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Article

Monitoring the Conditions of Hydraulic Oil with Integrated Oil Sensors in Construction Equipment

by
Sung-Ho Hong
1,* and
Hong-Gyu Jeon
2
1
Department of Mechanical System Engineering, Dongguk University-WISE Campus, Gyeongju-si 38066, Korea
2
Department of Reliability Assessment, Hyundai Construction Equipment, Yongin-si 16891, Korea
*
Author to whom correspondence should be addressed.
Lubricants 2022, 10(11), 278; https://doi.org/10.3390/lubricants10110278
Submission received: 14 September 2022 / Revised: 20 October 2022 / Accepted: 21 October 2022 / Published: 25 October 2022
(This article belongs to the Special Issue Advanced Industrial Lubricants and Future Development Trends of Tribo-Systems for Tribological Performance Evaluation)
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Abstract

:
Maintenance and reliability are crucial aspects for operating construction equipment in harsh environmental conditions. One of the methods used to improve the maintenance and reliability of construction equipment is oil diagnosis. In this study, monitoring of conditions was performed through the use of an integrated oil sensor for hydraulic oil in construction equipment. Hydraulic oil in construction equipment is polluted by various materials such as moisture and dust. Therefore, the present work investigated the change in the state of hydraulic oil caused by the presence of major pollutants in construction equipment. The degree of contamination of the hydraulic oil was evaluated using an integrated oil sensor that could measure absolute viscosity, density, temperature and dielectric constant. It is difficult to determine the influence of each pollutant source on hydraulic oil. Therefore, a simple experimental device and diagnostic program were constructed to effectively measure variations in the properties of hydraulic oil caused by each contaminant source. In particular, the focus was on the dielectric constant and examination of its usefulness. In addition to testing various contaminant conditions in the laboratory, varnish-contaminated lubricants used in actual construction equipment were verified. The results showed little change in the dielectric constant when introducing dust and improper lubricants. However, the dielectric constant was affected by the incorporation of water and the generation of varnish, which led to evident variations. In particular, a direct correlation was found between varnish contamination and the dielectric constant measured by the oil sensor, and the cause was explained based on the results of elemental component analysis. Therefore, measuring absolute viscosity, density, and temperature when monitoring the condition of hydraulic oil in construction equipment can provide useful information regarding lubricant and machine condition; however, the dielectric constant is most useful in detecting moisture incorporation and varnish contamination caused by oil deterioration.

