Download pdf - Application of dielectric spectroscopy for engine lubricating oil degradation monitoring

Am

LO

a

ARRAA

KEOF(DM

1

IntddvateCot(amcomadZ

0d

Sensors and Actuators A 168 (2011) 22–29

Contents lists available at ScienceDirect

Sensors and Actuators A: Physical

journa l homepage: www.e lsev ier .com/ locate /sna

pplication of dielectric spectroscopy for engine lubricating oil degradationonitoring

. Guan ∗, X.L. Feng, G. Xiong, J.A. Xieil Application and Management Department, Logistical Engineering University, Shapingba District, Chongqing 401311, China

r t i c l e i n f o

rticle history:eceived 29 September 2010eceived in revised form 15 March 2011ccepted 15 March 2011vailable online 23 March 2011

a b s t r a c t

In this paper, Dielectric Spectroscopy (DS) was employed to analyze the oxidation degradation process ofengine lubricating oil qualitatively and quantitatively compared with Fourier Transform Infrared Spec-troscopy (FTIR). It was found that both DS and FTIR can directly obtain the degradation features fromthe spectral data. With the combination of DS and multivariate calibration (Partial Least Square PLS),three main oil monitoring properties including Oxidation Duration (OD), Total Acid Number (TAN) and

eywords:ngine lubricating oilil monitoringourier transform infrared spectroscopyFTIR)

Insoluble Content (IC) can be determined quantitatively and accurately. It was proved that operatingtemperature had more influence on DS data than excitation amplitude. The results in the article showthat DS can be developed into an effective oil monitoring/analysis method.

© 2011 Elsevier B.V. All rights reserved.

ielectric spectroscopy (DS)ultivariate calibration

. Introduction

Lubricating oil plays a key role in internal-combustion engines.t consists of complex mixtures of hydrocarbons and is a combi-ation of base oils and additives [1]. Engine lubricants are usedo reduce the frictions of the mobile components and to keep theifferent elements clean, being able to work as detergents andispersant agents. The engine lubricating oil ageing process is aery complex process during which degradation of the base oilnd depletion of its additives take place simultaneously. Oxida-ive high temperature degradation and contamination by water,thylene glycol, fuel, soot, and wear metals are the main factors.urrently, the main methods for determining engine lubricatingil condition are the routine physical & chemical tests to assesshe properties including kinematic viscosity, Total Acid NumberTAN), Total Base Number (TBN) and Insoluble Content (IC), whichre always time-consuming, laborious and require specific equip-ent for the determination of each property of interest. In this

ontext, kinds of efficient alternative methods have been devel-ped. Ferrography [2–5] is currently the prevalent and effective
ethod to evaluate the wear ferromagnetic particles. Analytical
nd direct reading (DR) ferrography are the two main types. As foretermination of wear metals such as Na, Mg, Al, Ca, Ti, V, Cr, Cu,n, Mo, Ag, and Cd. Laser-induced breakdown spectroscopy (LIBS)

∗ Corresponding author. Tel.: +86 023 86730900.E-mail address: gl [email protected] (L. Guan).

924-4247/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.sna.2011.03.033

[6,7], atomic spectroscopy [8,9], X-ray fluorescence spectroscopy[10] are already introduced respectively. Visible spectrophotomet-ric detection in association with flow injection analysis (FIA-visiblespectrometry) is proposed and employed to determinate the insol-uble content [11]. In particular, middle infrared spectroscopy (MIR)has been largely applied [1,11–17], which offers several advantagesfor this type of application, such as nondestructive nature.

