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Oil condition monitoring technique for fault diagnosis on Crane Liebherr LHM-1200.

Introduction

Oil condition instruments are suitable for use in all major industrial applications, including iron & steel, agriculture, military, mines & quarries, power generation, transport and used oil analysis. The most effective and cost efficient groups of machine condition monitoring are oil analysis [1, 2]. Preventative maintenance program is essential for optimizing operational efficiency and performance of machinery. Continuous oil condition monitoring of machinery and lubricant testing is fast becoming the established method of predicting and avoiding impending machinery breakdown. Jonathan had been used statistical analysis to create wear debris alarm limits [3]. By ferrous debris monitors and Ferro sensors can be identified and replaced before any serious machinery damage occurs. In this way, production can be maintained, equipment life extended and the return on capital investment increased. Research showed that analytical wear rate determination based on oil analysis was suitable for fault diagnosis of internal combustion engine [4]. Oil analysis often contributes valuable information for oil operating time to change the oil [5]. The main limitation is that it is comparatively expensive to operate and can also be a time consuming activity. The employment of this technique can be used as both predictive and proactive tools in order to identify machine wear and diagnose faults occurring inside machinery against different kinds of oils. New research showed that oil analysis had been used to assessing wear problems [6]. However, recent evidence shows that oil analysis technique provides greater and more reliable information, thereby resulting in a more effective maintenance program with large cost benefits to industry [7-10]. As a general rule, machines do not break down or fail without some form of warning, which is indicated by increased wear debris materials. By measuring and analyzing the oil of a machine, it is possible to determine both the nature and severity of the defect, and hence predict the machine's failure [2, 11] and also choose the best oil for machine and investigate optimum operating time for that oil. On the other hand, oil analysis has proven in many instances to be a leading indicator to identify fault diagnosis in a machine [12-16]. Oil analysis may have two purposes those are safeguarding the oil quality (contamination by parts, moist) and safeguarding the components involved (characterization of parts).

The objective of this research was to investigate the fault diagnosis for reciprocating machine by oil condition monitoring. This was achieved by investigating different oil analysis of crane Liebherr LHM-1200. The oil analysis was initially run under regular interval during machine life. A series of tests were then conducted under the operating hours of machine. Oil sample was regularly collected. Numerical data produced by oil analysis was compared with previous data, in order to quantify the effectiveness of the results of oil condition monitoring technique. The results from this paper have given more understanding on the dependent and independent roles of oil analyses in predicting and diagnosing machine faults.

Experimentation and testing

The experiments and tests were conducted on crane Liebherr LHM-1200 use for loading and unloading bulk and solid materials from ship to depot area and truck. The crane that was selected for the tests conducted in this work had a diesel motor. Details of engine components are given in table 1.

Hence, both sliding and rolling wear processes were examined in this study, which are commonly associated with rotating equipment in industry. Fourteen months oil analyses were conducted. Samples had been taken from August 2005 to September 2006. For each test five repetitions have been done and statistical information was extracted from them by using of the statistical software (SPSS). Table 2 shows the equipment and its abilities that has been used in this study. This equipment has sensor for detect the amount of each element and character. Also it is possible to use in situ sensors in engine to measure these parameters and to automate oil condition monitoring.

All of the oil used for lubricant in carne was normal lubrication 5W40 cSt oil, which is the recommended oil to lubricate the engine under normal operating conditions. The viscosity characteristics of oil at 40 [degrees]C showed in figure 1.

Due to the duration of the running-in phase, oil samples were collected on regular period at every months after repair of engine at July 2006 and after that until September 2006. The transition point of the running-in period to the normal phase was determined by examining the trend of the change in oil analysis results. Oil samples were collected on a regular basis after the run in period.

Oil samples were collected from the reservoir using a pipette through the filler point. Results of oil analysis showed that there were significant different between viscosity characteristics of oil at 40 [degrees]C during tests. Viscosity before repair the engine at June 2006 was lower than another measurement.

