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Wear Condition Monitoring and Working PatternRecognition of Piston Rings and Cylinder Liners UsingOn-Line Visual FerrographWei Caoab, Wei Chena, Guangneng Donga, Jiaoyi Wua & Youbai Xiea

a Theory of Lubrication and Bearing Institute, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P.R. Chinab School of Mechanical and Electronic Engineering, Xi’an Technological University, Xi’an,Shaanxi 710032, P.R. ChinaAccepted author version posted online: 04 Apr 2014.Published online: 13 May 2014.

To cite this article: Wei Cao, Wei Chen, Guangneng Dong, Jiaoyi Wu & Youbai Xie (2014) Wear Condition Monitoring andWorking Pattern Recognition of Piston Rings and Cylinder Liners Using On-Line Visual Ferrograph, Tribology Transactions, 57:4,690-699, DOI: 10.1080/10402004.2014.906693

To link to this article: http://dx.doi.org/10.1080/10402004.2014.906693

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Tribology Transactions, 57: 690-699, 2014Copyright C© Society of Tribologists and Lubrication EngineersISSN: 1040-2004 print / 1547-397X onlineDOI: 10.1080/10402004.2014.906693

Wear Condition Monitoring and Working PatternRecognition of Piston Rings and Cylinder Liners Using

On-Line Visual FerrographWEI CAO,1,2 WEI CHEN,1 GUANGNENG DONG,1 JIAOYI WU,1 and YOUBAI XIE1

1Theory of Lubrication and Bearing Institute, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P.R. China2School of Mechanical and Electronic Engineering, Xi’an Technological University, Xi’an, Shaanxi 710032, P.R. China

This study applies stepped loading and stepped velocity ap-

proaches to simulate the actual working conditions of gasoline

engines. Accelerated wear tests were conducted for piston rings

and cylinder liners under different lubricating conditions by us-

ing a self-made pin-on-disc wear machine equipped with an

on-line visual ferrograph (OLVF) for wear monitoring. The

wear coefficients for oil monitoring were extracted to distin-

guish between constant conditions and stepped changing con-

ditions. A similarity model for oil monitoring was constructed

and the monitoring data sets of similar working conditions

were grouped together. Results show that the OLVF monitor-

ing system can be used to obtain the real-time variation in de-

bris concentration. The index of particle coverage area (IPCA)

of OLVF increases abruptly after the load or speed changes.

The similarity model can evaluate the similarity of the varia-

tion trend of IPCA under different operating conditions. The

relationship between IPCA and working conditions was exam-

ined in this study and provides an essential support to wear

monitoring and life prediction of engines.

KEY WORDS

Ferrography; Oil Condition Monitoring; Wear; Piston Rings;Cylinder Liners

INTRODUCTION

Significant wear of the piston ring and cylinder liner pair inengines results in high energy consumption, and monitoring wearof this friction pair in an actual engine environment is difficultand expensive. For this reason, many scholars have monitored thewear of piston rings and cylinder liners through laboratory tests.Ting and Mayer (1) developed an analytical model for simulatingthe wear in piston rings and cylinder liners on the basis of the Ar-chard’s equation. Truhan, et al. (2) studied the friction and wearbehavior of piston rings and cylinder liners under different lubri-cating and working conditions through laboratory experiments.

Manuscript received January 23, 2013Manuscript accepted March 17, 2014

Review led by by Martin PriestColor versions of one or more of the figures in the article can be

found online at www.tandfonline.com/utrb.

They found that the wear rate of piston rings and cylinder linersin engines under stepped loading conditions is higher than thatunder constant conditions. Jensen, et al. (3) simulated the wearbehavior of a piston ring and cylinder liner for marine diesel en-gines by using a pin-on-disc tester and clarified the mechanismsleading to the wear phenomenon known as “smearing and scuff-ing” in two-stroke marine diesel engines.

The wear loss of samples cannot be measured during weartests until the wear machine has stopped. However, oil monitor-ing technology can be used to detect wear debris in lubricants andto examine the wear characteristics of friction pairs during exper-iments (Ozogan and Khalil (4)). Macian, et al. (5) successfullyapplied analytical ferrography to detect failures in diesel enginefuel injection systems. The wear data currently available also in-volve the use of radiotracers. Schneider and Blossfeld (6) devel-oped a radiotracer method to measure the wear rates of pistonrings and cylinder bores in spark-ignition engines. The system issensitive and precisely detects nanometer-scale wear in a quanti-tative manner, thereby preventing excessive wear during the lifeof the engine. However, advanced radiation processing is neces-sary for this system, and those who use the equipment shouldhave undergone radiation safety training. Becker (7) analyzedwear debris captured by helicopter magnetic chip detectors andoil filters using an X-ray fluorescence instrument. The primaryadvantage of this instrument is the ability to determine the com-position of unidentified metallic debris without a practical uppersize limitation for wear debris. However, identification of the al-loying elements in smaller debris using this instrument is difficult.In fact, finely filtered systems of engines would separate largerdebris from the oil sample. Moreover, an annual radiation surveyis recommended.