Recently, much attention has been paid to dielectric and elec-trochemical impedance properties of industrial lubricants. Lvovichand Schmiechowski [18] have discussed the relationship betweenchemical composition of lubricants and their electrochemicalproperties obtained by means of electrochemical impedance spec-troscopy (EIS). The non-linear impedance analysis of industriallubricants has also been performed by higher harmonic non-linearelectrochemical impedance spectroscopy (NLEIS) [19]. EIS andNLEIS are able to offer the opportunities to characterize, evalu-ate and provide insights into chemical composition, changes andmechanisms of lubricants. Wang and Lee [20,21] have used a.c.impedance technique to detect glycol contamination in engine oil.And a new technique to detect minor antifreeze in engine oil bymeasuring the changes of engine oil resistance was introduced too.EIS has been used to estimate soot and diesel contamination inengine oil simultaneously [22]. Oil condition sensors [23–27] based
on dielectric constant and conductivity measurement of the enginelubricating oil have been designed and fabricated. These sensorscan detect the relative variation of lubricant degradation. Wang[28] has established a good correlation between TAN and this kindof sensor’s output.
dx.doi.org/10.1016/j.sna.2011.03.033

http://www.sciencedirect.com/science/journal/09244247

http://www.elsevier.com/locate/sna

mailto:[email protected]

dx.doi.org/10.1016/j.sna.2011.03.033

L. Guan et al. / Sensors and Actuators A 168 (2011) 22–29 23

of sa

radco

tepWear�sooem

chpcswabamcser

TI

Fig. 1. Schematic diagram

Dielectric and electrochemical impedance analysis methods areelatively fast, simple, inexpensive and free from the difficultiesssociated with the current standard testing methods. Moreover,ielectric and electrochemical properties are related with thehemical composition and physical structures of engine lubricatingil.

Dielectric spectroscopy (DS) is an analytical technique onhe interaction between dielectric material and electromagneticnergy in the radio-frequency and microwave range, which is aowerful structural detection technique for dielectric materials.hat DS studies is the dependence of materials’ dielectric prop-

rties on wavelength or frequency. The difference between DSnd EIS: the main interest of DS is on the intrinsic electric mate-ial properties. The complex permittivity ε*(ω) or conductivity*(ω) spectra can be easily evaluated from Z*(ω) with the help ofample dimensions; the focus of EIS is mostly on the propertiesf electrode/material interfaces and the materials under test areften electrolytes or ion conductors. Petroleum products includingngine lubricating oil are all typical dielectric materials. So DS isore suitable than EIS for engine lubricating oil analysis.At present, DS technique enables researchers to make sound

ontributions to contemporary problems in modern physics. DSas been employed to quality sensing application of agriculturalroduct [29,30]. Our previous publications have investigated theorrelations between DS data and petroleum products’ compo-ition and quality indexes by means of multivariate calibration,hich include classification of virgin engine lubricating oils by SAE

nd source [31] and determination of clean gasoline octane num-ers [32]. It is already proved that DS is a practical and effectivenalysis method to obtain rich composition and structure infor-ation of complex mixture systems. Especially, with the help of

hemometrics multivariate calibration methods the direct relation-hips between DS data and quality properties can be establishedfficiently, which is more effective than the interpretation of EISesults by means of complex equivalent circuit (EC) models. The

able 1nformation on three virgin engine lubricating oils.

Name API SAE

Set one CD 15 W/40Set two CD-SE 50Set three SJ 15 W/40

mple preparation device.

new analysis idea is explained in our previous publication too[32].

The monitoring sensor for oil condition, especially for enginelubricating oil should be easy to be cleaned and is able to extractsufficient dielectric information. Interdigitated comb capacitor sen-sor is a good and practical selection, which has been applied foroil analysis widely [27,31–33]. So the measuring sensor for enginelubricating oil in this article is a type of interdigitated comb capac-itor sensor too.

In this article, DS is employed to examine the oxidative degra-dation of engine lubricating oil and to determine the OxidationDuration (OD), Total Acid Number (TAN) and Insoluble Content (IC)properties. All the DS analysis results are discussed compared withthe Fourier Transform Infrared spectroscopy (FTIR).