[FIGURE 1 OMITTED]

Results

Wear debris analysis

Machinery manufacturers will often also give guidance on limit values applicable to specific items of machinery. According the results of researchers if the concentration Wear debris materials were between 50 to 100 ppm, fifty percent change in wear debris materials could show the fault in engine [17]. According to another research, optimums of wear debris materials introduced, and are shown in figure 2 [18].

[FIGURE 2 OMITTED]

An important factor in any monitoring program is the ability to obtain reliable trend information or details of gradual changes with time or running hours. A careful observation of these trends can be very revealing. Any significant variation from the trends such as rapid increase or decrease in a measured value, gives early warning of an impending problem, well before the limit value is reached [19].

Lost production and expensive capital equipment replacement are major costs associated with any catastrophic failure of machinery, the prevention of which is crucial for optimal operational performance. Oil condition monitoring involves sampling lubricants from critical rotating plant and equipment and then analyzing the lubricant for clues as to the operational condition of the machinery under inspection.

The wear debris analysis results for fourteen oil analyses were given in figure 3. Figure 4 shows the Fe and recommended level of that in oil sample. Results showed that Fe was exceeded of recommended value on June 2006 and also after repair the amount of Fe were exceeded of recommended value. Ferrous debris detected in oil analysis before repair showed that some defects were running inside the engine and Ferrous debris detected in oil analysis after repair showed that Ferro materials those came inside engine during repair process. So Ferrous debris could be identified and replaced before any serious machinery damage occurs.

According to the results, Pb was exceeding during May and June and on July it was higher than previous months. On July the engine had been overhauled and several damages in journal bearing had been saw and engine exactly needed to repair. It could show that increasing the amount of Pb in oil could show the serious defect in engine. Figure 5 shows the increasing amount of Pb during exam period and recommended level of Pb. Three major types of wear particles corresponding to rubbing, cutting and laminar wear were found in the oil samples. From their color, it was evident that the majority of the cutting particles came from the softer surface.

Table 3 shows the average value, warning zone (between average plus 1 and 2 times of standard deviation) and variation percent of wear debris materials of oil samples at repair time. Results illustrated that amount of Pb was on warning zone and also variation percent of Al and Ti was more than fifty percent instead of variation percent not access more than fifty percent.

[FIGURE 3 OMITTED]

[FIGURE 4 OMITTED]

[FIGURE 5 OMITTED]

Abrasive materials

Oil samples were analyzed and the results of abrasive materials are shown in figure 6. A significant amount of abrasive materials were found on December 2005, January, March, and June 2006 before repair of engine. Increasing the amount of Si could be cause of engine failure. Figure 7 shows the amount of Si and recommended level of that. As shown the amount of Si not exceed of recommended level but increasing amount of Si at long period of engine running time could affected engine parts and worn those parts.

Table 4 shows the average value, warning zone (between average plus 1 and 2 times of standard deviation) and variation percent of Abrasive materials of oil samples at repair time. According to the results the amount of B was on warning zone and its variation percent was more than fifty percent.

[FIGURE 6 OMITTED]

[FIGURE 7 OMITTED]

Additive materials

The building blocks of lube oil are known as base oil. Generally speaking, base oil is a mixture of various fractions from the crude oil refining process. Additives are then mixed within this base oil to impart additional desirable properties over and above those already present in the base oil. Base oil is refined by solvent extraction (usually with propane at a pressure high enough to keep it in liquid form) and by hydrotreatment (reaction with hydrogen).

These processes eliminate unwanted heavy hydrocarbons and aromatics (benzene-based chemicals) from the oil and make them suitable for use as base oil. The base oil is then mixed in the correct proportions with the additive package to give the correct viscosity grade for the machinery they will lubricate.

Oil samples were analyzed and the results of additive materials are shown in figure 8. Not significant changing amount of additive materials were found in oil during all test. So qualities of oil were same and not related to engine defect. Figure 9 showed that Mg had been increased on April, May, Jun, and July 2006 until repair and it could cause of engine defects.