Graphical Abstract

1. Introduction

Construction machinery is extensively used both in civil engineering works and in construction sites, such as house construction and factory construction. Construction equipment used in the construction industry makes an important contribution to both productivity and efficiency [1]. Previous studies on construction equipment have been conducted in various ways, so there has been no systematic classification. Regarding future research directions, it is predicted that a machine maintenance strategy that can replace parts in real time using a more complex predictive model than before will be developed. Moreover, research on automated construction equipment is expected to proceed due to the advantages of unmanned machines. It is also predicted that nanotechnology and convergence technologies will contribute to the development of construction machinery [2,3,4]. Most construction equipment requires more energy to operate than industrial equipment, so many researchers and manufacturers have to think hard about how hydraulic oil can contribute to energy efficiency and how to properly manage the oil. The amount of hydraulic oil used exceeds the amounts of engine oil and gear oil. Hydraulic oil has critical functions such as power transmission, maintaining pressure, transferring heat, preventing wear and corrosion, etc. Therefore, it is very important to monitor the maintenance and condition of hydraulic oil. The related research field is machine condition monitoring.
Machine condition monitoring is a field that contributes to improving machine reliability by diagnosing machine faults or failures based on data and information measured using various sensors and measuring devices. Machine condition monitoring prevents machine failures and enhances the level of maintenance of the mechanical system. In diagnosing the machine condition, compliance monitoring, which diagnoses the machine condition based on reference values of physical quantities such as pressure or temperature, and structure integrity monitoring, which involves diagnosing the machine condition by measuring the stress and strain of the structure using a strain gauge, have been widely used in the past. At present, in addition to these methods, machine state diagnosis using vibration and noise, using thermography, using non-destructive techniques such as ultrasound, and using lubricant and wear particle analysis are all widely used, as shown in Figure 1 [5,6].
Diagnosis of the machine condition through lubricant analysis has been tested in various fields. It is applied not only for monitoring the condition of important machine parts, such as engines and gearboxes in automobiles, high speed-trains, and military machines such as tanks and armored vehicles, but also in construction equipment, marine engines, and chemical or power plants. It is also widely applied in wind turbines and aircrafts [7].
The methods used to analyze lubricant in a mechanical system are shown in Figure 2. These can be broadly divided into three methods: analyzing lubricant through oil sampling is called the off-line method, analyzing directly where the main flow occurs is called the in-line method, and the third method, the on-line method, utilizes a detour (by-pass) without affecting the main flow or operation and performance in a representative spot. The biggest disadvantage of analyzing lubricant via the off-line method is that real-time analysis is not possible. To compensate for this, an on-line method based on a lubricant sensor is typically applied. Compared to the off-line method, the on-line method can reduce human error such as contamination in the oil sampling process, and can also prevent major failure by enabling the early detection of wear particles through wear particle sensors. Moreover, there are low maintenance costs and there is no need for professional skills to analyze or control for measurement. Due to these advantages, the on-line method using a lubricant sensor is preferred over the off-line method [8].
In terms of condition monitoring technologies, Figure 3 shows the market shares in 2021 for vibration monitoring, thermography, oil analysis, corrosion monitoring, ultrasound emission monitoring, and others. Vibration monitoring still holds the largest market share with roughly 32%. Compared with the past, the market shares of oil analysis, thermography, and ultrasound fields are growing in the field of machine condition monitoring [9]. In the near future, it is expected that the market share of oil analysis will expand further due to the continued development of oil sensors and diagnostic algorithms.
Lubricant sensors are used to measure various properties of lubricant, such as viscosity, density, water content, etc. A sensor is used to measure viscosity, which is the most basic property of lubricant. The main methods used by a lubricant sensor to measure viscosity are displacement [10], acoustic wave [11,12] and vibration [13,14,15] methods. The displacement method measures viscosity by measuring the time it takes for a ferromagnetic piston to move in a small channel with electromagnetic coils installed above and below it. The acoustic wave method measures viscosity through a resonance frequency shift with a piezoelectric material and a quartz membrane. The vibration method measures viscosity by checking the variation in the frequency or amplitude of resonance depending on viscosity [16]. In addition to the viscosity sensor, a wear particle sensor is typically used. Wear particle sensors mainly utilize a method using capacitance and inductance [17,18,19,20,21,22,23,24], an acoustic method using an ultrasonic transducer [25,26], a method using optics [27,28,29], or a method using a permanent magnet and an inductance together [16].
Sensors for measuring moisture content [30], sensors for measuring acid number (AN) and base number (BN) [31,32], aeration sensors [33], and contamination sensors have been developed and used in industrial fields. Moreover, studies have examined the dielectric constant and electrical conductivity methods used to determine the properties of lubricants [34,35,36,37,38].
By measuring the dielectric constant or electrical conductivity, variations in other properties can also be estimated; that is, the tendencies of change are similar between the dielectric constant and AN, and therefore any change in AN can be inferred by measuring the dielectric constant, as shown in Figure 4.
The contamination and deterioration of lubricants have complex causes, rather than being caused by one type of pollutant. Therefore, to diagnose the condition of a lubricant or machine, it is necessary to evaluate various physical properties. As a result, it is desirable to use an integrated sensor that can simultaneously measure several properties. With the development of MEMS or sensor manufacturing technologies, the reliability and cost of integrated sensors have improved compared to past technologies [5].
The complex structure of construction equipment leads to various fault sources, and to predict fault occurrence, condition monitoring systems need to have various sensors installed to accurately diagnose the health of construction machinery [39]. It is common for hydraulic or mechanical transmission failures to be caused by oil contamination, and oil contamination is responsible for 70% of failures in hydraulic systems [40]. Past systems mainly monitored the pressure, viscosity and temperature of hydraulic oil, but various other properties of lubricants are now monitored through oil sensors to diagnose the condition. In Hitachi systems, not only is a pollution level sensor installed but also an integrated oil sensor (TE FPS 2000) to diagnose the condition of hydraulic oil [41,42]. Moreover, machine learning and artificial intelligence approaches have also been used for condition monitoring with oil analysis [43].
As mentioned above, oil diagnosis plays a very important role in achieving good maintenance of construction equipment. To diagnose the condition of hydraulic oil using an oil sensor, some basic knowledge is needed to determine the main property or reference value for the diagnostic standard. Although the results of a field test provide useful information, it is difficult to create a guideline for diagnosing the condition of hydraulic oil because of the results of various contamination sources. In other words, it is difficult to determine the standard value or guideline for condition diagnosis when measuring hydraulic oil that has been affected by various pollutants. It is more efficient to classify the pollutant sources in a laboratory setting through a simple experimental device. This paper presents guidelines for diagnosing the condition of hydraulic oil using an existing integrated oil sensor. Using a simple experimental device with a monitoring program, variations in properties according to temperature were investigated for several contamination conditions of hydraulic oil. In particular, for construction equipment, the results effectively show a direct relationship between varnish contamination and the dielectric constant of hydraulic oil when using an oil sensor that can measure the dielectric constant via a simple experimental device.