2. Experimental

2.1. Samples

All the samples were prepared with the device illustrated inFig. 1. The main function of the device is to oxidize the virgin sam-ples under given oxidation conditions. The air flow generated bythe air pump and the copper wire catalyst are used to acceleratethe oxidization process.

According to the sample preparation device, three types of vir-gin engine lubricating oils were employed to produce three seriesof samples with different degrees of degradation. The three virginengine lubricating oils are listed in Table 1.

For each series of samples, three types of properties includingOxidation Duration (OD), Total Acid Number (TAN) and Insoluble
Content (IC) were recorded. OD values were recorded accordingto sampling time. TAN and IC properties were respectively deter-mined by TAN&TBN Analyzer and Insoluble Content Analyzer madeby Beijing China Invent Instrument Technology Ltd. Company. Thesamples with three properties are shown in Tables 2–4.
Company Oxidation temperature (◦C)

KunLun (China) 150Shell 150Dalian Petroleum (China) 150

24 L. Guan et al. / Sensors and Actuators A 168 (2011) 22–29

Table 2Sample list generated by set one.

No. Oxidationduring (min)

TAN(mgKOH/g)

Insoluble content(mg/g)

1-00 0 0.87 0.001-01 666 1.44 0.021-02 1501 1.45 0.051-03 2282 1.25 0.111-04 3075 1.09 0.221-05 3907 0.91 0.391-06 4756 1.26 0.591-07 5525 1.83 0.761-08 6375 1.96 1.021-09 7215 2.81 1.421-10 8033 5.43 1.611-11 8945 5.60 2.81

Table 3Sample list generated by set two.


TAN(mgKOH/g)


2-00 0 1.10 0.022-01 668 1.00 0.172-02 1485 0.85 0.352-03 1947 0.76 0.582-04 2667 0.72 0.682-05 3474 0.79 0.812-06 3618 0.70 1.132-07 4303 0.94 1.322-08 5079 0.79 1.542-09 5890 1.01 1.872-10 6733 1.18 2.212-11 7540 1.09 2.682-12 8342 1.36 3.242-13 9150 1.27 3.482-14 9869 0.99 3.872-15 10659 1.23 4.082-16 10963 1.13 4.29

Table 4Sample list generated by set three.


TAN(mgKOH/g)


3-00 0 1.34 0.003-01 772 0.94 0.053-02 1539 0.55 0.143-03 2328 0.43 0.263-04 3103 0.56 0.373-05 3445 0.74 0.433-06 3889 0.68 0.583-07 4072 1.00 0.653-08 4693 0.60 1.023-09 4971 1.10 1.283-10 6180 1.11 3.60

2

F0swabr

employed in this article. The construction of the sensor is shownin Fig. 2. The sensor is fabricated by printed circuit board (PCB)technique.

The comb electrodes of the sensor are made of gold plated cop-per layers with 35 �m height h. The copper finger width and spacing

3-11 6660 1.05 7.56

.2. FTIR apparatus and method

The FTIR instrument used in this work was Perkin Elmer 1725XT-IR spectrometer. A pair of well-polished KBr windows with.05 mm spacer creating a fixed thin film was used to acquire theample spectral data. During spectra measurement, a blank KBrindow spectra was collected and used as reference for sample

bsorbance calculation. And then the sample was injected into thelank KBr windows and the sample spectra were collected in theange of 400 to 4000 cm−1 at 4 cm−1 resolution.

Fig. 2. Construction of sensor (a) schematic. Construction of sensor (b) photograph.

2.3. DS apparatus and method

A Dielectric Spectroscopy Analyzer for Petroleum (DSAP)instrument made by Logistical Engineering University (LEU) wasemployed to obtain the DS data of samples [31,32]. During DSmeasurement, DSAP generates a. c. signals with desired frequen-cies and amplitudes which are applied to the samples undertest. Oil-composition-dependent current, which is similar to oil-condition-dependent current [23], is amplified, filtered, rectified,converted into a d. c. voltage output and put into a 12 bits A/Dconverter. So the values DSAP measures, called ‘response signals’,are non-dimensional numbers and range from 0 to 4096. The mainparameters of DSAP are:

Waveform generated: 1. sine wave, 2. square wave, 3. trianglewave.Amplitude range: −10 V to +10 V.Frequency range: 50 kHz–16 MHz.Temperature held: room temperature to 100 ± 1 ◦C.Analysis time for one sample: about 3 min.