Table 5 shows the average value, warning zone (between average minus 1 and 2 times of standard deviation) and variation percent of additive materials of oil samples at repair time. Results showed that the amount of additive materials was not on warning zone but only Zn was on warning zone and its variation percent was near with fifty percent.

[FIGURE 8 OMITTED]

[FIGURE 9 OMITTED]

Particle Quantifier

Oil samples were analyzed and the results of particle quantifier were shown in figure 10. Significant amount of particle quantifier was found in oil, it could be cause of engine worn. Particle quantifier could cause worn of engine parts.

[FIGURE 10 OMITTED]

Total Base Number

The oil is continuously exposed to acidic combustion products and these must be neutralized before they could corrode engine parts. Engines operating on heavier residual fuels are exposed to a more corrosive regime, as fuel sulphur levels are typically 2 to 4%. Here the TBN levels are typically between 20 and 40 dependent on fuel sulphur level. Maintaining a correct alkaline reserve is critical in preventing unnecessary corrosion of the upper piston, piston rings and top end bearing. Additionally, low TBN is indicative of reduced oil detergency. Most problematic are residual fuelled engines with small sumps and low oil consumption, where TBN decrease can be rapid.

The over basing impacts both alkaline reserve and detergency and is of particular importance in the piston ring packs where detergency will provide cleaning and prevent varnish formation. Low TBN will cause some problems that most important are corrosion of combustion space and bearing and fouling within the engine. Lowest recommended TBN for oil according to our full in Iran is 6. Figure 11 shows the TBN change during the tests.

As a result, significant decreasing amount of Total Base Number had been showed during tests. Results showed that TBN is almost equal to recommended level and it showed that engine exposed to a more corrosive regime. Corrosive environment could cause corrosion of combustion space and bearing and fouling within the engine.

Table 6 shows the average value, warning zone (between average minus 1 and 2 times of standard deviation) and variation percent of physical & chemical indexes of oil samples at repair time. Results illustrated that amount of VIS40 and TBN was not on warning zone and their variation percent was lower than fifty percent.

[FIGURE 11 OMITTED]

Correlation of oil condition monitoring and fault diagnosis

Oil analysis technique has been used to assess the condition of the reciprocating motor and diagnose any problems of that. The results from oil analysis of this experimental research indicated some defaults of diesel motor. Oil analysis of diesel motor could discover fault on piston, piston rings, top end bearing, fix bearing, crankcase, and most important fault of motor.

The correlation between the oil analysis and fault diagnosis was excellent, as oil condition technique was able to pick up on different issues, thus presenting a broader picture of the machine condition. Oil analysis detected a continuing motor defect, mechanical damage and wear debris materials. Oil analysis technique was capable in covering a wider range of machine diagnostics and faults within the diesel motor.

Conclusions

Oil analysis is the most effective techniques for monitoring the health of machinery. They offer complementary strengths in root cause analysis of machine failure, and are natural allies in diagnosing machine condition. They reinforce indications seen in each technology, and have unique diagnostic strengths in highlighting specific wear conditions.

Wear mechanisms include the detection of rubbing, metal-to-metal contact, and boundary lubrication breakdown.

The oil analysis was initially run under regular interval during machines life. A series of tests were then conducted under the operating hours of machine. Period of tests were every months. Oil samples were regularly collected. Numerical data produced by oil analyses were compared with another sample, in order to quantify the effectiveness of the results of oil condition monitoring technique. Results of oil analysis showed that there were significant different between viscosity characteristics of oil at 40 [degrees]C during time period. Viscosity before repair the engine at June 2006 was lower than another measurement.

According to the results, Pb was exceeding during May and June and on July it was higher than previous months. On July the engine had been overhauled and several damages in journal bearing had been saw and engine exactly needed to repair. It could show that increasing the amount of Pb in oil could show the serious defect in engine.