2. Integrated Oil Sensor and Experimental Device

In this study, an integrated oil sensor (TE FPS 2000) was used for diagnosing the condition of hydraulic oil in construction equipment, as shown in Figure 5. The measurement parameters, measurement range, and accuracy of this sensor are listed in Table 1. This sensor is based on a tuning fork flexural resonator. The resonator is made of quartz, a piezoelectric material that is capable of deformation upon the application of a voltage and reciprocal electrical polarization under mechanical stress. The two tines of the fork oscillate and generate a response indicative of the physicochemical and electrical properties of the lubricant in which the sensor is immersed. The application of a sinusoidal excitation voltage to the tuning fork’s thin electrodes causes mechanical stress and periodic elastic deformation. This vibration produces a corresponding current through the electrodes. The impedance of the system can be measured based on the ratio of the excitation voltage to the induced current. This process is dependent on the excitation frequency, the elastic properties of the piezoelectric material, and the properties of the fluid [44]. Moreover, this sensor can be used under pressure conditions of up to 25 bar. It is impossible to conduct measurements where there is no flow of fluid. Therefore, it is difficult to diagnose oil condition when the machine is stopped or stagnant.
For the lubricating oil, VG46 oil was used, which is used as a hydraulic oil for construction equipment; its properties are listed in Table 2.
The present work developed a condition monitoring program that can diagnose the condition of hydraulic oil, as shown in Figure 6. The program was developed using JAVA; it can be executed once the reference values for properties such as temperature and dielectric constant are set, and a communication port can be installed as shown in Figure 6a. The reference values for the properties were determined based on the research experience of the manufacturer. The sampling frequency used for digital processing of the measured data was 0.033 Hz. When the program was executed, it showed variation in the measured properties as both a graph and a digital value (Figure 6b). Moreover, depending on the set reference values, the current state of the lubricant was displayed as one of three stages: normal, caution, and danger. The oil condition was not only displayed as a traffic light but also through an alert with a warning sound at a caution or danger level. Data that exceeded the set reference value were automatically stored in a separate space for parts, which facilitates analysis.
Figure 7 shows the flow of the signal and power supply in the monitoring system. The integrated oil sensor communicates with CAN; therefore, Arduino and CAN shield were used for signal processing.
Figure 8 shows the experimental setup. The amount of lubricant used in the experiment was 400 mL, which is approximately half the volume of one beaker. The temperature of the lubricant was controlled by a heater and temperature probe. The temperature of the heater could be adjusted in 1 °C increments. A cross-shaped magnetic bar was rotated at 250 rpm to stir of the lubricant. At a rotation of 250 rpm or more, a large vortex is generated near the center of the beaker, which interferes with the measurements obtained by the sensor.
Contamination of hydraulic oil can result from various sources. In addition to mixing with the wrong lubricant, there can also be infiltration of moisture and dust, along with the generation of wear particles. In fact, the possibility of moisture and dust infiltration is higher than that of other machines. Sludge may be generated due to processes such as oxidation, and often leads to varnish contamination. The experiments in the study were conducted under the conditions of mixing with the wrong lubricant, infiltration of dust and moisture, and varnish contamination. Wear particles could also occur, but the generation of ferrous particles was excluded. This was because a magnetic bar was used to stir the lubricant when introducing foreign materials, and therefore, artificial ferrous particles were collected at the magnetic bar rather than being dispersed in the lubricant. Distilled water was used for moisture and VG 64 hydraulic oil with high viscosity was used as the wrong lubricant. The artificial dust used in the experiment was ISO 12103-1 Arizona Test Dust from PTI, which consists of the components presented in Table 3. As can be seen in Table 3, the dust is comprised of SiO2 and Al2O3, which are the main components of soil dust.