For this study, the sine waveform was selected; amplitudes were4 V, 6 V, and 8 V; frequencies ranged from 50 kHz to 16 MHz andfrequency interval was 200 kHz; measurement temperatures were40 ± 1 ◦C and 100 ± 1 ◦C.

A type of interdigitated comb capacitor sensor is fabricated and


F

ati

3

3

caie

etou

Table 5Peak area results of set one.

No. S1 S2 S3

1-00 22.193 248.666 128.8941-01 17.331 228.165 135.9491-02 18.897 230.387 158.7541-03 19.300 271.496 178.9811-04 21.300 311.430 204.3151-05 25.781 385.124 210.9271-06 35.395 496.285 231.5701-07 54.494 659.748 237.3501-08 86.910 827.334 251.9101-09 105.635 861.001 251.8321-10 163.407 1167.237 281.7531-11 226.322 1177.521 319.109

Table 6Peak areas results of set two.

No. S1 S2 S3

2-00 0.000 0.000 248.4622-01 −1.826 59.469 208.5072-02 25.437 141.699 236.5712-03 12.173 250.713 273.9272-04 15.530 366.493 280.4542-05 30.444 354.302 266.5992-06 55.156 770.127 303.3602-07 55.968 845.631 317.5842-08 96.491 1146.900 307.3532-09 115.090 1241.389 380.2862-10 155.654 1352.902 392.5262-11 188.805 1576.000 420.0482-12 177.432 1389.495 378.5012-13 255.886 1757.598 494.5982-14 219.195 1468.520 423.2342-15 294.524 1804.537 525.7342-16 288.930 1711.515 551.087

Table 7Peak areas results of set three.

No. S1 S2 S3

3-00 9.043 170.631 131.8053-01 13.187 197.463 132.5673-02 12.904 222.201 110.5053-03 16.111 281.947 109.6643-04 20.263 291.729 98.9383-05 22.478 368.709 116.5473-06 27.519 422.198 127.8553-07 26.342 366.387 115.7603-08 38.515 455.334 133.9593-09 44.867 500.059 148.623
ig. 3. MIR spectra (a) set one. MIR spectra (b) set two. MIR spectra (c) set three.
re both 200um. The substrate of the sensor is a type of polyte-rafluoroethylene (Teflon or PTFE or F4) whose dielectric constants 2.65.

. Results and discussion

.1. FTIR measurements

The MIR spectra of three series of samples are shown in Fig. 3.The features in the three series of MIR spectra can be con-

luded that absorption bands centered at 1774 cm−1, 1713 cm−1

nd 1604 cm−1 have the increasing trends as for absorption peakntensity with the oxidation of the lubricating oil, which can bexplained as below:

The band feature centered at 1604 cm−1 is due to the pres-nce of saponified matter namely the initial oxidized products;he band feature centered at 1713 cm−1 is due to the presencef carbonyl matter namely the intermediate oxidized prod-cts; the band feature centered at 1774 cm−1 is due to the

3-10 97.849 838.778 237.9633-11 124.464 908.065 401.006

presence of carboxyl matter namely the deeply oxidized prod-ucts.

The absorption peak areas centered at 1774 cm−1, 1713 cm−1

and 1604 cm−1 for three series of samples, which were labeled byS1, S2 and S3, respectively, were calculated by Thermo ScientificOmnic software. The results are shown in Tables 5–7.