Three major types of wear particles corresponding to rubbing, cutting and laminar wear were found in the oil samples. From their color, it was evident that the majority of the cutting particles came from the softer surface. A significant amount of abrasive materials were found on December 2005, January, March, and June 2006 before repair of engine. Increasing the amount of Si could be cause of engine failure. Not significant changing amount of additive materials were found in oil during all tests. So qualities of oil were same and not related to engine defect.

Significant amount of particle quantifier were found in oil, it could be cause of engine worn. Particle quantifier could cause worn of engine parts. As a result, significant decreasing amount of Total Base Number had been shown during tests. Results showed that TBN is almost equal to recommended level and it showed that engine exposed to a more corrosive regime. Corrosive environment could cause corrosion of combustion space and bearing and fouling within the engine.

Results have been shown that oil condition monitoring technique has detected similar wear mechanisms associated with the engine. By comparing the results of the different months, a more reliable assessment of the condition of the test rig can be made. Meanwhile, the oil condition monitoring has its individual advantages. Wear debris analysis provides further insight on the wear rate and mechanism of the engine. Oil analysis has provided quick and reliable information on the condition of the bearings.

References

[1] Toms, L.A., 1998, Machinery Oil Analysis: Methods, Automation and Benefits: a Guide for Maintenance Managers & Supervisors, Coastal press.

[2] Barron, R., 1997, Engineering Condition Monitoring: Practice, Methods and Applications, Addison Wesley Longman press.

[3] Jonathan, S., 2001, "Use Statistical Analysis to Create Wear Debris Alarm Limits," Practicing Oil Analysis Magazine.

[4] Macian, V., Tormos, B., Olmeda, P., and Montoro, L., 2003, "Analytical Approach to Wear Rate Determination for Internal Combustion Engine Condition Monitoring Based on Oil Analysis," J. Trib. Intern., 36,pp. 771-776.

[5] Barnes, M., 2000, "Advanced Strategies for Selecting Oil Analysis Alarms and Limits," Practicing Oil Analysis Conference.

[6] Clark, D., 2004, "A New Approach to Assessing Wear Problems Using Oil Analysis," Practicing Oil Analysis Magazine.

[7] Mathew, J and Stecki, J.S., 1987, "Comparison of Vibration and Direct Reading Ferro Graphic Techniques in Application to High-Speed Gears Operating Under Steady and Varying Load Conditions," J. Soc. Tribol. Lubr. Eng., 43.pp. 646-653.

[8] Maxwell, H., and Johnson, B., 1997, "Vibration and Lube Oil Analysis in an Integrated Predictive Maintenance Program," Proc. 21st Annual Meeting of the Vibration Institute, pp. 117-124.

[9] Troyer, D.D., and Williamson, M., 1999. "Effective Integration of Vibration Analysis and Oil Analysis," Proc. International Conference on Condition Monitoring, University College of Swansea, pp, 411-420.

[10] Byington, C.S., Merdes, T.A., and Kozlowski, J.D., 1999, "Fusion Techniques for Vibration and Oil Debris/Quality in Gearbox Failure Testing," Proc. International Conference on Condition Monitoring, University College of Swansea, pp. 113-128.

[11] Williams, J.H., Davies A., and Drake, P.R., 1994, Condition-Based Maintenance and Machine Diagnostics, Springer press.

[12] Roylance, B.J., and Hunt, T.M., 1999, Wear Debris Analysis, Coxmoor Publishing Company.

[13] [13] Hunt, T.M., 1996, Condition Monitoring of Mechanical and Hydraulic Plant: a Concise Introduction and Guide, Chapman & Hall Press.

[14] Newell, G.E., 1999, "Oil Analysis-Cost Effective Machine Condition Monitoring Technique," J. Ind. Lubr. Tribol., 51, pp. 119-124.