3. Results

First, an experiment testing uncontaminated oil was conducted in order to investigate the different properties depending on temperature before conducting experiments on the effect of contamination. Measurement was conducted by starting from 20 °C, gradually increasing the temperature to 85 °C, and then lowering the temperature to 20 °C again. This process took approximately 3 h. The tests on contamination conditions were carried out in the same way, and compared with the test results for uncontaminated oil.

3.1. Test on Uncontaminated Oil

Figure 9a shows the results for absolute viscosity given varying temperature. The number of data obtained in the experiment was 2699. Each square block dot is an experimental datum obtained from the sensor, while the red solid line is the curve fitting. The root mean square of the curve fitting was 0.985. The relationship between temperature and absolute viscosity obtained via regression could be expressed as shown in Equation (1). In the formula, AV and T are absolute viscosity and temperature in degrees Celsius, respectively. Typically, viscosity allows for a range of −20% to 30% relative to the value of uncontaminated oil. Therefore, using curve fitting, the upper and lower limits are indicated by brown dotted lines and green lines with triangles for the allowable range.
Figure 9b shows the dielectric constant given varying temperature, and the number of data obtained was 1946. The red solid line was obtained through linear regression. Equation (2) (red solid line) shows the relationship between the dielectric constant and temperature. In the formula, DC is the dielectric constant and T is the temperature in degrees Celsius. The root mean square associated with error is 0.00538. The dielectric constant data had an almost constant width, and the width of the variation was expressed as equations using the regression method. Moreover, the width of the variation indicates the range allowable due to measurement error. The lower and upper limits were drawn using Linear fitting-2 (Equation (3)) and Linear fitting-3 (Equation (4)) in Figure 9b.
AV = 9.58 + 416.11 × e(−T/15.84)
DC = −0.0022 × T + 2.34
DC = −0.0022 × T + 2.32
DC = −0.0022 × T + 2.35
Figure 9c shows density given varying temperature. The densities obtained from the sensor had a large dispersion, so there was no need for regression analysis. Moreover, the measured density values were not used when testing changes in the properties of the lubricant in response to the incorporation of foreign materials.
Figure 10 shows the relationship between density and temperature using data obtained from a lubricant manufacturer. The red solid line was obtained through linear regression. Equation (5) shows the relationship between density and temperature; ρ is density and T is temperature in degrees Celsius.
ρ = −6.03 × 10−4 × T + 0.85

3.2. Influence of Incorporation of Dust and Improper Lubricant

Figure 11 presents the absolute viscosity and dielectric constant given varying temperature when artificial dust was introduced into uncontaminated oil at quantities ranging from 60 mg to 400 mg. Figure 12 shows the absolute viscosity and dielectric constant given varying temperature when improper oil was introduced into uncontaminated oil at quantities ranging from 0.4 mL (1000 ppm) to 4.0 mL (10,000 ppm). VG 64 hydraulic oil was used as the improper oil. In Figure 11 and Figure 12, it can be seen that the amounts of artificial dust and improper oil used in the experiment did not significantly affect the variation in the properties of the lubricant as a whole. Of course, it is believed that the incorporation of very large amounts would alter the variations in the properties of lubricating oil. Moreover, tests were not carried out under mechanical operating conditions after introducing dust or improper oil, and sufficient time was not given for the oxidation and deterioration of lubricant. To compensate for this, field experiments on actual construction machines were conducted, as shown in Figure 13, and additional research results will be presented in another paper.

