membrane filters. Figure 2 schematically shows binary images of two r e g i o n s h a v i n g same shape. T h e i r peripheries are drawn by thick lines. Both the regions consists of 21 pixels of which 11 are bright and I 0 are dark. However the number of pairs having different brightness are 7 for the left
I[ .I..L.I.!..)_I )mlt ....... I t [..... .......
mmm mm m m
m
m
i!!iii?m!i!!i:iiii! m ii!iim m mm mmm
Figure 2. Two regions with different contrast
433
region and 25 for the right, resulting in the contrasts of 7/30 and 25/30, respectively. Thus particles with fine textures, usually due to their roughness created by plastic deformation, will have high values of C, whereas those having relatively smooth surfaces will have low values of C. A particle with either totally bright or totally dark surface will have a C of zero. Determination of the feature parameters are made using an image analysis system consisting of an optical microscope, a CCD camera, and a personal computer connected to a workstation [9,10]. Microscopic images of wear debris on membrane filters are taken with transmitted light and reflected light separately, and stored as 24bit colour images of 512x512 pixels. In the present study, fifty pairs of images are taken for each filter using either or both of an objective lens of magnifications of 20 and 40. Details of the method of obtaining the first four parameters are described elsewhere [ 10]. For the fibre ratio, a skeleton [ 15] of each particle is obtained, and its length is used as the fibre length. Skeletons for non-fibrous particles often have branches, which are excluded here. In binarising images for calculating the contrast, a threshold grey level of 128 is employed.
3. F Z G G E A R T E S T S Gear tests were conducted using an FZG gear oil test rig in two ways. One was the pitting test in which spur gears were run under constant load until fatigue failure on gear teeth occurred. The other was the FZG gear oil test in which the load was increased in steps according to the standard procedure until scuffing occurred. One pitting test and two standard oil tests were conducted, which will be written as PI, S I and $2. 3.1 Pitting test In order to study gear wear that progresses until fatigue failure occurs, a pair of gears with no profile displacement were prepared. Table 1 shows the specification of the wheel and the pinion. The test was conducted under a pinion speed of 2250 rpm, and a load equivalent to the FZG load stage 8, which gave a Herzian contact pressure of 1.74 GPa at the pitch line. Bulk oil
Table 1 Gears used in the pitting test Pinion
Wheel
Module (mm) 4.5 Number of teeth 16 24 Effective face width (mm) 10 20 Pressure angle (°) 20 Material SCM420(Hardened) Brinnel hardness (kgf/mm 2) 634 545 Surface finish Ground Table 2 Lubricants used Test
PI and S 1 $2
Viscosity (mmVs) at 40°C at 100*C 90.51 25.01
10.89 4.707
Viscosity Index 107 106
t e m p e r a t u r e was kept c o n s t a n t at 80°C. A mineral base oil P-500N with 0.5% of an SP-type e x t r e m e p r e s s u r e a d d i t i v e was used as a lubricant. The oil was applied by dip lubrication; the amount of the oil was 1.5 liter. Table 2 shows viscosities of the oils used. The rig was stopped at predetermined time of run, and the oil was drained and the gears were removed from the rig for observation and weight measurement. After mounting the gears again and filling the chamber with new oil, the rig was restarted for another duration of run. The times of the oil change were I, 3, 10, 50, 90 and 120 in hours, and the test was finished at 130 hours when severe pitting occurred. The periods of run will be numbered from 1 to 7; e.g. the period 2 for the run between 1 hour and 3 hours. The drained oils were collected for particle analyses. E x a m i n a t i o n of f e r r o g r a m s by a normal procedure and the determination of the wear severity index I on the DR ferrography were conducted. 3.2 Standard gear oil tests In o r d e r to study g e a r w e a r l e a d i n g to scuffing failure, the tests were conducted by the
434
Table 3 Gears used in the scuffing tests Pinion
Wheel
Gear type Tooth profile A gears ( D I N 5 1 3 5 4 ) Module(mm) 4.5 Number of teeth 16 24 Effective face width (mm) 20 20 Profile displacement 0.8635 -0.5 Material 29 MnCr 5 (DIN 17210) Surface finish Ground (Maag cross-grinding)
FZG standard gear oil test procedure [16]. Pairs of the tooth profile A gears were used, whose specifications are shown in Table 3. Under a pinion speed of 2170 rpm, and the starting oil temperature of 900C, load was increased in steps every 15 minutes. In the test S1, the oil was replaced with fresh oil before each load stage, as in the pitting test, whereas the same oil was used throughout the test $2. The mineral base oil an SP-type EP additive and a gear oil containing several percent of an EP additive and some other agents were used in S 1 and $2, respectively.
4. R E S U L T S 4.1 Pitting test The test was conducted for 130 hours, at the end of which pitted craters was observed on a tooth of the pinion. Also observed were frosting near the roots and scratch marks near the tips of the pinion. On the wheel, there were frosting near the tips and roots, and small dents along the pitch line. Figure 3(a) shows photographs of the pinion taken after the test. The arrow indicate the pitted tooth. Observation of gear teeth at earlier periods revealed that scratch marks appeared on the tips of the wheel as early as at the end of the first period, while ground marks on both the wheel and pinion were gradually erased off by 50 hours, when light frosting appeared near the roots. Pitting on two teeth of the pinion and the mating teeth of the wheel was observed after the sixth period, i.e. at 120 hours.
~.~!~
.:.~.
•
Figure 3. Gears after the tests; (a) P I, (b) S 1
Figure 4(a) shows the variation of wear rate obtained from the weight measurement. The wear rate exhibits a form of the typical bathtub c u r v e s h o w i n g r u n n i n g - i n w e a r up until 10 hours, steady state wear till 90 hours, and the final fatigue wear in which the wear rate rises again. The pinion wore much more than the wheel. These results agree with the observation described above. In the figure are also shown square marks, for which the explanation will be made later. 4.2 Standard gear oil tests The test S 1 was terminated at the load stage 8 by the occurrence of heavy scuffing. Figure 3(b) shows the pinion after the test. Radial tear marks spread over the pinion tooth and three wheel teeth. A n o t h e r pinion tooth and the mating wheel teeth are covered partly with tear marks. These were observed only at the load stage 8. In earlier stages, scratch marks appeared on the pinion at the stage 3 and wear gradually polished off the crossed ground marks by the stage 6. Wear amount was measured only after stages 1, 6, 7 and 8. The result is shown in Figure 4(b).
435
Because of the short duration of each run, the weight loss are quite small except for the final stage in which considerable amount of wear has been recorded. Figure 4(c) shows the result for the test $2.