On-line oil analysis technology can be used in the real-timemonitoring and prediction of wear behavior of equipment with-out subjecting the equipment to disintegration (Levi and Eliaz(8); Du and Zhe (9), (10); Craig, et al. (11); Booth, et al. (12)).An on-line visual ferrograph (OLVF), which was developed byXi’an Jiaotong University, has been used in the real-time wearmonitoring of both marine diesel engines and gasoline engines.The OLVF can provide real-time wear debris ferro-images,thereby allowing direct observation of wear debris. Wear debrislarger than 5 μm can be detected more efficiently (Zhang, et al.(13); Wu, et al. (14)). However, previous studies have paid

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Working Pattern Recognition 691

NOMENCLATURE

FN = Applied contact forceg = Sampling volume of OLVF within one

sampling period (mL)H = Hardness of the materialH1 = Hardness of the ring segmentH2 = Hardness of the gray cast iron discK = Relative wear coefficient for oil monitoringKOC = Wear coefficient for oil monitoringk1 = Wear coefficients of the ring segmentk2 = Wear coefficients of the gray cast iron discL = Number of segmentspi = Probability of the ith intervalq = Speed of oil supply within one sampling period is

denoted by (mL/s)

S = Cumulated sliding distances = Relative sliding velocityT = Length of the sampling period (s)tn = Experimental time of one segmentationV = Total wear volumeV0 = Volume of wear debris in the oil remnants of the last

sampling periodv (i) = Volume of wear debris in the oil pool during the current

sampling period�v = Total wear rate of the ring segment and cast iron disc�v1 = Wear loss of the ring segment during time dt�v2 = Wear loss of the gray cast iron disc during time dtβ(i) = Wear debris concentration of oil within the ith sampling

periodδ1, δ2 = Weight values

less attention to the relationship between the varying workingconditions and the index of particle coverage area (IPCA) ofOLVF.

Some scholars have investigated the relationship between tra-ditional oil monitoring data and the wear state of equipment.They pointed out that the wear debris concentration of a lubri-cant corresponds to its wear stage (Liu, et al. (15)). The wearstate of an engine could be determined by using the monitoringdata. Yan, et al. (16) extracted features from the statistical infor-mation of wear debris to identify the wear condition of a tribo-logical system and established a standard pattern library for wearmode. Laghari and Boujarwah (17) trained feedforward neuralnetworks using a reducing dimension cooccurrence matrix anddivided the debris texture into five types using the above neu-ral networks. By considering the incompleteness of data due tothe small sample size and the effect of the environment on oilmonitoring, Xu and Li (18) extracted key features and processeddata using wavelet packet, K-means clustering, and multivariablesynthesis analysis to obtain implicit information on oil monitor-ing data. Chen, et al. (19) developed the grey target theory andderived the trend features of oil monitoring data for wear moderecognition in systems.

In previous studies, the relationship between working condi-tions and the on-line monitoring results of the OLVF were notdiscussed in detail. Traditional pattern recognition methods arenot suitable for on-line ferrography. Moreover, accurately eval-uating the wear state of engines is difficult when using only onepattern recognition method.

To simulate the actual working condition of piston ringsand cylinder liners, accelerated wear tests were conductedunder stepped changing conditions in this study. The OLVFwas also used to monitor the debris concentration in lubri-cants. A similarity model was designed to research the implicitrelationship between working conditions and the monitoringresult of the OLVF. A suitable data processing method andpattern recognition method for the OLVF was developed. Thefindings in this article supply essential background materialand supporting documentation on wear monitoring and lifeprediction of engines as well as energy conservation and emissionreduction.

WEAR MONITORING TEST FOR RINGS AND CYLINDERLINERS

Test Equipment

Accelerated wear tests were performed on a self-made pin-on-disc wear machine. A controllable peristaltic pump was usedto supply oil with speed ranging from 0 to 50 rpm, and a siliconetube (inner diameter of 2.5 mm, external diameter of 4 mm) wasused as the oil pipe. The OLVF was connected to the oil pool ofthe tester, from which lubricating oil samples were collected pe-riodically to obtain the wear debris concentration of oil. Whenthe experiment was conducted, a self-made mixer was used toprevent the wear debris from settling in the oil pool. Figure 1aillustrates a schematic of the experimental setup. Experimentalring segments were obtained from an actual top ring cut into sev-eral segments, which had 100-μm-thick chromium coatings on asubstrate. The outer cylindrical surface of the ring segment hada hardness and surface roughness of HV 800 or more and (Ra)0.523, respectively. The substrate had hardness of over HV 400.Figure 1b shows a ring segment inserted in a holder. Liner sam-ples were machined from a flat gray cast iron specimen (HT250)in accordance with the materials of engines. The liner sampleshad diameters of 60 mm, thicknesses of 5 mm, and hardness ofHV 210.

The characteristics of a cylinder liner–piston ring combinationwere not completely matched, such as reciprocating sliding andtemperature. However, this did not influence the objective of thisstudy.

Experimental Common Running Conditions

An alternating load was adopted for the engine bench tests(Zhang, et al. (13)). Thus, in this study, more attention was paidto real-time wear monitoring under time-varying working condi-tions. Three working conditions were employed, namely, steppedloading, stepped velocity, and constant conditions, to evaluate thesensitivity of the OLVF to the real-time variation in load or ve-locity. In the wear tests, a heavier load than that typically used inother wear tests was adopted to accelerate wear progress. In addi-tion, the use of the flat cast iron counterface would provide stress

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692 W. CAO ET AL.