Nine multiple linear regression (MLR) models were, respec-tively, built to examine the relationships between the peak areas ofthree series of samples and those three properties. In these models,the predictor variables were peak areas and the response variableswere the three properties including OD, TAN and IC. The programswere written in Matlab2008a and based on the ‘regress’ function
in Matlab Statistics Toolbox. For each series of samples, three MLRmodels were, respectively, constructed according to OD, TAN andIC properties. During modeling leave-one-out full cross validationwas used to obtain the predicted results. The predicted correlation

26 L. Guan et al. / Sensors and Actua

Table 8Predicted results based on MIR peak areas in correlation coefficient.

Property Set one Set two Set three

ci

oco

3

3

so

(

Fs

Oxidation duration (min) 0.9905 0.9723 0.5796TAN (mgKOH/g) 0.8670 0.4918 0.1138Insoluble content (mg/g) 0.9385 0.9718 0.9131

oefficients between the predicted and the measured were listedn Table 8.

As shown in Table 8, poor correlation coefficients for TAN werebtained for three series. For set one and set two, the correlationoefficients for OD and IC were relatively acceptable. For set three,nly IC property had good predicted results.

.2. DS measurements

.2.1. DS spectra features analysisDS spectra of all three series of samples were collected and

hown in Fig. 4. From Fig. 4 we can conclude the main featuresf DS spectra:

1) The output DS spectra of DSAP have the shift trend to the lowerfrequency range as oxidation duration increases, which is dif-

ig. 4. DS spectra (a) set one at 40 ◦C. DS spectra (b) set one at 100 ◦C. DS spectra (c) setpectra (f) set three at 100 ◦C.

tors A 168 (2011) 22–29

ferent from the peak area or height changing feature in MIRspectra.

(2) Similar characteristics can be obtained under different testingconditions. For example, at 40 ◦C and 100 ◦C testing tempera-tures and at 4Vpp, 6Vpp and 8Vpp excitation signal amplitudeshave almost the same testing results.

(3) Characteristic band peak features cannot be seen from the spec-tra data of DS, which is different from MIR and somewhatsimilar with NIR. It can be difficult to assign specific featuresto specific chemical components.

3.2.2. Quantitative calculation based on DS dataAs DS is based on the interaction of an external field with

the electric dipole moment of the sample, it is difficult to assignspecific features to specific chemical components of the sam-ple like MIR analysis. However characteristic ingredient andstructure information of the sample can be obtained from DSdata. In order to find out the relationship between DS data
and usual chemical and physical properties such as TAN andinsolubles, multivariate (multiple frequencies) calibration tech-niques (e.g. principal components analysis PCA, partial leastsquares PLS, or artificial neural networks) are often employed[31,32].
two at 40 ◦C. DS spectra (d) set two at 100 ◦C. DS spectra (e) set three at 40 ◦C. DS


Table 9Predictive results of sample set one based on DS data.

Property 4Vpp 6Vpp 8Vpp

40 ◦C 100 ◦C 40 ◦C 100 ◦C 40 ◦C 100 ◦C

Oxidation duration (min) 0.8560 0.9623 0.9263 0.9906 0.9889 0.9896TAN (mgKOH/g) 0.9614 0.9923 0.9078 0.9926 0.9355 0.9963Insoluble content (mg/g) 0.7884 0.9814 0.7330 0.9846 0.9151 0.9762

Table 10Predictive results of sample set two based on DS data.


40 ◦C 100 ◦C 40 ◦C 100 ◦C 40 ◦C 100 ◦C


Table 11Predictive results of sample set three based on DS data.


40 ◦C 100 ◦C 40 ◦C 100 ◦C 40 ◦C 100 ◦C


Table 12Predicted results of all samples based on transformed DS data.