20
(xlO 3) ,2O
15
15 ,r,-
_.=
t1 0 ~" O
:E
~5
5--
1
2
3
4 5 Period
6
7
(xl0 a)
40
20
(b) S 1 30
15
Pinion
E ~'20 -
lo-
10...=
~ 1
6
7
8
Load stage 80 6O
B
'
E ~'40
The gears survived longer up to the stage 10 and scuffing occurred at the stage ! 1. The extreme pressure additives may have worked to provide the less wear and longer life. 4.3 W e a r d e b r i s p a r a m e t e r s W e a r d e b r i s visible u n d e r an o p t i c a l microscope are mainly steel particles having shiny or partly shiny surfaces in the present tests. T h e r e are also p a r t i c l e s of o x i d e s or those covered with oxides or with films produced possibly by c h e m i c a l reaction with the EP additives. Although some of them are large enough to be detected for analysis, in particular in the latter stages, most of them are very small and neglected in the particle analysis. Evidence of such submicron particles is found in the co!our of membrane filters. Considering the range of size of metallic particles present, and the resolution of the image a n a l y s i s for d e s c r i b i n g particle shapes and surface features as described in the previous section, only particles with projected areas larger than 43 lam 2 are chosen and analysed. This corresponds to particles of over 400 pixels with the x40 objective lens. Fifty image frames for each oil sample were randomly taken. Amount of oil filtered varied between 5 and 45 ml, depending on particle's concentration in the oil s a m p l e s . This is taken into a c c o u n t w h e n number of wear debris or volume are compared. Table 4 shows the number of wear particles analysed in the tests P l and S1. In the pitting test, the number in the second period, i.e. from 1 to 3 hours, is the largest. Because the time involved in each period is not the same, the numbers should be compared in terms of number per unit time. Hence it can be easily seen that the n u m b e r of w e a r p a r t i c l e s g e n e r a t e d is significantly high during the running-in period, Table 4 Number of wear debris analysed with x40 lens
20
5-6
7-8 9 Load stage
10
Figure 4. Variation of wear amount
11
PI Period
1 2 52 184
3 68
S 1 Stage
1 69
7 8 50 157
6 42
4 5 6 7 75 102 119 145
436 0.7
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so
> o 0.5
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1.9 1.8
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I ...........
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.Ill,.,.,...=, - , - " =
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,,
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Elongation
Figure 6. Variations in the average values of wear particle parameters in the test P 1
Figure 7. Variations in the average values of wear particle parameters in the test S 1
and then decreases until it rises again in the latter periods when pitting finally occurs. In the test S 1, the number is larger for higher load, except for the initial load stage where the running-in wear must have occurred. Modified roundness is plotted against the representative diameter for all the particles analysed for the second and the fourth periods of the test P I in Figure 5. The values scatter very widely, though the parameters extend in a wider region in the second period than in the fourth period. This indicates that particles of larger size and more complex shapes have been generated in the second period. The simplest method of c o m p a r i n g distributed values is to take averages. Figures 6 to 8 show the averages of the four parameters plotted in the forms of reflectivity against
diameter, and roundness against elongation. The plots for the test P I show a striking feature; they form a loop in which the parameters in the final period are close to those in the first period. The values are largest in the second period, and decrease in the third and fourth periods, the latter periods are characterised by slight decrease in reflectivity and increase in roundness. This suggests that, after the long duration of steady state wear, fatigue of the contacting surfaces begin to cause generation of particles which have features partly closer to those in the running-in wear. This reflects the formation of scratches and frosting that proceeded the severe flaking as described in Section 4.1. In the test S I, the change from running-in wear in stage 1 to steady state wear in stage 6 is similar to that seen in P l, though four stages
437
~
0.8
5 ""~"
0.7
incident light. The modified roundness exhibits a characteristic change that it increases as the load increases, which agrees with the result of S1. This is surprising when the difference in the m e t h o d of s a m p l i n g oils are c o n s i d e r e d . However, because the latter two stages produced much more wear than other stages, the high roundness values remain after the averaging with particles from other stages.
6 (a)
°-.
>
°~
o 0.6
11 , ~ , ~
Stage 10
0.5 0.4
--
9
~ ...... , 10 11
t 13
12
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Diameter, tam
1.9
(b)
1.8 ¢D
~ 1 .7
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o 1.6 I::E
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1.5 1.4
1
1.4
1.5
1.6
7" bT Stage 5 _
I
1.7
. . . . . . . .
I ................................
1.8
1.9
Elongation Figure 8. Variations in the average values of wear particle parameters in the test $2 i n b e t w e e n are not a n a l y s e d . The m a r k e d increase in elongation and roundness in the stage 7 may indicate severer wear causing scratches and small scuff marks under increased load, while the drop in elongation may represent the debris formed by heavy scuffing. The data for the test $2 shown in Figure 8 were obtained with a x20 objective lens. These show complicated traces as compared with the former two tests, though the values are within a much narrower region. This is because the oil was not changed throughout the test and so the oil samples contained wear debris generated in that stage as well as those generated in all the preceding stages. The level of reflectivity is higher than those in Figures 6 and 7, which may be mainly due to the difference in aperture of the lenses used which d e t e r m i n e s the angle of
4.4 C l a s s i f i c a t i o n o f w e a r d e b r i s
Observation of the wear debris has revealed that the debris analysed include several types of particles which can be visually distinguished. In order to investigate how the feature parameters shown in the preceding section describe the features of these particle types, some particles are randomly selected here from the tests P1 and S1, and relations between the parameters and their visual features are examined. Images were taken with a x20 objective lens. In addition to the four parameters, the fibre ratio and the simplified contrast value were obtained. The debris were arbitrarily classified into the following six types: (i) (ii) (iii) (iv) (v) (vi)
normal platelet- bright laminar particle with smooth contour, irregular platelet - bright laminar particle with distorted contour shape, d e f o r m e d p a r t i c l e - large p a r t i c l e with curved surface or irregular roughness, rough p a r t i c l e - platelet having rough surface, usually with striations, dark particle - particle with rough dark surface, and curl - curly elongated particle
Photographs of typical debris for these types are shown in Figure 9. Particles of types i to iv are laminar particles. Although all the types of debris may suffer from plastic deformation to s o m e e x t e n t before, d u r i n g or after their generation, the word 'deformed' is used for type iii because their shapes look as if they were heavily bent or sheared, a l t h o u g h t h e y might actually not have suffered from that deformation. Type v are mainly oxides, and type vi are usually called cutting wear particles.
438
" "'~ ......................
(a) 00 00
4
v
0) C~
,
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= .
:::::I ::
o Normal platelet ,, Irregular platelet ,t Deformed particle i Rough particle = Dark particle v Curl
=
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.
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..........
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................ =...
50
40
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20L, pm ,,!..... Figure 9. Various types of wear debris
A
........................ "
1
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-t
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.................t_ ...
0.25
....................................................
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I
Because only debris larger than 6.6 I.tm in diameter are analysed here, ' n o r m a l rubbing wear particle' in the conventional definition [3] is not included in the six types. If the appearance of particles is only concerned, however, the 'normal rubbing' may correspond to i and also to some in ii, iii and iv. "Fatigue chunk' and 'severe wear particle' may correspond to large ones in iii and iv, and to ii and iv, respectively. There is no clear one-to-one relation between the present and the conventional definition, because the present types are based on simple features of wear debris, i.e. brightness, general shape, and general surface feature. Six parameters for about hundred debris from the tests PI and S 1 were computed and plottexl in Figure 10. The x20 objective lens was used; therefore reflectivity takes higher values. Marks used represent the types of wear debris which are determined by human eyes.
C]V
0.15
5
(c)
I
II ea II
6
t
m
-
C
o 00.10
0.05
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0.00
0.0
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A I A
A
Q
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IA
A
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li ~ I ~
.