Fig. 1—Experimental setup: (a) schematic of test and (b) closer viewof the ring segment holder.

multiplications, which ensure accelerated wear within a reason-able test period.

The following lubricants were used in the tests: (a) liquidparaffin (analytical-reagent grade), (b) SAE 15W-40 SJ gasolineengine oil, and (c) SAE 15W-40 SJ gasoline engine oil mixed withsilica sand of 150 mesh and hardness of 7 Mohs (0.5%). In thisstudy, 0.5% is the mass percentage of the sand mixed with SAE15W-40 oil, and the oil flow rate of the peristaltic pump was set to1.7 mL/s. The viscosities of the lubricants are listed in Table 1.

Table 2 shows the control parameters for the OLVF monitor-ing system. Ferromagnetic debris in lubricants is deposited whenoil passes through the oil flow channel of the OLVF with highmagnetic fields. The IPCA was obtained by using the image pro-cessing software of the OLVF. The variation in IPCA could re-flect the real-time variation in ferromagnetic debris concentrationin lubricants. The IPCA is formulated as follows:

IPCA =∑

Ciwh

× 100.

TABLE 1—SPECIFICATION OF THE LUBRICANTS

Oil Properties SAE 15W-40 SJ Liquid Paraffin

Kinematic viscosity(mm2/s at 100◦C)

13 7

Density (kg/L) 0.86 0.846Flash point (◦C) 230 220

TABLE 2—CONTROL PARAMETERS FOR OLVF MONITORING

SYSTEM

Index

SamplingPeriod(h)

SamplingVolume(mL)

FlushingTime(min)

Flow Rate(mL/min)

MagnetomotiveForces (At)

Largerparticles

0.15 6 0.5 2 800

Smallerparticles

0.15 6 0.5 2 1,100

As shown in Fig. 2, w and h denote the width and height of theimage, respectively, and

∑Ci denotes the object pixel number of

wear debris in the segmented image (Wu, et al. (14)).Tests were conducted at room temperature (20–25◦C) with

relative humidity of 65%. Prior to testing, the surface of thedisc specimen was ground with 600 grit SiC paper, and the ringsegment and gray cast disc were rinsed in acetone. The surfaceroughness of the cast iron liners is show in Table 3. Table 3 alsolists the lubricants, loading, and velocity used for each test.

EXPERIMENTAL RESULTS AND DISCUSSION

Test under Stepped Loading Conditions

Two 480-min tests were conducted under stepped loading con-ditions, and the load was stepped up every 120 min. The effectsof different lubricants on wear progression were compared. Thestepped loading process and the IPCA for test SL1 are shown inFig. 3a. The specimen lubricated with liquid paraffin in test SL1shows a serious wear scar, which is verified by the sudden increasein IPCA. Engine oil offers good lubrication, thus impeding wearprogression. Therefore, a heavier load was used in test SL2 thanin test SL1 to accelerate wear in the friction pairs and to increasethe IPCA (see Fig. 3b).

Previous research (Truhan, et al. (2)) indicated that steppedloading accelerates the wear of specimens, which agrees with theresults in this study. Figure 3 shows that under stepped loadingconditions, the IPCA increases abruptly with each load increase.

Test under Stepped Velocity Conditions

Three 360-min tests were conducted under stepped velocityconditions, and sliding velocity was stepped up every 120 min. Intest SV1, liquid paraffin was used as the lubricant to accelerate

Fig. 2—Definition of the IPCA.

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Working Pattern Recognition 693

TABLE 3—PARAMETERS AND FEATURES FOR WEAR TESTS

Working Surface Roughness Velocity LoadConditions Test Lubricants of the Disc (Ra) (m/s) (N) Sgnk(x)

Stepped loading SL1 Liquid paraffin 0.65 0.785 Stepped load −1SL2 SAE 15W-40 SJ 0.96 1.57 Stepped load −1

Stepped velocity SV1 Liquid paraffin 0.89 Stepped velocity 75 −1SV2 SAE 15W-40 SJ 0.73 Stepped velocity 75 −1SV3 SAE 15W-40 SJ

mixed withsilica sand

0.92 Stepped velocity 62 −1

Constant conditions M SAE 15W-40 SJ 0.84 1.57 75 +1

the wear process. The process of stepped velocity and the IPCAfor test SV1 are shown in Fig. 4a. Figure 4a shows that IPCA hasa sudden increase when the relative sliding velocity increased.Figure 4b shows the process of stepped velocity and the IPCAfor test SV2. Gasoline engine oil was used as the lubricant in testSV2; thus, no apparent wear was generated, and a small IPCAwas observed. Likewise, the amplitude of the sudden increase inthe value of IPCA, which was caused by stepped velocity, is small.In test SV3, high-hardness silica sand was added to engine oil toaccelerate wear. Figure 4c shows the process of stepped velocityand the IPCA for test SV3. Significant wear on the surface of fric-tion pairs was generated, resulting in higher debris concentrationin the oil; thus, the amplitude of the sudden increase of IPCA islarge.

Fig. 3—Stepped loading process and IPCA for (a) test SL1 and (b) testSL2.