40 ◦C 100 ◦C 40 ◦C 100 ◦C 40 ◦C 100 ◦C

0.9734 0.9733 0.9524 0.98490.9544 0.9933 0.9808 0.99860.9031 0.9867 0.8635 0.9866

btwaftr

ae

oaod

oHftnaswsms

s

Oxidation duration (min) 0.9038 0.9908TAN (mgKOH/g) 0.9729 0.9911Insoluble content (mg/g) 0.8371 0.9265

In this article partial least-squares (PLS) regression is used touild the relationship between DS data and OD, TAN and IC proper-ies. Three series of samples including set one, set two and set threeere respectively calculated by means of PLS. The target properties

re the OD, TAN and IC. As the number size of samples was relativelyew, leave-one-out method (full cross validation) was adopted. Allhe programs were written and run in Matlab2008a. The calculationesults of correlation coefficients are listed in Tables 9–11.

The obvious conclusion can be obtained from Tables 9–11: moreccurate results can be obtained at 100 ◦C than at 40 ◦C while thexcitation amplitude has less influence.

To summarize, the respective predicted results of three seriesf samples indicated that DS data contained the compositionalnd structural information, which accounted for the degradationf engine lubricating oil. With PLS, TAN and IC properties can beetermined accurately based on the DS data.

The results above were obtained by respective calculations. Sone PLS model had to be constructed for each series of samples.owever, it is necessary and practical to build an overall PLS model

or different series of samples. During oxidation-induced degrada-ion of lubricating oil, kinds of polar oxidation and nitration wereewly generated. The presence of these polar products can be seennd interpreted from the changes (shifts) in DS spectra. Before con-truction of the overall PLS model, all three series of DS spectraere transformed by subtracting the corresponding virgin sample

pectra, respectively. And the overall PLS multivariate calibrationodel was built based on the transformed DS data. The transformed

pectra of the three series of samples were shown in Fig. 5.According to the overall PLS model, the predicted results are

hown Table 12. Fig. 5. The transformed spectra of three series (a) at 40 ◦C. The transformed spectraof three series (b) at 100 ◦C.

28 L. Guan et al. / Sensors and Actua

Fs

bot

a

pw(

4

tdcptOhes

[

[

[

[

[

[

[

[

[

[

[

[

[

[23] H.-S. Lee, S.S. Wang, In situ monitoring of high-temperature degraded engine

ig. 6. Regression line for oxidation duration (a). Regression line for TAN (b). Regres-ion line for insoluble content (c).

From results in Table 12, more accurate results can be obtainedy transformed DS data with the overall PLS model. And higherperating at 100 ◦C was more enhancing than at 40 ◦C, which shownhat operating temperature was an important parameter to control.

In Fig. 6 shows the regression lines at 100 ◦C operating temper-ture and 8Vpp amplitude.

From Fig. 6, good performance can be seen except for one sam-le’s predicted value of IC property as shown in Fig. 6(f). However,e found that the sample which had the largest residual value

1.965 mg/g) had the largest IC value (7.56 mg/g).

. Conclusions

Based on the described engine lubricating oil oxidation device,hree series of samples with different oxidation degrees were pro-uced. MIR and DS were employed to discover the degradationharacteristics of the samples. The oxidation features of the sam-les can be directly seen from both MIR and DS spectra. Based onhe characteristic peak areas calculated based on MIR spectra data,
D and IC can get good predicted performance but TAN failed. Weave demonstrated that DS is suitable to monitor the degradation ofngine lubricating oil. OD, TAN and IC properties can be determinedimultaneously quantitatively and accurately.
[

[

tors A 168 (2011) 22–29

DS, as the most effective method to extract the dielectric char-acteristic from the dielectric material, can be developed into anefficient oil monitoring/analysis method. With the chemometricsmultivariate calibration such as PLS, qualitative and quantitativeanalysis models can be built to monitor the degradation degreesand to determine the main oil monitoring properties of enginelubricating oil such as OD, TAN and IC. We believe that the remain-ing useful life of engine lubricating oil can be predicted based onon-line or in situ DS data.