0.5 1.0 Reflectivity
1.5
Figure 10. Particle parameters for the six types of wear debris from the tests P1 and S 1 Some quantitative description for the six types can be made from the figures. Type i and ii have reflectivity higher than 0.9, while type v has reflectivity lower than 0.5. Those particles having reflectivity between 0.5 and 0.9 belong either to type iii or iv. Type vi is readily
439
distinguishable from its high fibre ratio. Type ii debris are generally larger in size and roundness than type i debris. Debris of types iii and iv cannot clearly be distinguished because the parameters are in similar region. However, type iii debris are in general larger and have less contrast than type iv. Thus alternative classification can be made if types iii and iv are regrouped on the basis of the parameter values. Using the same numbers for the types in the above classification, the revised types and their conditions are: (I) (II) (III) (IV)
(v)
(VI)
normal platelet - R > 0.9 and ~ ' < 1.5, irregular platelet- R >_0.9 and ~'_> 1.5, deformed particle - 0.5 <_ R < 0.9 and (D> 10~tm and C<0.15), rough particle 0.5 _< R < 0.9 and (D< I 0btm or C>0.15), dark particle R < 0.5, F>5 curl-
Images for all the test stages were taken again using the x20 lens, and analysed to count the numbers of the six types of debris according to the above rules. The results are shown in terms of ratio of the numbers in Figure 11. In the pitting test P l, type IV predominates others throughout the test. There is increase in debris of type II in the running-in wear in the second period, while there is gradual increase in larger debris of types I and II after the fourth period. This agrees with the increase in the average diameter and roundness shown in Fig. 6. In the standard gear oil tests S 1 and $2, type IV again keeps higher percentage, although the type V, dark particles, also shares comparable amount. General trend is that as the load is increased, types IV and V increase while types I and II, shiny particles, decrease. Changes prior to the occurrence of heavy scuffing, namely from stage 6 to stage 7 in the test S 1 and from stage 9 to stage 10 in the test $2, are characterised by increase in debris of types V and decrease in types I and II. This may imply that heavier loads have enhanced oxidisation or some other chemical reactions and generation of heavily deformed particles. The generation of c u t t i n g w e a r p a r t i c l e s of type VI may be reflecting the scratches observed on the gear
0.5 IN.
E =
.... !!'!;=!,,, L
(a,
i = Type V! ]
0.4
/
c 0.3 tD
0.2
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0.0
1
0.5
2
3
~
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4 5 Period
6
7
6 Load stage
7
8
(b)
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=........................................
(c)
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0.4 E c 0.:3
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E =0.1 z 0.0
5
6
7 8 9 Load stage
10
11
Figure 11. Ratio of the number of six types of wear debris to the total number
teeth. Detection of particle types may thus be more effective in the cases where unusual or distinctive particles are generated.
440
Table 5 Ferrography results with I s for the test P1 Period
1
2
3
Rubbing wear particles Severe wear particles Cutting wear particles Fatigue chunks Large laminar particles Other steel particles
x x
x
x
x
I s (x104)
0.86
5.7
2.7
8.5
XX
XX
4
5
XX
XXX
6
7
XXX
XXX
.
.
.
.
X
X
.
.
.
.
XX
XX
x
x
x
10.2
12.0
10.1
xxx: many, xx: fair, x: a few, -" none
Table 6 Ferrography results with I s for the test S I Stage
1
6
Rubbing wear particles Severe wear particles Cutting wear particles Fatigue chunks Large laminar particles Other steel particles
xx x
xx x
I (xlO')
1.1
0.53
7
8
xx
xx
-
xx
x
x
0.89
2.9
xxx: many, xx: fair, x: a few, -- none
4.5 Ferrography r e s u l t s T a b l e s 5 and 6 s u m m a r i s e s the results of ferrographic examination together with the wear severity index I s obtained by D R Ferrography for the tests P1 and S 1. According to the definition w h i c h the o p e r a t o r p r o v i d e s , r u b b i n g w e a r particles are free metal flakes smaller than 5 ~tm in d i a m e t e r , s e v e r e w e a r particles are those particles larger than 15 la.m with sharp edges and striations, cutting wear particles are thin curly c u t t i n g f r a g m e n t s , f a t i g u e c h u n k s are t h i c k p a r t i c l e s l a r g e r t h a n 10 !.tm w i t h i r r e g u l a r p e r i p h e r y , l a r g e l a m i n a r p a r t i c l e s are t h i n laminar particles larger than 25 la.m with holes within themselves, and the other steel particles include those larger than 10 ~tm and thinner than
the chunk that are often observed in break-in wear. T h o s e i n c l u d e d in the list o f the e x a m i n a t i o n r e s u l t s , i.e. o x i d e s , c o r r o s i v e particles, non-ferrous particles, inorganic crystalline substances, a m o r p h o u s substances and fibres, are omitted here. As can be s e e n f r o m the t a b l e s , the examination is successful in detecting fatigue in the test PI as early as at the period 6, whereas it fails to predict the occurrence of scuffing in the test S 1, although it does detect chunks at the final stage. It also fails to detect the cutting type of wear debris in S 1. The wear severity index I s may add quantitative information. In fact, in the test S 1, I s increases from the stage 6 to 7, which may be indicative of increase in wear that may lead to scuffing. I s values divided by hours for each period are plotted in Figure 3(a) for comparison with the wear rate. The plot correlates well with the bathtub wear curve.
5. D I S C U S S I O N The present results will now be discussed in terms of the applicability of the particle analysis to prediction of wear progression and destructive damages. It is interesting that, in the pitting test, the debris feature parameters averaged over larger debris e x h i b i t a c h a r a c t e r i s t i c c h a n g e w h i c h a p p e a r s to f o r m l o o p s w h e n p l o t t e d . T h e
441
expression used in F i g u r e s 6 to 8 seems to p r o v i d e a c l e a r e r visible i n d i c a t i o n o f the changes occurring on the gear teeth than those of the class percentages shown in Fig. I 1. The reason may be that, either because of overlap in the features of different types of debris, or because of inadequate ability of the present simple p a r a m e t e r s for r e p r e s e n t i n g detailed features of the debris, the feature of each debris is less informative than general trends described by the average values. The attempt to distinguish particles by their feature parameters will however be improved by improving characterisation of surface textures. The present contrast parameter is so primitive in setting the threshold grey level that it cannot clearly identify the visual features that human eyes can recognise. The nature of wear that it consists of removal of a n u m b e r of p a r t i c l e s s h o u l d also be considered. Even in one simple sliding system made of a pin and a disc, millions of debris will be g e n e r a t e d u n d e r m i l l i o n s of d i f f e r e n t geometrical and mechanical conditions. Gears have far wider variation in microscopic contact conditions. Unless a vast number of debris of distinct features are generated, such as in the cases when pitting or scuffing have occurred, it may not be easy to deduce decisive information from features of a small n u m b e r of debris. Nevertheless, it is not after these damages have occurred but during apparently normal wear stages that such information is necessary. Recent improvements in wear debris analysis [4-7,10] are m a i n l y focused on p r e c i s e determination and characterization of each wear debris for proper classification. One goal of this may be to establish a method to predict wearing conditions from analysis of a small number of debris. In order to make this possible, it may be the way to sort and comprehend the data that is the most important. As the averaging of the parameters works to describe gradual changes in w e a r in the p r e s e n t study, some statistical treatment may be necessary for interpreting even detailed i n f o r m a t i o n a c q u i r e d by a d v a n c e d methods. Although no statistical consideration has been made in the test $2 where the oil samples might contain debris from all different stages of
wear, the present analysis has detected changes in wear in the stage preceding the scuffing. This is b e c a u s e the a m o u n t of w e a r has g r e a t l y increased in that stage so that the probability of encountering the debris produced in the stage is relatively high. However, this is not always the case. To make effective selection of debris before or during the analysis may therefore be necessary. Ferrographic analysis has an advantage in that it first separate particles according to their sizes. This m a k e s it e a s i e r to find a large particles, of normally few percentage in their number. It seems that, however, the ferrography at the present state is inappropriate to detect minor changes in wear that might lead to more destructive damages. In the case of the test S 1, it has failed to catch the changes in the stage 6 to the stage 7. In ferrography as well as in the present debris analysis, it is not only the absolute number or c h a r a c t e r i s t i c s but also the c h a n g e s in the c h a r a c t e r i s t i c s that m u s t c o n t a i n useful information for estimation and prediction of contact conditions. In this respect, reliable and quantitative description is essential. Future works will focus on selection and representation of w e a r debris on the basis of s t a t i s t i c a l reliability.