Test under Constant Conditions

For comparison, a 360-min experiment (test M) under con-stant conditions was conducted. Figure 5 shows the experimentalresults of test M. Under constant conditions, the IPCA increasesabruptly and then decreases until stable. Although a few smallpeaks are present in the OLVF monitoring curves, the overall

Fig. 4—Process of stepped velocity and IPCA for (a) test SV1; (b) testSV2; and (c) test SV3.

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Fig. 5—PCA for test M.

trend of IPCA is evident; thus, the small peaks can be ignored asnoise.

Discussion

The load or speed increments for each test were different.However, the change patterns of each test were same. When com-paring the IPCA data sets of different tests, what is actually com-pared is not the absolute value of IPCA data sets but the tendencyand variation in IPCA data sets. Thus, the different sets of exper-iments can be compared with each other.

A comparison of the Figs. 3, 4 and 5 shows that IPCA has in-creased abruptly during the first 60 min of every test and thendecreased gradually until stable. This trend is consistent with thevariation in wear rate of the specimen; that is, the primary run-inwear stage transformed into the steady mild wear stage in everytest. The sudden increase in IPCA under stepped changing con-ditions increases the initial trend of IPCA under constant condi-tions, which then decreases gradually until stable.

Stepped loading and stepped velocity accelerate the wear pro-cess in tests. However, the relative amplitude of a sudden increasein IPCA under stepped velocity conditions is smaller than thatunder stepped loading conditions. This observation indicates thatthe variation in relative sliding velocity has less effect on the wearstate of piston rings and cylinder liners compared to the effect ofvariation in applied load. This phenomenon has been discussedin previous research (Stolarski (20)).

The OLVF can monitor the real-time variation in load or ve-locity because changes in load and velocity vary the wear rate,thereby further increasing the wear debris concentration in oil.The sudden increase in IPCA lags behind the change in work-ing conditions. The reason behind this phenomenon is that theOLVF collects oil sample from the oil pool, but time is neededfor the oil to flow from the surface of friction pairs into theoil pool; thus, the increase in debris concentration in the oilpool lags behind the changes in load and velocity. Consequently,the sudden increase in IPCA lags as well. However, this lagdoes not significantly affect the overall trend of the monitoringcurve.

Each Time after the IPCA increases abruptly, it quickly de-creases to a lower value again. The reason for this phenomenonis that when the load or velocity increases, the destruction of theoriginal stable wear state of the friction pair results in an increase

Fig. 6—Comparison of wear volume and IPCAsum in different test.

in IPCA, whereas after a short run-in process, the wear state ofthe friction pairs will return to its stable state.

Study of Worn Samples

During postinspection of the samples, confocal microscopywas used to observe the surface morphology of the samples andmeasure the wear scar width. The wear volume of each test wasdetermined with the wear volume models of the ring and liner(Truhan, et al. (2)). At the same time, the sum of the IPCA value(IPCAsum) in each test was calculated.

The wear volume of the different tests is plotted and joinedinto a curve. In addition, the IPCAsum of the different tests isplotted and joined into a curve. As Fig. 6 illustrates, the IPCAsumof a test increases with increasing wear volume. We can concludethat a positive correlation exists between the IPCAsum and thetotal wear volume.

The cast iron flat showed considerably more weight loss thanthe ring because the former is softer than the plasma-sprayed Crplating of the ring segment. Figure 6 shows that test SV3 has thelargest wear volume, whereas test SL1 has the smallest. The sur-face morphology of the discs from test SL1 and test SV3 was ob-served via confocal microscopy, as shown in Figs. 7a and 7b. Aploughing effect can be observed on the surface of the sample.Abrasive wear and adhesive wear are the major wear modes.

As Fig. 7a indicates, although paraffin oil was used as a lu-bricant in test SL1, the wear scar width is smaller and the wearis not significant. This result can be attributed to the relativelysmall load and velocity. The gasoline engine oil was mixed withsilica sand in test SV3. As shown in Fig. 7b, the wear scar widthis larger and the plouging effect as well as the wear damageare more serious. During friction, the hard particles in the lubri-cant can be embedded into the friction surface, thereby causingabrasive wear. The rolling and scratching of the hard particleson the friction surface further produce local plastic deformation,which causes direct contact between the metal and exacerbatesadhesion wear.

DATA PROCESSING FOR WEAR MONITORING TEST

Wear Feature Extraction for Oil Monitoring

To evaluate the effects of load and speed on the wear stateunder stepped changing conditions, data sets obtained duringthe first 360 min of the experiment were selected for data

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Working Pattern Recognition 695

Fig. 7—Surface morphology of the disc for (a) test SL1 and (b) test SV3.

processing. Each experimental data set was divided into L seg-ments, where L = 3. For each data segment, the sliding veloc-ity and load applied during the test were definite values, and thewear state belonged to the same wear stage.

Previous research indicated that the working conditions affectwear loss (McColl, et al. (21)). Varying the sliding velocity or loadchanges the wear rate and debris concentration of oil, therebychanging the value of IPCA. The relative wear coefficient for oilmonitoring (KOC) can be used to calculate the wear loss of test-ing materials. A detailed calculation for KOC can be seen in theAppendix.