3.3. Influence of Incorporation of Moisture

The influence of the incorporation of moisture was investigated via categorizing levels of introduced moisture into two groups: 3000 ppm or less, or more than 3000 ppm. Figure 14 shows the variation in absolute viscosity and dielectric constant given varying temperature and amount of moisture when mixing with water at levels of 3000 ppm or less. As the amount of introduced moisture increased, absolute viscosity decreased slightly, but the variation was not large. Meanwhile, dielectric constant increased with increased moisture amounts, and the variation was relatively large. Figure 15 shows the variation in absolute viscosity and dielectric constant given varying temperature when mixing with water at levels of 3000 ppm or more. The results in Figure 15 indicate the same tendency as shown in Figure 14, but the variation in the dielectric constant was much larger because there was a larger amount of introduced moisture. It can be seen that the incorporation of moisture reacted sensitively to changes in the dielectric constant, unlike the previous results for the introduction of dust and improper lubricant. To analyze the results, it is necessary to understand the dielectric constant. The basic concepts and properties of the dielectric constant are as follows.
The dielectric constant (DC) is a term for relative permittivity; it provides information on variations in the physical properties of a lubricant. It also provides information on chemical changes in lubricants, such as oxidation or depletion of additives. The dielectric constant is expressed as a ratio of permittivity or a ratio of capacitance, as can be seen in Equation (6). Permittivity refers to the degree that a material transmits an electric field, while capacitance means the ability of a capacitor or ability to store an electric charge. That is, the dielectric constant represents the ratio of the capacitance (C’) or permittivity (εm) of a material to capacitance (Co) or permittivity (ε0) in a vacuum.
DC = εm/ε0 = C’/C0
μ1 = αE
(ε−1)/(ε + 2) = (α + μ12/(3kT1))(Lρ/(3MW)
The dielectric constant is largely expressed as the sum of two types of polarizability (α) and the dipole moment (μ1). Polarization is the interaction of electrons in a molecule with an electric field, while the dipole moment is evaluated by measuring the center of gravity of the positive and negative charges in the molecule. If the two centers do not coincide, then the molecule is electrically asymmetric, in which case it has a net polarity and therefore a permanent dipole. Equation (7) shows the relationship between dipole moment (α), electric field (E), and polarization (μ1). Equation (8) shows the Debye formula for permittivity, where ε is the permittivity of oil, k is the Boltzmann constant, T1 is temperature in Kelvin, L is the Avogadro number, ρ is the density of oil, and MW is the molecular weight of oil. The (Lρ/3MW) term represents the volume of a single molecule, and kT1 means the amount of thermal energy available [45,46,47]. These formulas for permittivity help materially understand the experimental dielectric constant results.
Lubricant typically has a dielectric constant ranging from 2.1 to 2.4. This depends on the viscosity of the oil, the oil density, the relative paraffinic, naphthenic, and aromatic contents, the temperature, the frequency of the electric field, and the additive content of the oil. Higher additive levels tend to increase the dielectric constant of uncontaminated oil because additives themselves have higher dielectric constants than base oil, and there is no dipole moment contribution in the base oil [48]. Looking at the results of the dielectric constant in Figure 14 and Figure 15, the influence of moisture incorporation is obvious. The incorporation of moisture was relatively sensitive to variations in dielectric constant. This is because water has a relatively large and temperature-dependent dielectric constant due to its permanent electric dipole and the resultant effects of hydrogen bonding. Therefore, it is useful to utilize a lubricant sensor that can measure dielectric constant to determine the degree of moisture contamination in hydraulic oil.
Each mechanical system has a different moisture tolerance for its lubricants. In general, the allowable moisture capacity of hydraulic oil for construction machinery is 2000 ppm. Therefore, Figure 16 shows the allowable range of the dielectric constant when 2000 ppm of moisture was introduced. The red solid line is the result of linear regression. Equation (9) shows the relationship between dielectric constant and temperature when adding 2000 ppm of moisture. Since the dielectric constant data had an almost constant width, the lower and upper limits were drawn with Linear fitting-2 (Equation (10)) and Linear fitting-3 (Equation (11)), exhibited in Figure 16. In Equations (9)–(11), temperature (T) is given in degrees Celsius.
DC = −0.0018 × T + 2.33
DC = −0.0018 × T + 2.32
DC = −0.0018 × T + 2.34