6. C O N C L U S I O N S Wear debris generated in FZG gear tests were analysed. It was found, and q u a n t i t a t i v e l y described with image analysis, that a range of different size and types of wear particles were generated. Six descriptive p a r a m e t e r s were used, and also wear debris were classified into six types according to the parameter values. In a prolonged pitting test, increase in the average diameter and roundness was indicative of increase in fatigue wear that lead to pitting failure in the further running. In increasing-load tests, relatively dark and large rough platelets were shown to be generated at latter stages under heavy loads, which could be a sign of the fatal scuffing damage to occur at a higher load. The present study showed the applicability of the image analysis approach to monitoring of
442
complicated sliding system as gears. Reliability of the method can be improved by introducing selective detection and more precise description of wear debris that are representative of the wear process that leads to heavier destructive failure.
ACKNOWLEDGEMENT The authors would like to thank Mr. M. Hashimoto for his help in conducting the particle analysis.
REFERENCES
I. AGMA, Nomenclature of Gear Tooth Failure Modes, ANSI/AGMA 110.04, 1980. 2. Ku, P. M., Gear Failure Modes - Importance of Lubrication and Mechanics, STLE Trans., 19, 3 (1975) 239-249. 3. Bowen B., Scott D., Seifert W. and Westcott V. C., F e r r o g r a p h y , T r i b o l o g y Intrn., 9 (1976) 109-I 15. 4. Uedelhoven W., Franzl M. and Guttenberger J., The Use of Automated Image Analysis for the Study of Wear Particles in Oil-Lubricated Tribological Systems, Wear, 142 (I 991) 107113. 5. Thomas A. D. H., Davis T. and Luxmoore A. R., C o m p u t e r I m a g e Analysis for Identification of Wear Particles, Wear, 142 (1991) 213-226. 6. Kirk T. B., Panzera D., Anamalay R. V. and Xu Z. L., Computer Image Analysis of Wear Debris for Machine Condition Monitoring and Fault Diagnosis, Wear, 181-183 (1995)
717-722. 7. R a a d n u e S. and R o y l a n c e B. J., The Classification of Wear Particle Shape, Lubr. Engrg., 51, 5 (I 995) 432-437. 8. Roylance B. J., Albidewi !. A., Luxmoore A. R. and Price A. L., The Development of a Computer-Aided Systematic Particle Analysis Procedure- CAPSA, Lubr. Engrg., 48, 12 (1992) 940-946. 9. Sugimura J. and Yamamoto Y., Development of Diagnostic System for Sliding Surfaces Part 1: Image Analysis of Wear Particles, Proc. JSLE Tribology Conf., Fukuoka, 1991, 365-368. 10. Sugimura J., Hashimoto M. and Yamamoto Y., being submitted. 11. Itoh T., Sugimura J. and Yamamoto Y., Prognosis of Scuffing Failure by Wear Debris Image Analysis, Proc. Intrn. Triboiogy Conf., Yokohama 1995, 193-198. 12. Sugimura J., Umeda A. and Yamamoto Y., W e a r Debris Identification with Neural Networks, J. JSME, Pt.C, 61, 590 (1995) 4055-4060. 13. Sugimura J., Umeda A. and Yamamoto Y., Diagnosis of Friction Conditions Based on Wear Debris Analysis, Proc. JAST Tribology Conf., Kitakyushu 1996-10, 377-379. 14. Haralick R. M., Shanmugan R. and Dinstein I., Textural Features for Image Classification, IEEE Trans. Syst. Man Cybern., SMC-3, 6 (1973) 610-621. 15. Zhang T. Y. and Suen C. Y., A Fast Parallel Algorithm for Thinning Digital Patterns, Comm. ACM, 27, 3 (1984) 236-239. 16. DIN 51354, Testing of Lubricants on the FZG-Gear Test Rig, 1964.
431
Wear debris analysis for condition monitoring of gears J. Sugimura a, M. Takesue b and Y. Yamamoto a a Department of Energy and Mechanical Engineering, Kyushu University 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-81, Japan b Lubricants Research Laboratory, Idemitsu Kosan Co., Ltd. 24-4 Anesakikaigan, Ichihara-shi, Chiba 299-01, Japan
A technique of wear debris image analysis was applied to monitoring of wear of spur gears. Six feature parameters were determined for a number of wear debris collected at different stages of run. In a pitting test under a constant load, initial running-in wear was followed by steady state wear and finally the fatigue failure. The progress of wear was characterised by wear debris features; the increase in the average diameter and roundness was indicative of fatigue wear that lead to severe pitting. In the tests in which load was increased in steps, relatively dark and large rough particles were generated at latter stages, which could be a sign of the fatal scuffing failure to occur by further increase in load. Quantitative examination was made by the averages of the feature parameter and by classification of the debris according to the parameter values.
1. I N T R O D U C T I O N G e a r s s u f f e r from a wide v a r i a t i o n of tribological failures [1,2], because they are subject to r e p e a t e d c o n t a c t u n d e r transient conditions. Among these the most destructive failures are fatigue and s c u f f i n g of tooth surfaces, and it is often the problem that sign of their occurrence is hardly detectable before they occur. While the mechanisms of the failures have yet to be investigated, it is very important to establish a method for properly m o n i t o r i n g conditions of gear surfaces in operation in order to prevent such fatal damages. The most popular technique of monitoring contact conditions is to analyse wear debris contained in oils using e.g. ferrography [3]. This kind of techniques, however, has disadvantages that in many cases they only give qualitative information, and that experienced persons who knows well about the machines are required in order to give reliable diagnosis. One way of overcoming these disadvantages is to aid the determination of particle features and diagnosis by computers. Many investigators have introduced computer image technologies
for measuring shapes and surface features of wear debris [4-7] and constructing database and diagnostic tools in computers [8]. The authors developed a method to predict lubrication and wear conditions of sliding surfaces based on computer image analysis of wear debris [9-13]. The technique (i) describes features of wear debris using several feature parameters, (ii) sorts relations between these features and sliding c o n d i t i o n s using neural n e t w o r k s , and (iii) identifies debris from unknown conditions based on (i) and (ii). This works fairly well for debris generated under well-defined sliding conditions as in laboratory wear tests. This paper describes the application of the technique to monitoring wear of spur gears. In addition to c o n v e n t i o n a l p a r a m e t e r studies, classification of wear debris are made. The results are compared with ferrographic results.