KOC is not constant because the IPCA of the different seg-ments came from different working conditions and belonged todifferent wear stages. Considering that each test was completedwithout breaks, the wear coefficient (KOC) of the different seg-ments in each test is plotted and joined into a coefficient curve.Figure 8a shows a comparison of KOC under stepped loading con-ditions, and Fig. 8b presents a comparison of KOC under steppedvelocity conditions. The KOC values of test SV1 and test SV3 areextremely large, which make the wear coefficient trend for testSV2 and test M appear flat. Thus, the inset in Fig. 8b was plottedto indicate the trend for test SV2 and test M, where the Y-axis issmall enough.

Fig. 8—Comparison of the KOC of different test curves: (a) stepped load-ing and (b) stepped velocity.

For comparison, data segmentation was also performed forthe IPCA of test M. Although the load and relative sliding ve-locity are definite values under constant conditions, different seg-ment of test M belong to different wear stages; thus, the KOC ofthe different segments under constant conditions is different.

Figure 8 shows that the KOC is larger during the first segments(0–120 min) of all tests than during other segments, which indi-cates that the first segment belongs to the run-in stage. The KOC

of test M decreases during the second (120–240 min) and third(240–360 min) segments in all tests, which indicates that the wearrate decreases and then tends to be stable under constant condi-tions. Under stepped changing conditions, the KOC of the secondand third segments increases slightly because the stepped chang-ing of load or the relative sliding velocity results in a variation inthe wear rate. The wear scar on the disc deepens, denoting thatthe wear rate of the piston rings and cylinder liners is high.

Under different working conditions, the variation trend ofKOC is different. Thus, the attribute feature is derived to iden-tify qualitatively the working conditions on the basis of the KOC

of each segment. Let sgnk(x) be the attribute feature.

sgnk(x) =L−1∏i=1

KOC (i + 1) − KOC (i)∣∣KOC (i + 1) − KOC (i)∣∣ . [1]

An increase in the load or relative sliding velocity understepped changing conditions results in increased KOC of the lasttwo segments and results in changes in the KOC trend. Accordingto Eq. [1], a negative sgnk(x) is present in the test under steppedchanging conditions, whereas under constant conditions, the wearrate decreases and then tends to stabilize, and the changes in

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the KOC trend of the last two segments are consistent. Thus, apositive sgnk(x) is present in the test under constant conditions,and the attribute feature sgnk(x) can be expressed as

sgnk(x) ={

1, constant conditions−1, stepped changing conditions

. [2]

The attribute feature sgnk(x) can be used to differentiate con-stant conditions from stepped changing conditions. However, thefurther subdivision of IPCA under stepped changing conditionsis also necessary.

Comprehensive Relative Amplitude Coefficient

A comparison of Figs. 3 and 4 denotes that the relative am-plitude of sudden increasing points under stepped loading con-ditions is greater than that under stepped velocity conditions.Therefore, the relative amplitude of IPCA at sudden transitionpoints is related to the working conditions during an experiment.The comprehensive relative amplitude coefficient is constructedto describe the amplitude of IPCA at transition points. The cal-culation steps are as follows:

Step1: Divide the 360 min experimental dataset X =(X1X2, . . . Xn) into L segments by using the segmentationmethod presented in last section, the dataset is then expressedas Eq. [3], where, f is the number of segments, f = 1, 2, 3; j isthe serial number of the dataset of each segment.

X (fj ) =

⎧⎪⎨⎪⎩

X (11) X (12) . . . .. X (1r)X (21) X (22) . . . .. X (2r)X (31) X (32) . . . .. X (3r)

⎫⎪⎬⎪⎭ j = 1, 2, . . . r [3]

New datasets X′ can be obtained by subtracting the average ofIPCA of each segment (X(f )) from the monitoring data.

X′ (fj ) = X (fj ) − X(f ) [4]

The relative amplitude coefficient of the f th segmentation (yf ) iscalculated.

yf =(

max1≤j ≤n

X′ (fj ) − min1≤j ≤n

X′ (fj ))

/X(f ) [5]

Step2: Calculate the comprehensive relative amplitude coeffi-cient (Y) for the entire 360 min experiment.

∑Y = 1

L

∑L

f =1yf [6]

For comparison, let Y of the constant condition be equal to zero.Step3: Calculate the difference between the Y values of different

tests (DY(X1, X2)).

DY(X1, X2) = |Y1 − Y2| [7]

For tests under varying conditions, DY(X1, X2) can be used tocompare the similarity of the amplitude of IPCA at transitionpoints.

Entropy of the Slope of Curves

Previous studies showed that the slope of IPCA curve, i.e., thevariation rate of IPCA, is proportional to the wear rate (Wang(22)). If the variations in load and speed are similar, the wear

states of the piston rings and cylinder liners are also similar. Con-sequently, the geometry of the experimental curve and the rela-tive slope of the monitoring curves in each period are also similar.Then the entropy of the slope difference curve (SRent) is calcu-lated to evaluate the geometric similarity between two monitor-ing curves. The calculation steps are as follows:

Step 1: Compute the relative slope of each point on thecurve(SR).

SR (j ) = x (j + 1) − x (j )x (j + 1)

j = 1, 2, . . . , n − 1. [8]

The relative slope curve (SRC) is constructed based on SR (j ):

SRC = (SR1, SR2, . . . SRn−1) .