References

[1] A. Borin, R.J. Poppi, Application of mid infrared spectroscopy and iPLS for thequantification of contanminants in lubricating oil, Vib. Spectrosc. 37 (2005)27–32.

[2] Y. Liu, Z. Liu, S.Z. Wen, Y.B. Xie, Motion analysis on the particles in a magneticfield detector, Tribol. Int. 33 (2000) 837–843.

[3] B.J. Roylance, Ferrography then and now, Tribol. Int. 38 (2005) 857–862.[4] H.L. Xiao, The development of ferrography in China-some personal refflections,

Tribol. Int. 38 (2005) 904–907.[5] V. Macian, R. Payri, B. Tormos, L. Montoro, Applying analytical ferrography as a

technique to detect failures in diesel engine fuel injection systems, Wear 260(2006) 562–566.

[6] P. Yaroshchyk, R.J.S. Morrison, D. Body, B.L. Chadwick, Quantitative determina-tion of wear metals in egine oils using laser-induced breakdown spectroscopy:a comparison between liquid jets and static liquids, Spectrochim. Acta Part B60 (2005) 986–992.

[7] P. Yaroshchyk, R.J.S. Morrison, D. Body, B.L. Chadwick, Quantitative determina-tion of wear metals in engine oils using LIBS: the use of paper substrates anda comparison between single-and double-pulse LIBS, Spectrochim. Acta Part B60 (2005) 1482–1485.

[8] E.J. Ekanem, J.A. Lori, S.A. Thomas, The determination of wear metals in usedlubricating oils by flame atomic absorption spectrometry using sulphanilic acidas ashing agent, Talanta 44 (1997) 2103–2121, 2108.

[9] Z. Yang, X. Hou, B.T. Jones, Determination of wear metals in engine oil by mildacid digestion and energy dispersive X-ray fluorescence spectrometry usingsolid phase extraction disks, Talanta 59 (2003) 673–680.

10] M. Pouzar, T. Cernohorsky, A. Krejcova, Determination of metals in lubricatingoils by X-ray fluorescence spectrometry, Talanta 54 (2001) 829–835.

11] M. Knochen, A. Sixto, G. Pignalosa, S. Domenech, S. Garrigues, M. de la Guardia,Determination of insolubles in diesel lubricating oil by FIA-visible spectrome-try, Talanta 64 (2004) 1359–1363.

12] ASTM E-2412-0, Standard Practice for Condition Monitoring of Used Lubri-cants by Trend Analysis Using Fourier Transform Infrared (FT-IR) Spectrometry.Standard Volume.03.06 (2005).

13] A.R. Caneca, M. Fernanda Pimentel, Roberto Kawakami Harrop Galvao, C.E.da Matta, F.R. de Carvalho, I.M. Raimundo@@Jr., C. Pasquini, J.J.R. Rohwedder,Assessment of infrared spectroscopy and multivariate techniques for monitor-ing the service condition of diesel-engine lubricating oils, Talanta 70 (2006)344–352.

14] H.F. Yuan, X.L. Chu, H.R. Li, Y.P. Xu, Determination of multi-properties of residualoils using mid-infrared attenuated total reflection spectroscopy, Fuel 85 (2006)1720–1728.

15] M.A. Al-Ghouti, L. Al-Atoum, Virgin and recycled engine oil differentiation: aspectroscopic study, J. Environ. Manage. 90 (2009) 187–195.

16] N. Gracia, S. Thomas, P. Bazin, L. Duponchel, F. Thibault-Starzyk, O. Lerasle,Combination of mid-infrared spectroscopy and chemometric factorizationtools to study the oxidation of lubricating base oils, Catal. Today 155 (2009)255–260.

17] Y. Felkel, N. Dorr, F. Glatz, K. Varmuza, Determination of the total acid number(TAN) of used gas engine oils by IR and chemometrics applying a com-bined strategy for variable selection, Chemom. Intell. Lab. Syst. 101 (2010)14–22.