2. W E A R D E B R I S A N A L Y S I S
F o u r basic p a r a m e t e r s [ I 0 ] are used for describing morphological and optical features of wear debris c o l l e c t e d on m e m b r a n e filters.
432
.:~i~: ~i
shape but only represents complexity of contour shape. The term 'roundness' will be used for the modified roundness in what follows. Reflectivity R of a particle is defined by
Periphery P
~
R = (D/D0) '/Y R0
ProjectionAreaA
where D is the grey level averaged over the particle's reflection image, D Oand R 0 is the grey level and reflectivity of a reference surface, and T is an exponent that describes the nonlinearity in output of the camera [ 10]. The camera currently used has "/of 0.568. A white membrane filter is used as a reference surface with R 0 of unity under the light source adjusted to give D Oof 200. Fibre ratio F is for describing thin and fibrous particle and written as
~ Averagegrey level D
Figure I. Dimensions of wear debris These are representative diameter D, elongation 0, modified roundness ~ ' , and reflectivity R. In a d d i t i o n , fiber ratio F and c o n t r a s t C are introduced. Figure 1 schematically shows a wear debris and its dimensions. The representative diameter D is defined as square root of projected area A of a particle. E l o n g a t i o n ~ is the aspect ratio L / W where particle width W is the minimum Feret diameter and the length L is taken perpendicular to that, Modified roundness ~ ' is defined as a square of the ratio of the perimeter P to a perimeter P l of an ellipse having the same area and the same aspect ratio [ I0]. Formally, (1) = p2/Pel2 where Pel - 4 l ] - ~
E( I 1 - ; l
'
(2,
and a function E(k) is the elliptic integral of the second kind given by E(k) =
~/1-k2 sin 2 0 dO
(4)
(3)
This is the extension of roundness which uses a perimeter of a circle instead of an ellipse. The advantage of using an ellipse is that the deduced roundness value is not affected by elongated
F = L '2 / A (5) where L' is the fibre length measured along the fibre. This has been used by Kirk et al. [7] to detect and measure cutting wear debris. S u r f a c e s of wear debris e x h i b i t v a r i o u s patterns of brightness and colour due to their surface roughness and/or chemical reactants formed on metallic surfaces. Such patterns are called textures. As the simplest form of surface textural d e s c r i p t o r s [14], the p r e s e n t study introduces contrast C for a binary image. This is defined as a ratio of the number of those pair of neighbouring pixels that have different grey levels, to the number of all possible pairs based on 4-neighbours. It does not distinguish between the four d i r e c t i o n s , w h i c h is p r a c t i c a l l y advantageous in the present study because wear debris lie randomly on membrane filters. Figure 2 schematically shows binary images of two r e g i o n s h a v i n g same shape. T h e i r peripheries are drawn by thick lines. Both the regions consists of 21 pixels of which 11 are bright and I 0 are dark. However the number of pairs having different brightness are 7 for the left
I[ .I..L.I.!..)_I )mlt ....... I t [..... .......
mmm mm m m
m
m
i!!iii?m!i!!i:iiii! m ii!iim m mm mmm
Figure 2. Two regions with different contrast
433
region and 25 for the right, resulting in the contrasts of 7/30 and 25/30, respectively. Thus particles with fine textures, usually due to their roughness created by plastic deformation, will have high values of C, whereas those having relatively smooth surfaces will have low values of C. A particle with either totally bright or totally dark surface will have a C of zero. Determination of the feature parameters are made using an image analysis system consisting of an optical microscope, a CCD camera, and a personal computer connected to a workstation [9,10]. Microscopic images of wear debris on membrane filters are taken with transmitted light and reflected light separately, and stored as 24bit colour images of 512x512 pixels. In the present study, fifty pairs of images are taken for each filter using either or both of an objective lens of magnifications of 20 and 40. Details of the method of obtaining the first four parameters are described elsewhere [ 10]. For the fibre ratio, a skeleton [ 15] of each particle is obtained, and its length is used as the fibre length. Skeletons for non-fibrous particles often have branches, which are excluded here. In binarising images for calculating the contrast, a threshold grey level of 128 is employed.
3. F Z G G E A R T E S T S Gear tests were conducted using an FZG gear oil test rig in two ways. One was the pitting test in which spur gears were run under constant load until fatigue failure on gear teeth occurred. The other was the FZG gear oil test in which the load was increased in steps according to the standard procedure until scuffing occurred. One pitting test and two standard oil tests were conducted, which will be written as PI, S I and $2. 3.1 Pitting test In order to study gear wear that progresses until fatigue failure occurs, a pair of gears with no profile displacement were prepared. Table 1 shows the specification of the wheel and the pinion. The test was conducted under a pinion speed of 2250 rpm, and a load equivalent to the FZG load stage 8, which gave a Herzian contact pressure of 1.74 GPa at the pitch line. Bulk oil
Table 1 Gears used in the pitting test Pinion
Wheel
Module (mm) 4.5 Number of teeth 16 24 Effective face width (mm) 10 20 Pressure angle (°) 20 Material SCM420(Hardened) Brinnel hardness (kgf/mm 2) 634 545 Surface finish Ground Table 2 Lubricants used Test
PI and S 1 $2
Viscosity (mmVs) at 40°C at 100*C 90.51 25.01
10.89 4.707
Viscosity Index 107 106
t e m p e r a t u r e was kept c o n s t a n t at 80°C. A mineral base oil P-500N with 0.5% of an SP-type e x t r e m e p r e s s u r e a d d i t i v e was used as a lubricant. The oil was applied by dip lubrication; the amount of the oil was 1.5 liter. Table 2 shows viscosities of the oils used. The rig was stopped at predetermined time of run, and the oil was drained and the gears were removed from the rig for observation and weight measurement. After mounting the gears again and filling the chamber with new oil, the rig was restarted for another duration of run. The times of the oil change were I, 3, 10, 50, 90 and 120 in hours, and the test was finished at 130 hours when severe pitting occurred. The periods of run will be numbered from 1 to 7; e.g. the period 2 for the run between 1 hour and 3 hours. The drained oils were collected for particle analyses. E x a m i n a t i o n of f e r r o g r a m s by a normal procedure and the determination of the wear severity index I on the DR ferrography were conducted. 3.2 Standard gear oil tests In o r d e r to study g e a r w e a r l e a d i n g to scuffing failure, the tests were conducted by the
434
Table 3 Gears used in the scuffing tests Pinion
Wheel
Gear type Tooth profile A gears ( D I N 5 1 3 5 4 ) Module(mm) 4.5 Number of teeth 16 24 Effective face width (mm) 20 20 Profile displacement 0.8635 -0.5 Material 29 MnCr 5 (DIN 17210) Surface finish Ground (Maag cross-grinding)
FZG standard gear oil test procedure [16]. Pairs of the tooth profile A gears were used, whose specifications are shown in Table 3. Under a pinion speed of 2170 rpm, and the starting oil temperature of 900C, load was increased in steps every 15 minutes. In the test S1, the oil was replaced with fresh oil before each load stage, as in the pitting test, whereas the same oil was used throughout the test $2. The mineral base oil an SP-type EP additive and a gear oil containing several percent of an EP additive and some other agents were used in S 1 and $2, respectively.