Step 2: The slope difference curve (DSRC) is generated based onthe difference between curves SRC1 and SRC2 point by point.DSRC is a line with slope close to zero when two curves havesimilar geometries; otherwise, DSRC is a curve with significantfluctuation.

Step 3: Divide DSRC into m equal intervals. From the first to themth interval, count the point number belonging to the ith in-terval and determine the probability (pi) of the ith interval.

Step 4: Compute SRent according to Eq. [9]. The Shannon en-tropy, a measure of uncertainty, is adopted to quantitativelydescribe the difference between two curves. The greater SRentis, the more significant the difference between two curves be-comes. When two curves have identical geometries, SRent isequal to zero.

SRent = −m∑

i=1

pi log pi. [9]

Clustering Framework for the OLVF

On the basis of the amplitude feature and the entropy of theslope difference curve, the similarity model for any two monitor-ing curves of the OLVF (X1, X2) was developed.

sim(X1X2) = [δ1∗DY(X1, X2) + δ2 ∗ SRent] . [10]

Here, weight values δ1 + δ2 = 1. If sim(X1X2) is close to zero,then the type of working conditions are similar. Based on thevalue of sim(X1X2), further subdivision between the monitoringcurves under stepped loading conditions and that under steppedvelocity conditions can be conducted, so sim(X1X2) is also calleda numerical feature.

Present features are utilized to establish a clustering frame-work for the clustering and analysis of IPCA. The implementa-tion steps are as follows:

Step 1: Data preprocessing: Calculate sgnk(x) , Y, andSRent.Step 2: Preliminary classification: Differentiate constant condi-

tions from stepped changing conditions according to the pos-itive and negative values of sgnk(x).

Step 3: Final classification:Randomly select a monitoring data set under stepped changing

conditions as the initial center of class 1, named C1; calculate

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Working Pattern Recognition 697

Fig. 9—Clustering results of experimental data: (a) preliminary classifi-cation and (b) final classification.

the similarity value (sim1) between data sets C1 and other datasets.

Select the experimental data set with the largest sim1 as the cen-ter of class 2, named C2; compute the similarity value (sim2)between data set C2 and other data sets.

Compare sim1 and sim2 of each experimental data set. The classcenter that has the smaller sim with the experimental data set isselected as the object, and the experimental data set is classifiedinto object class.

Compute parameter MY, which is the mean value of parameterY for one class. Thus, the class with the larger MY is identifiedas the test under stepped loading conditions and the class withsmaller MY as the test under stepped velocity conditions.

EXPERIMENTAL VALIDATION

The attribute features sgnk(x) of each wear test are calcu-lated and listed in Table 3. Preliminary classification is performedto distinguish between constant conditions and stepped changingconditions, as shown in Fig. 9a.

TABLE 4—SL AND SV TEST COMPREHENSIVE RELATIVE

AMPLITUDE COEFFICIENTS

y1 y2 y3 Y

SL1 4.777 6.577 3.829 4.892SL2 10.955 6.79569 1.492275 5.159651SV1 2.16 2.63 1.46 2.08SV2 6.5 1.995 1.353 3.28SV3 0.902493 3.796466 2.661645 2.453534

TABLE 5—SIMILARITY BETWEEN ANY TWO TEST CURVES

SL1 SL2 SV1 SV2 SV3

SL1 0 0.3677 1.5353 1.0399 1.2927SL2 0 1.8535 1.2058 1.5378SV1 0 0.953 0.8690SV2 0 0.7339SV3 0

The coefficient Y is calculated for each experimental data set,and the results are listed in Table 4. Table 4 shows that the coef-ficient Y of the test under stepped loading conditions (SL1, SL2)is greater than that under stepped velocity conditions (SV1, SV2,SV3). For two tests belonging to the stepped loading conditionand stepped velocity condition, respectively, the DY(X1, X2) isgreater than that under similar stepped changing conditions.

The similarity (sim) between any two test curves is computedand listed in Table 5, where the weight values (δ1, δ2) are equalto 0.5. Table 5 shows that for any two experimental data sets be-longing to similar time-varying test conditions, sim is less than 1,whereas for any two experimental data sets belonging to differ-ent types of time-varying conditions, sim is greater than 1. A lowsimilarity indicates similar testing conditions. On the basis of theclustering method and the computation results, the final cluster-ing results are shown in Fig. 9b.

Figure 9 indicates that the recognition result of the clusteringmethod presented in this article is consistent with the actual test-ing conditions; that is, this clustering algorithm is effective for theclassification of actual wear monitoring results and the identifica-tion of working conditions.

As mentioned above, the sudden increase in IPCA lags behindthe changes in load and velocity. When the similarity comparisonwas conducted, trend characteristics were compared between thetwo experimental curves. Thus, the lag would not affect the recog-nition results.

CONCLUSION

For wear tests under different working conditions, the OLVFmonitoring system can be used to obtain the real-time variation indebris concentration. The IPCA increases abruptly after the loador velocity changes. This change in IPCA shows a slight lag. Theamplitude of IPCA at a sudden increasing point under steppedvelocity conditions is smaller than that under stepped loadingconditions.