18] V.F. Lvovich, M.F. Smiechowski, Impedance characterization of industrial lubri-cants, Electrochim. Acta 51 (2006) 1487–1496.

19] V.F. Lvovich, M.F. Smiechowski, Non-linear impedance analysis of industriallubricants, Electrochim. Acta 53 (2008) 7375–7385.

20] S.S. Wang, H.-S. Lee, The application of a.c. impedance technique fordetecting glycol contamination in engine oil, Sens. Actuators B 40 (1997)193–197.

21] S.S. Wang, Y. Lin, A new technique for detecting antifreeze in engine oil duringearly stage of leakage, Sens. Actuators B 96 (2003) 157–164.

22] H.P. Christian Ulrich, H. Sundgren, F. Bjorefors, C. Krantz-Rulcker, Simultaneousestimation of soot and diesel contamination in engine oil using electrochemicalimpedance spectroscopy, Sens. Actuators B 127 (2007) 613–618.

oil condition with microsensors, Sens. Actuators B 20 (1994) 49–54.24] S.S. Wang, H.-S. Lee, D.J. Smolenski, The development of in-situ electrochemical

oil condition sensors, Sens. Actuators B 17 (1994) 179–185.25] S.S. Wang, H.-S. Lee, An electrochemical sensor for distinguishing two-stroke-

engine oils, Sens. Actuators B 40 (1997) 199–203.

Actu

[

[

[

[

[

[

[

[

L. Guan et al. / Sensors and

26] S. Wang, Road tests of oil condition sensor and sensing technique, Sens. Actu-ators B: Chem. 73 (2001) 106–111.

27] S. Raadnui, S. Kleesuwan, Low-cost condition monitoring sensor for used oilanalysis, Wear 259 (2005) 1502–1506.

28] S.S. Wang, Engine oil condition sensor: method for establishing correlation withtotal acid number, Sens. Actuators B 86 (2002) 122–126.

29] S.O. Nelson, Dielectric spectroscopy for agricultural applications, Proc. IEEEInstr. Meas. Technol. Conf. 1 (2004) 752–754.

30] S.O. Nelson, Dielectric spectroscopy of fresh fruits and vegetables, Proc. IEEEInstr. Meas. Technol. Conf. 1 (2005) 360–364.

31] L. Guan, Feng.F X.L., G. Xiong, Engine lubricating oil classification by SAEgrade and source based on dielectric spectroscopy data, Chim. Acta 628 (2008)117–120.

32] L. Guan, X.L. Feng, Z.C. Li, G.M. Lin, Determination of octane num-bers for clean gasoline using dielectric spectroscopy, Fuel 88 (2009)1453–1459.

33] V.F. Lvovich, C.C. Liu, M.F. Smiechowski, Optimaization and fabrication of planarinterdigitated impedance sensors for highly resistive non-aqueous industrialfluids, Sens. Actuators B 119 (2006) 490–496.

ators A 168 (2011) 22–29 29

Biographies

L. Guan Ph.D. (Engineering), Logistical Engineering University (LEU), Chongqing,China, 2009. He is now the lecturer of LEU. Testing and analysis technologies, devel-opment of analytical instruments for fuel and lubricating oil especially based ondielectric spectroscopy technology are the current study topics.

X.L. Feng Ph.D. (Engineering), Logistical Engineering University (LEU), Chongqing,China, 2004. He is now the professor of LEU. Chemometrics, testing and analysistechnologies and development of analytical instruments for fuel and lubricating oilare the current fields of interest. Near infrared spectroscopy and dielectric spec-troscopy is his hot study topics nowadays.

G. Xiong Master, Logistical Engineering University (LEU), Chongqing, China, 2003.He is now the engineer of LEU. Electronics engineering and development of analysisinstrument is his major fields of interest.

J.A. Xie Master, Logistical Engineering University (LEU), Chongqing, China, 2008.Chemometrics is his current study field of interest.