4. R E S U L T S 4.1 Pitting test The test was conducted for 130 hours, at the end of which pitted craters was observed on a tooth of the pinion. Also observed were frosting near the roots and scratch marks near the tips of the pinion. On the wheel, there were frosting near the tips and roots, and small dents along the pitch line. Figure 3(a) shows photographs of the pinion taken after the test. The arrow indicate the pitted tooth. Observation of gear teeth at earlier periods revealed that scratch marks appeared on the tips of the wheel as early as at the end of the first period, while ground marks on both the wheel and pinion were gradually erased off by 50 hours, when light frosting appeared near the roots. Pitting on two teeth of the pinion and the mating teeth of the wheel was observed after the sixth period, i.e. at 120 hours.
~.~!~
.:.~.
•
Figure 3. Gears after the tests; (a) P I, (b) S 1
Figure 4(a) shows the variation of wear rate obtained from the weight measurement. The wear rate exhibits a form of the typical bathtub c u r v e s h o w i n g r u n n i n g - i n w e a r up until 10 hours, steady state wear till 90 hours, and the final fatigue wear in which the wear rate rises again. The pinion wore much more than the wheel. These results agree with the observation described above. In the figure are also shown square marks, for which the explanation will be made later. 4.2 Standard gear oil tests The test S 1 was terminated at the load stage 8 by the occurrence of heavy scuffing. Figure 3(b) shows the pinion after the test. Radial tear marks spread over the pinion tooth and three wheel teeth. A n o t h e r pinion tooth and the mating wheel teeth are covered partly with tear marks. These were observed only at the load stage 8. In earlier stages, scratch marks appeared on the pinion at the stage 3 and wear gradually polished off the crossed ground marks by the stage 6. Wear amount was measured only after stages 1, 6, 7 and 8. The result is shown in Figure 4(b).
435
Because of the short duration of each run, the weight loss are quite small except for the final stage in which considerable amount of wear has been recorded. Figure 4(c) shows the result for the test $2.
20
(xlO 3) ,2O
15
15 ,r,-
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2
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6
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The gears survived longer up to the stage 10 and scuffing occurred at the stage ! 1. The extreme pressure additives may have worked to provide the less wear and longer life. 4.3 W e a r d e b r i s p a r a m e t e r s W e a r d e b r i s visible u n d e r an o p t i c a l microscope are mainly steel particles having shiny or partly shiny surfaces in the present tests. T h e r e are also p a r t i c l e s of o x i d e s or those covered with oxides or with films produced possibly by c h e m i c a l reaction with the EP additives. Although some of them are large enough to be detected for analysis, in particular in the latter stages, most of them are very small and neglected in the particle analysis. Evidence of such submicron particles is found in the co!our of membrane filters. Considering the range of size of metallic particles present, and the resolution of the image a n a l y s i s for d e s c r i b i n g particle shapes and surface features as described in the previous section, only particles with projected areas larger than 43 lam 2 are chosen and analysed. This corresponds to particles of over 400 pixels with the x40 objective lens. Fifty image frames for each oil sample were randomly taken. Amount of oil filtered varied between 5 and 45 ml, depending on particle's concentration in the oil s a m p l e s . This is taken into a c c o u n t w h e n number of wear debris or volume are compared. Table 4 shows the number of wear particles analysed in the tests P l and S1. In the pitting test, the number in the second period, i.e. from 1 to 3 hours, is the largest. Because the time involved in each period is not the same, the numbers should be compared in terms of number per unit time. Hence it can be easily seen that the n u m b e r of w e a r p a r t i c l e s g e n e r a t e d is significantly high during the running-in period, Table 4 Number of wear debris analysed with x40 lens
20
5-6
7-8 9 Load stage
10
Figure 4. Variation of wear amount
11
PI Period
1 2 52 184
3 68
S 1 Stage
1 69
7 8 50 157
6 42
4 5 6 7 75 102 119 145
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Figure 6. Variations in the average values of wear particle parameters in the test P 1
Figure 7. Variations in the average values of wear particle parameters in the test S 1
and then decreases until it rises again in the latter periods when pitting finally occurs. In the test S 1, the number is larger for higher load, except for the initial load stage where the running-in wear must have occurred. Modified roundness is plotted against the representative diameter for all the particles analysed for the second and the fourth periods of the test P I in Figure 5. The values scatter very widely, though the parameters extend in a wider region in the second period than in the fourth period. This indicates that particles of larger size and more complex shapes have been generated in the second period. The simplest method of c o m p a r i n g distributed values is to take averages. Figures 6 to 8 show the averages of the four parameters plotted in the forms of reflectivity against
diameter, and roundness against elongation. The plots for the test P I show a striking feature; they form a loop in which the parameters in the final period are close to those in the first period. The values are largest in the second period, and decrease in the third and fourth periods, the latter periods are characterised by slight decrease in reflectivity and increase in roundness. This suggests that, after the long duration of steady state wear, fatigue of the contacting surfaces begin to cause generation of particles which have features partly closer to those in the running-in wear. This reflects the formation of scratches and frosting that proceeded the severe flaking as described in Section 4.1. In the test S I, the change from running-in wear in stage 1 to steady state wear in stage 6 is similar to that seen in P l, though four stages
437
~
0.8
5 ""~"
0.7
incident light. The modified roundness exhibits a characteristic change that it increases as the load increases, which agrees with the result of S1. This is surprising when the difference in the m e t h o d of s a m p l i n g oils are c o n s i d e r e d . However, because the latter two stages produced much more wear than other stages, the high roundness values remain after the averaging with particles from other stages.
6 (a)
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Elongation Figure 8. Variations in the average values of wear particle parameters in the test $2 i n b e t w e e n are not a n a l y s e d . The m a r k e d increase in elongation and roundness in the stage 7 may indicate severer wear causing scratches and small scuff marks under increased load, while the drop in elongation may represent the debris formed by heavy scuffing. The data for the test $2 shown in Figure 8 were obtained with a x20 objective lens. These show complicated traces as compared with the former two tests, though the values are within a much narrower region. This is because the oil was not changed throughout the test and so the oil samples contained wear debris generated in that stage as well as those generated in all the preceding stages. The level of reflectivity is higher than those in Figures 6 and 7, which may be mainly due to the difference in aperture of the lenses used which d e t e r m i n e s the angle of
4.4 C l a s s i f i c a t i o n o f w e a r d e b r i s
Observation of the wear debris has revealed that the debris analysed include several types of particles which can be visually distinguished. In order to investigate how the feature parameters shown in the preceding section describe the features of these particle types, some particles are randomly selected here from the tests P1 and S1, and relations between the parameters and their visual features are examined. Images were taken with a x20 objective lens. In addition to the four parameters, the fibre ratio and the simplified contrast value were obtained. The debris were arbitrarily classified into the following six types: (i) (ii) (iii) (iv) (v) (vi)
normal platelet- bright laminar particle with smooth contour, irregular platelet - bright laminar particle with distorted contour shape, d e f o r m e d p a r t i c l e - large p a r t i c l e with curved surface or irregular roughness, rough p a r t i c l e - platelet having rough surface, usually with striations, dark particle - particle with rough dark surface, and curl - curly elongated particle
Photographs of typical debris for these types are shown in Figure 9. Particles of types i to iv are laminar particles. Although all the types of debris may suffer from plastic deformation to s o m e e x t e n t before, d u r i n g or after their generation, the word 'deformed' is used for type iii because their shapes look as if they were heavily bent or sheared, a l t h o u g h t h e y might actually not have suffered from that deformation. Type v are mainly oxides, and type vi are usually called cutting wear particles.