The wear coefficients for oil monitoring, as well as the at-tribute features, are obtained to distinguish between constantconditions and stepped changing conditions. The wear coeffi-cients for oil monitoring decline gradually and then tend to bestable under constant conditions, whereas the wear coefficientsfor oil monitoring under stepped changing conditions tend to de-cline first before increasing slightly.

The similarity model developed in this study can accuratelyevaluate the similarity of the variation of IPCA data set underdifferent time-varying working conditions. Consequently, a clus-tering framework for the on-line wear monitoring and wear stateassessment of gasoline engines can be established by considering

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698 W. CAO ET AL.

the influence of lubricants and by further optimizing the cluster-ing algorithm.

ACKNOWLEDGEMENTS

We thank Dr. Tonghai Wu and YaLi Zhang at Xi’an JiaotongUniversity for their help in performing experiments and writingthis article.

FUNDING

This work was partly supported by the National Science Foun-dation of China (Grant Nos. 50935004, 51275381) and the Scien-tific Research Program Funded by Shaanxi Provincial EducationDepartment (Program No. 12JK0676).

REFERENCES(1) Ting, L. L. and Mayer, J. E. (1974), “Piston Ring Lubrication and Cylinder

Bore Wear Analysis, Part I—Theory,” Journal of Lubrication Technology,96(3), pp 305–313.

(2) Truhan, J. J., Qu, J., and Blau, P. J. (2005), “A Rig Test to Measure Frictionand Wear of Heavy Duty Diesel Engine Piston Rings and Cylinder LinersUsing Realistic Lubricants,” Tribology International, 38, pp 211–218.

(3) Jensen, M. F., Bottiger, J., Reitz, H. H., and Benzon, M. E. (2002),“Simulation of Wear Characteristics of Engine Cylinders,” Wear, 253,pp 1044–1056.

(4) Ozogan, M. S. and Khalil, A. I. (1989), “Tribological Failure Detection andCondition Monitoring for Diesel Engines,” Wear, 130(1), pp 189–201.

(5) Macian, V., Payri, R., Tormos, B., and Montoro, L. (2006), “Applying An-alytical Ferrography as a Technique to Detect Failures in Diesel EngineFuel Injection Systems,” Wear, 260, pp 562–566.

(6) Schneider, E. W. and Blossfeld, D. H. (2003), “Radiotracer Method forMeasuring Real-Time Piston–Ring and Cylinder-Bore Wear in Spark-In Engines,” Nuclear Instruments and Methods in Physics Research, 505,pp 559–563.

(7) Becker, A. (2008), “Application of an X-ray Fluorescence Instrument toHelicopter Wear Debris Analysis,” Defence Science and Technology Or-ganisation (Australia), 1, pp 15–17.

(8) Levi, O. and Eliaz, N. (2009), “Failure Analysis and Condition Monitor-ing of an Open-Loop Oil System Using Ferrography,” Tribology Letters,36(1), pp 17–29.

(9) Du, L. and Zhe, J. (2012), “Parallel Sensing of Metallic Wear Debris in Lu-bricants Using Undersampling Data Processing,” Tribology International,53, pp 28–34.

(10) Du, L. and Zhe, J. (2011), “A High Throughput Inductive Pulse Sen-sor for Online Oil Debris Monitoring,” Tribology International, 44(2),pp 175–179.

(11) Craig, M., Harvey, T. J., Wood, R. J. K., Masuda, K., Kawabata, M., andPowrie, H. E. G. (2009), “Advanced Condition Monitoring of TaperedRoller Bearings, Part 1,” Tribology International, 42, pp 1846–1856.

(12) Booth, J. E., Nelson, K. D., Harvey, T. J., Wood, R. J. K., Kawabata,M., and Powrie, H. E. G. (2006), “The Feasibility of Using ElectrostaticMonitoring to Identify Diesel Lubricant Additives and Soot Contamina-tion Interactions by Factorial Analysis,” Tribology International, 39(12),pp 1483–1756.

(13) Zhang, Y. L., Mao, J. H., and Xie, Y. B. (2011), “Engine Wear Monitoringwith OLVF,” Tribology Transactions, 54(2), pp 201–207.

(14) Wu, T. H., Mao, J. H., Wang, J. T., Wu, J. Y., and Xie, Y. B. (2009), “A NewOn-Line Visual Ferrograph,” Tribology Transactions, 52(5), pp 623–631.

(15) Liu, Y., Liu, Z., Xie, Y. B., and Yao, Z. G. (2000), “Research on an On-Line Wear Condition Monitoring System for Marine Diesel Engine,” Tri-bology International, 33, pp 829–835.

(16) Yan, X. P., Xie, Y. B., Li, X. F., and Xiao, H. L. (1996), “Predictive Modeland Experiment for Wear of 8NVD48A 2U Diesel Engine,” Tribology,16(4), pp 358–366 (in Chinese).

(17) Laghari, M. S. and Boujarwah, A. (1999), “Wear Particle Texture Classifi-cation Using Artificial Neural Networks,” Pattern Recognition and Artifi-cial Intelligence, 13(3), pp 415–428.

(18) Xu, Q. S. and Li, Z. G. (2007), “Recognition of Wear Mode Using Multi-Variable Synthesis Approach Based on Wavelet Packet and ImprovedThree-Line Method,” Mechanical Systems and Signal Processing, 21(8),pp 3146–3166.