[15] Ahn, H.S., Yoon, E.S., Sohn, D.G., Kwon, O.K., Shin, K.S., and Nam, C.H., 1996, "Practical Contaminant Analysis of Lubricating Oil in a Steam Turbine-Generator," J. Tribol. Int., 29, pp. 161-168.

[16] Kirk, T.B., 1994, "Numerical Characterization of Wear Debris for Machine Condition Monitoring," International Tribology Conference, AUSTRIB '94 Perth, Australia.

[17] Poley, J., 2000, "Reciprocating Engine Oil analysis," Practicing Oil Analysis Conference.

[18] Hunt, T.M., and Evans, J.S., 2008, Oil Analysis Handbook. Coxmoor press.

[19] Barnes, M., 2002, "lemental Analysis," Practicing Oil analysis Magazine.

Hojat Ahmadi (1) and Kaveh Mollazade (2)

(1), (2), Department of Agricultural Machinery, Faculty of Biosystems Engineering, University of Tehran, Karaj, Iran

* Corresponding Author. E-mail: kaveh.mollazade@Gmail.com
Table 1: Details of machine.

Machine component           Description

Model of engine             DAIMLER BENZ INDUSTRIAL, diesel engine
Type & number of cylinder   OM 444 LA, 12 cylinders
Engine capacity (kW)        491 KW at 1900 rpm
Cooling system              Water

Table 2: Equipment that has been used to measurements.

Equipment                     Usage

ANALEXrpd (Rotary Particle    Ferrous & Non-Ferrous
Depositor)                    Metallic Debris Detection
OIL TEST CENTRE (FG-K1-100-   Viscosity (heated), Viscosity (unheated)
KW)                           Total Base Number

Table 3. Average value, warning zone and variation percent of wear
debris materials of oil samples at repair time.

Wear         Average     Average of wear   Average of wear   Variation
debris      value at     debris material   debris material   percent at
material   repair time   + 1 * STDV(ppm)   + 2 * STDV(ppm)     repair
              (ppm)                                             time

Fe            30.55           44.84             57.88            3.93
Cr             2.07            2.40              3.27           34.93
Al             8.93            9.25             12.97           61.48
Cu             3.1             4.05              5.29           10.71
Pb            17.54           10.85             16.32          226.88
Ni             1.64            2.30              3.09            8.49
Ti             3.64            4.77              7.19           55.28

Table 4. Average value, warning zone and variation percent of Abrasive
materials of oil samples at repair time.

             Average       Average of       Average of
             value at      abrasive          abrasive       Variation
Abrasive      repair       materials        materials +    percent at
materials   time (ppm)   + 1 * STDV(ppm)   2 * STDV(ppm)   repair time

Si             8.11           12.81            19.61           34.92
Na             2.86            6.18             7.76           37.75
B              3.45            3.32             4.86           94.55

Table 5. Average value, warning zone and variation percent of
additive materials of oil samples at repair time.

           Average    Average of   Average of
           value at    additive     additive
            repair     material     material     Variation
Additive    time        - 1 *         - 2 *     percent at
material    (ppm)      STDV(ppm)    STDV(ppm)   repair time

Zn           874        800.91       710.07         1.99
P            637        555.56       429.61         6.53
Ca           2926      2751.92      2393.17         5.94
Mg          37.76       21.13         14.97        38.37

Table 6. Average value, warning zone and variation percent of
Physical & chemical indexes of oil samples at repair time.

                      Average of       Average of
Physical   Value at   physical &       physical &
&          repair     chemical         chemical          Variation
chemical   time       indexes -        indexes           percent at
indexes    (ppm)      1 * STDV (ppm)   -2 * STDV (ppm)   repair time

VIS40        159          155.29           149.02          149.02
TBN          6.1           6.18             4.59            4.59
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Author:Ahmadi, Hojat; Mollazade, Kaveh
Publication:International Journal of Applied Engineering Research
Article Type:Report
Geographic Code:1USA
Date:Apr 1, 2009
Words:3460
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