438
" "'~ ......................
(a) 00 00
4
v
0) C~
,
~3
= .
:::::I ::
o Normal platelet ,, Irregular platelet ,t Deformed particle i Rough particle = Dark particle v Curl
=
..................................... .]
.
:D
o E:
I A
2-
A
~A
A
o
1
0
~'
el I
..........
.......................... i
10
/
ZX AII
=
20 30 Diameter, l~m
................ =...
50
40
20
(b) .g
!5
~10 .Q
i1 5
-
V A
0
20L, pm ,,!..... Figure 9. Various types of wear debris
A
........................ "
1
V
A
2
$
....t ..
-t
3 4 Elongation
.................t_ ...
0.25
....................................................
0.20
"
I
Because only debris larger than 6.6 I.tm in diameter are analysed here, ' n o r m a l rubbing wear particle' in the conventional definition [3] is not included in the six types. If the appearance of particles is only concerned, however, the 'normal rubbing' may correspond to i and also to some in ii, iii and iv. "Fatigue chunk' and 'severe wear particle' may correspond to large ones in iii and iv, and to ii and iv, respectively. There is no clear one-to-one relation between the present and the conventional definition, because the present types are based on simple features of wear debris, i.e. brightness, general shape, and general surface feature. Six parameters for about hundred debris from the tests PI and S 1 were computed and plottexl in Figure 10. The x20 objective lens was used; therefore reflectivity takes higher values. Marks used represent the types of wear debris which are determined by human eyes.
C]V
0.15
5
(c)
I
II ea II
6
t
m
-
C
o 00.10
0.05
"
I
iI~
-
0
III
0.00
0.0
I
A I A
A
Q
OV
IA
A
I
li ~ I ~
.
0.5 1.0 Reflectivity
1.5
Figure 10. Particle parameters for the six types of wear debris from the tests P1 and S 1 Some quantitative description for the six types can be made from the figures. Type i and ii have reflectivity higher than 0.9, while type v has reflectivity lower than 0.5. Those particles having reflectivity between 0.5 and 0.9 belong either to type iii or iv. Type vi is readily
439
distinguishable from its high fibre ratio. Type ii debris are generally larger in size and roundness than type i debris. Debris of types iii and iv cannot clearly be distinguished because the parameters are in similar region. However, type iii debris are in general larger and have less contrast than type iv. Thus alternative classification can be made if types iii and iv are regrouped on the basis of the parameter values. Using the same numbers for the types in the above classification, the revised types and their conditions are: (I) (II) (III) (IV)
(v)
(VI)
normal platelet - R > 0.9 and ~ ' < 1.5, irregular platelet- R >_0.9 and ~'_> 1.5, deformed particle - 0.5 <_ R < 0.9 and (D> 10~tm and C<0.15), rough particle 0.5 _< R < 0.9 and (D< I 0btm or C>0.15), dark particle R < 0.5, F>5 curl-
Images for all the test stages were taken again using the x20 lens, and analysed to count the numbers of the six types of debris according to the above rules. The results are shown in terms of ratio of the numbers in Figure 11. In the pitting test P l, type IV predominates others throughout the test. There is increase in debris of type II in the running-in wear in the second period, while there is gradual increase in larger debris of types I and II after the fourth period. This agrees with the increase in the average diameter and roundness shown in Fig. 6. In the standard gear oil tests S 1 and $2, type IV again keeps higher percentage, although the type V, dark particles, also shares comparable amount. General trend is that as the load is increased, types IV and V increase while types I and II, shiny particles, decrease. Changes prior to the occurrence of heavy scuffing, namely from stage 6 to stage 7 in the test S 1 and from stage 9 to stage 10 in the test $2, are characterised by increase in debris of types V and decrease in types I and II. This may imply that heavier loads have enhanced oxidisation or some other chemical reactions and generation of heavily deformed particles. The generation of c u t t i n g w e a r p a r t i c l e s of type VI may be reflecting the scratches observed on the gear
0.5 IN.
E =
.... !!'!;=!,,, L
(a,
i = Type V! ]
0.4
/
c 0.3 tD
0.2
E =0.1
Z
....
0.0
1
0.5
2
3
~
...t
~_,-.~,.-..---..~
4 5 Period
6
7
6 Load stage
7
8
(b)
CD
,', 0.4 E =
c 0.3
.,..=
O
~0.2 e~
E =0.1
Z
-
~ltr
0.0
O° 5
"~
L
=........................................
(c)
L--
0.4 E c 0.:3
~ 0.2 e~
E =0.1 z 0.0
5
6
7 8 9 Load stage
10
11
Figure 11. Ratio of the number of six types of wear debris to the total number
teeth. Detection of particle types may thus be more effective in the cases where unusual or distinctive particles are generated.
440
Table 5 Ferrography results with I s for the test P1 Period
1
2
3
Rubbing wear particles Severe wear particles Cutting wear particles Fatigue chunks Large laminar particles Other steel particles
x x
x
x
x
I s (x104)
0.86
5.7
2.7
8.5
XX
XX
4
5
XX
XXX
6
7
XXX
XXX
.
.
.
.
X
X
.
.
.
.
XX
XX
x
x
x
10.2
12.0
10.1
xxx: many, xx: fair, x: a few, -" none
Table 6 Ferrography results with I s for the test S I Stage
1
6
Rubbing wear particles Severe wear particles Cutting wear particles Fatigue chunks Large laminar particles Other steel particles
xx x
xx x
I (xlO')
1.1
0.53
7
8
xx
xx
-
xx
x
x
0.89
2.9
xxx: many, xx: fair, x: a few, -- none
4.5 Ferrography r e s u l t s T a b l e s 5 and 6 s u m m a r i s e s the results of ferrographic examination together with the wear severity index I s obtained by D R Ferrography for the tests P1 and S 1. According to the definition w h i c h the o p e r a t o r p r o v i d e s , r u b b i n g w e a r particles are free metal flakes smaller than 5 ~tm in d i a m e t e r , s e v e r e w e a r particles are those particles larger than 15 la.m with sharp edges and striations, cutting wear particles are thin curly c u t t i n g f r a g m e n t s , f a t i g u e c h u n k s are t h i c k p a r t i c l e s l a r g e r t h a n 10 !.tm w i t h i r r e g u l a r p e r i p h e r y , l a r g e l a m i n a r p a r t i c l e s are t h i n laminar particles larger than 25 la.m with holes within themselves, and the other steel particles include those larger than 10 ~tm and thinner than
the chunk that are often observed in break-in wear. T h o s e i n c l u d e d in the list o f the e x a m i n a t i o n r e s u l t s , i.e. o x i d e s , c o r r o s i v e particles, non-ferrous particles, inorganic crystalline substances, a m o r p h o u s substances and fibres, are omitted here. As can be s e e n f r o m the t a b l e s , the examination is successful in detecting fatigue in the test PI as early as at the period 6, whereas it fails to predict the occurrence of scuffing in the test S 1, although it does detect chunks at the final stage. It also fails to detect the cutting type of wear debris in S 1. The wear severity index I s may add quantitative information. In fact, in the test S 1, I s increases from the stage 6 to 7, which may be indicative of increase in wear that may lead to scuffing. I s values divided by hours for each period are plotted in Figure 3(a) for comparison with the wear rate. The plot correlates well with the bathtub wear curve.