(19) Chen, S. W., Li, Z. G., and Xu, Q. S. (2006), “Grey Target Theory BasedEquipment Condition Monitoring and Wear Mode Recognition,” Wear,260, pp 438–449.

(20) Stolarski, T. A. (1979), “Abrasive Wear of Lubricated Contacts,,” Tribol-ogy International, 12(4), pp 169–179.

(21) McColl, I. R., Ding, J., Leen, S. B., et al. (2004), “Finite ElementSimulation and Experimental Validation of Fretting Wear,” Wear, 256,pp 1114–1127.

(22) Wang, J. T (2006), Theoretical and Experimental Study on Online Visu-alization Ferrograph Sensor, Doctoral Thesis, Xi’an Jiaotong University,China (in Chinese).

APPENDIX: CALCULATION OF RELATIVE WEARCOEFFICIENT FOR OIL MONITORING

Given that the variation in working conditions affects the wearcoefficient, the relative wear coefficient for oil monitoring is de-rived to calculate the wear loss of testing materials by using thewear model of mixed lubrication:

VS

= KFN

H. [A1]

Let the relative sliding velocity be �s = dSdt , then Eq. [A1] can

be expressed by

dvdt

= KFN

H�s. [A2]

The total wear rate �v of the ring segment and cast iron disccan be expressed by

�v = dvdt

= �v1 + �v2 =(

k1

H1+ k2

H2

)FN�s. [A3]

Let KM denote the comprehensive wear coefficients of onedata segmentation:

KM =(

k1

H1+ k2

H2

)= �v/FN�s [A4]

Under the same working conditions, coefficients KM are thesame. Thus, when FN and �s increase, the abrasive generationspeed of friction pairs also increases.

For this experiment, remnants of the last sampling period arepresent because the peristaltic pump had supplied oil before thewear test began. Let the volume of residual oil W0 (mL) be equalto a constant value, then the oil volume in the oil pool within eachsampling period W(mL) can be expressed as

W = W0 + (q ∗ T − g) . [A5]

The volume of wear debris in the oil pool during the currentsampling period can be expressed as

v (i) = V0 +T∫

0

�vdt = V0 +T∫

0

KMFN�sdt. [A6]

Considering that V0 is very small, this variable can be replacedby a random variable γ with mean-zero Gaussian distribution andγ is on the positive half-axis of Gaussian distribution. Thus, v (i)can also be expressed as

v (i) =T∫

0

KMFN�sdt + γ (i) . [A7]

Here, Z sampling periods exist in a single segmentation. Thetotal volume of wear debris in the oil within one segmentation

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Working Pattern Recognition 699

can be expressed as

Vsum =Z∑

i=1

⎛⎝ T∫

0

KMFN�sdt + γ (i)

⎞⎠ . [A8]

For the same monitoring data segment, the wear state belongsto the same wear stage, and the load, speed, and lubricants arethe same. Given that the random variable γ (i) in the last item inEq. [A8] is very small or close to zero, then Vsum can also bewritten as

Vsum =Z∑

i=1

⎛⎝ T∫

0

KMFN�sdt

⎞⎠ = ZKMFN�sT. [A9]

Regardless of the cumulative effect of the wear debris,thewear debris concentration of oil within the ith sampling periodcan be expressed as

β (i) = v (i) / (W0 + q ∗ T − g) =∫ T

0 KMFN�sdt

W0 + q ∗ T − g+ γ (i)

W0 + q ∗ T − g.

[A10]

Ignoring the last item in Eq. [A10], the wear rate �v and β (i)within a sampling period show a linear relationship.

The total wear volume of Z sampling period (Vsum) can becalculated using β (i) through Eq. [A11]. The monitoring data ofthe OLVF during each sampling period are IPCA (t). The sumof the IPCA (t) after time tn under same working conditions isIPCAsum and can be expressed as Eq. [A12].

Vsum =tn∫

0

β (i) ∗ Wdt = ZKMFN�sT [A11]

IPCAsum =tn∫

0

IPCA (t) dt. [A12]

Previous experimental studies showed that IPCA (t) is influ-enced by β (i) within a sampling period; that is, IPCA (t) has apositive correlation with β (i). Equation [A10] denotes that β (i) isprimarily determined by load, relative sliding velocity, and wearcoefficient. Thus, IPCA (t) is also associated with load and rela-tive sliding velocities.

On the basis of the previous study that IPCA (t) is positivelycorrelated with β (i) and by combining Eqs. [A11] and [A12],it can be inferred that a positive correlation exists between thesum of the oil monitoring data (IPCAsum) and total wear volume(Vsum) within a single segment. Thus, similar to the KM in Eq.[A4], the wear coefficient for oil monitoring(KOC) was derivedfrom the IPCA. Vsum in Eq. [A11] is replaced by IPCAsum, andthe new coefficient K is determined by Eq. [A13]. Given that Zand T are definite values and KOC = k∗Z∗T, KOC, is determinedusing Eq. [A14], which indicates the relationships of IPCAsumand FN and �s.

K = IPCAsum(Z ∗ T∗�s)FN

[A13]

KOC = IPCAsum�sFN

= ∫tn0 IPCA (t) dt

�sFN. [A14]

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