5. D I S C U S S I O N The present results will now be discussed in terms of the applicability of the particle analysis to prediction of wear progression and destructive damages. It is interesting that, in the pitting test, the debris feature parameters averaged over larger debris e x h i b i t a c h a r a c t e r i s t i c c h a n g e w h i c h a p p e a r s to f o r m l o o p s w h e n p l o t t e d . T h e
441
expression used in F i g u r e s 6 to 8 seems to p r o v i d e a c l e a r e r visible i n d i c a t i o n o f the changes occurring on the gear teeth than those of the class percentages shown in Fig. I 1. The reason may be that, either because of overlap in the features of different types of debris, or because of inadequate ability of the present simple p a r a m e t e r s for r e p r e s e n t i n g detailed features of the debris, the feature of each debris is less informative than general trends described by the average values. The attempt to distinguish particles by their feature parameters will however be improved by improving characterisation of surface textures. The present contrast parameter is so primitive in setting the threshold grey level that it cannot clearly identify the visual features that human eyes can recognise. The nature of wear that it consists of removal of a n u m b e r of p a r t i c l e s s h o u l d also be considered. Even in one simple sliding system made of a pin and a disc, millions of debris will be g e n e r a t e d u n d e r m i l l i o n s of d i f f e r e n t geometrical and mechanical conditions. Gears have far wider variation in microscopic contact conditions. Unless a vast number of debris of distinct features are generated, such as in the cases when pitting or scuffing have occurred, it may not be easy to deduce decisive information from features of a small n u m b e r of debris. Nevertheless, it is not after these damages have occurred but during apparently normal wear stages that such information is necessary. Recent improvements in wear debris analysis [4-7,10] are m a i n l y focused on p r e c i s e determination and characterization of each wear debris for proper classification. One goal of this may be to establish a method to predict wearing conditions from analysis of a small number of debris. In order to make this possible, it may be the way to sort and comprehend the data that is the most important. As the averaging of the parameters works to describe gradual changes in w e a r in the p r e s e n t study, some statistical treatment may be necessary for interpreting even detailed i n f o r m a t i o n a c q u i r e d by a d v a n c e d methods. Although no statistical consideration has been made in the test $2 where the oil samples might contain debris from all different stages of
wear, the present analysis has detected changes in wear in the stage preceding the scuffing. This is b e c a u s e the a m o u n t of w e a r has g r e a t l y increased in that stage so that the probability of encountering the debris produced in the stage is relatively high. However, this is not always the case. To make effective selection of debris before or during the analysis may therefore be necessary. Ferrographic analysis has an advantage in that it first separate particles according to their sizes. This m a k e s it e a s i e r to find a large particles, of normally few percentage in their number. It seems that, however, the ferrography at the present state is inappropriate to detect minor changes in wear that might lead to more destructive damages. In the case of the test S 1, it has failed to catch the changes in the stage 6 to the stage 7. In ferrography as well as in the present debris analysis, it is not only the absolute number or c h a r a c t e r i s t i c s but also the c h a n g e s in the c h a r a c t e r i s t i c s that m u s t c o n t a i n useful information for estimation and prediction of contact conditions. In this respect, reliable and quantitative description is essential. Future works will focus on selection and representation of w e a r debris on the basis of s t a t i s t i c a l reliability.
6. C O N C L U S I O N S Wear debris generated in FZG gear tests were analysed. It was found, and q u a n t i t a t i v e l y described with image analysis, that a range of different size and types of wear particles were generated. Six descriptive p a r a m e t e r s were used, and also wear debris were classified into six types according to the parameter values. In a prolonged pitting test, increase in the average diameter and roundness was indicative of increase in fatigue wear that lead to pitting failure in the further running. In increasing-load tests, relatively dark and large rough platelets were shown to be generated at latter stages under heavy loads, which could be a sign of the fatal scuffing damage to occur at a higher load. The present study showed the applicability of the image analysis approach to monitoring of
442
complicated sliding system as gears. Reliability of the method can be improved by introducing selective detection and more precise description of wear debris that are representative of the wear process that leads to heavier destructive failure.
ACKNOWLEDGEMENT The authors would like to thank Mr. M. Hashimoto for his help in conducting the particle analysis.
REFERENCES
I. AGMA, Nomenclature of Gear Tooth Failure Modes, ANSI/AGMA 110.04, 1980. 2. Ku, P. M., Gear Failure Modes - Importance of Lubrication and Mechanics, STLE Trans., 19, 3 (1975) 239-249. 3. Bowen B., Scott D., Seifert W. and Westcott V. C., F e r r o g r a p h y , T r i b o l o g y Intrn., 9 (1976) 109-I 15. 4. Uedelhoven W., Franzl M. and Guttenberger J., The Use of Automated Image Analysis for the Study of Wear Particles in Oil-Lubricated Tribological Systems, Wear, 142 (I 991) 107113. 5. Thomas A. D. H., Davis T. and Luxmoore A. R., C o m p u t e r I m a g e Analysis for Identification of Wear Particles, Wear, 142 (1991) 213-226. 6. Kirk T. B., Panzera D., Anamalay R. V. and Xu Z. L., Computer Image Analysis of Wear Debris for Machine Condition Monitoring and Fault Diagnosis, Wear, 181-183 (1995)
717-722. 7. R a a d n u e S. and R o y l a n c e B. J., The Classification of Wear Particle Shape, Lubr. Engrg., 51, 5 (I 995) 432-437. 8. Roylance B. J., Albidewi !. A., Luxmoore A. R. and Price A. L., The Development of a Computer-Aided Systematic Particle Analysis Procedure- CAPSA, Lubr. Engrg., 48, 12 (1992) 940-946. 9. Sugimura J. and Yamamoto Y., Development of Diagnostic System for Sliding Surfaces Part 1: Image Analysis of Wear Particles, Proc. JSLE Tribology Conf., Fukuoka, 1991, 365-368. 10. Sugimura J., Hashimoto M. and Yamamoto Y., being submitted. 11. Itoh T., Sugimura J. and Yamamoto Y., Prognosis of Scuffing Failure by Wear Debris Image Analysis, Proc. Intrn. Triboiogy Conf., Yokohama 1995, 193-198. 12. Sugimura J., Umeda A. and Yamamoto Y., W e a r Debris Identification with Neural Networks, J. JSME, Pt.C, 61, 590 (1995) 4055-4060. 13. Sugimura J., Umeda A. and Yamamoto Y., Diagnosis of Friction Conditions Based on Wear Debris Analysis, Proc. JAST Tribology Conf., Kitakyushu 1996-10, 377-379. 14. Haralick R. M., Shanmugan R. and Dinstein I., Textural Features for Image Classification, IEEE Trans. Syst. Man Cybern., SMC-3, 6 (1973) 610-621. 15. Zhang T. Y. and Suen C. Y., A Fast Parallel Algorithm for Thinning Digital Patterns, Comm. ACM, 27, 3 (1984) 236-239. 16. DIN 51354, Testing of Lubricants on the FZG-Gear Test Rig, 1964.