3.4. Influence of Varnish

Various lubricant deterioration issues can affect lubrication systems. One of the serious problems in such systems is the presence of sludge and varnish. These products enter the lubricant in a dissolved form and accumulate until the lubricant reaches its capacity, which is referred to as the saturating point, where any excess is forced to convert into insoluble degradation products. Over time, some deposits can thermally cure to a tough enamel-like coating. Deposits that form on sensitive machines interfere with the flow of lubricant and the mechanical movements. The deposits can also contribute to wear and corrosion or impair heat transfer by clinging to surfaces [49]. Construction equipment also faces issues with varnish.
An experiment was conducted on hydraulic oil used in construction equipment for 4156 h. At this time, the degree of contamination with varnish was identified using MPC (Membrane Patch Colorimetry). MPC testing was performed using RM200 of FLUITEC. Figure 17 shows the sludge filtered onto the membrane patch by the MPC test. The MPC result was 95.1. Unfortunately, there is no MPC test criterion for hydraulic oil in construction equipment. Therefore, the MPC criterion provided by NORIA (company) for turbine oil was used as a baseline. When the result of the MPC test is less than 15, NORIA considers it to be normal; when it is 30 or more, it is considered abnormal; and when it exceeds 40, the varnish contamination is judged to be serious [49].
Table 4 shows the results of off-line analysis comparing unused oil to the oil used in the experiment. Elemental analysis was performed using ICP (Inductively Coupled Plasma). The kinematic viscosity of the used oil was lower than that of unused oil. This was because of the decrease in viscosity that occurred due to shear during operation. The increase in aluminum and silicon in used oil was attributed to the incorporation of soil dust. The reason for this is that the main components of soil are aluminum and silicon. The decrease in phosphorus and zinc in used oil occurred because anti-wear additives disappeared due to abrasion resistance and friction reduction action; the increase in iron, copper, and antimony was considered to be a phenomenon caused by wear.
Figure 18 shows the variation in absolute viscosity with changes in temperature for unused and used oil. The block square dots indicate the results for unused oil, and the red circle dots indicate those for used oil. The area between the upper guideline and the lower guideline indicates the allowable range of absolute viscosity, as shown in Figure 9a. The viscosity of the used oil was lower than that of unused oil. This is a phenomenon caused by shear, as mentioned earlier in the results of the off-line oil analysis. However, the viscosities of the used oil were within the allowable range. In this way, it is possible to diagnose the condition of the lubricant and the machine through real-time viscosity monitoring after setting the allowable range of viscosity.
Figure 19 represents the variation in dielectric constant with changes in temperature for unused and used oil. The block square dots indicate the results for unused oil and the red circle dots indicate those for used oil. The blue triangle dots are the results when 2000 ppm of moisture was mixed with the unused oil, and the data are the same as shown in Figure 14a. The range of measured values is indicated by the upper and lower limit lines. The dielectric constant exhibited by the used oil with its varnish contamination was larger than that of the unused oil contaminated with 2000 ppm of moisture. As oxidation proceeds, AN also increases, and residues such as sludge form, causing varnish contamination. This phenomenon can also be understood via the Debye formula (Equation (8)). An explanation of the reason for this is as follows. When lubricant is contaminated with varnish, the dielectric constant increases substantially, because of increases in molecular weight and AN due to oxidation and deterioration [49]. Furthermore, metal elements such as iron and copper were increased in used oil according to the off-line oil analysis. Metals have an infinite dielectric constant because they are conductors. As a result, the increased metal elements in used oil contribute to increases in the dielectric constant [48]. This result indicates that, when the allowable levels of varnish contamination are determined, the varnish contamination of hydraulic oil can be diagnosed by measuring the dielectric constant. The variation in dielectric constant caused by varnish contamination is clearly visible and can be identified.
In this study, various experiments were conducted on various sources of contamination of hydraulic oil for construction equipment. In actual hydraulic oil in the field, various types of pollution, such as oxidation and infiltration of dust or moisture, may proceed simultaneously. In other words, it is difficult to analyze the conditions of hydraulic oil solely by monitoring the properties obtained by an integrated oil sensor without research results on the variation in properties caused by each pollutant. It is also difficult to analyze data obtained in the field because such data contain noise. Furthermore, precise analysis in the laboratory, such as elemental analysis, is needed to understand the causes. Therefore, this study provides basic data necessary for diagnosing the condition of hydraulic oil in construction equipment. In particular, it suggests the possibility of using the dielectric constant, which is sensitive to contamination with moisture and varnish. In addition, these results are expected to serve as a basis for analyzing the results of field tests and to be useful for establishing oil diagnostic standards. Each manufacturer of construction equipment has its own management standards for hydraulic oil. When important properties and values corresponding to these management standards are found using integrated oil sensors, oil condition diagnosis using the sensors is possible. In that respect, this study shows that it is possible to diagnose varnish and moisture contamination by obtaining the dielectric constant through an integrated oil sensor.

4. Conclusions

In this study, experiments were conducted related to diagnosing the condition of hydraulic oil for construction equipment. These results provide guidelines for condition monitoring via integrated oil sensors, and are useful for assessing the practicality of condition diagnosis using oil analysis. The integrated oil sensor that was used could measure the absolute viscosity, density, temperature, and dielectric constant of hydraulic oil. The experimental results obtained in field tests are useful, but it is difficult to estimate variations in the properties of hydraulic oil caused by the influence of each pollutant. By contrast, in this study, the influence of each pollutant could be efficiently estimated, and the degree of variation in the main properties could be easily grasped. It was also possible to find the main variations in properties related to major contaminants in construction equipment. To achieve this, a simple experimental device was constructed, and a condition monitoring program was used to investigate the influence of foreign materials and varnish contamination. There was little variation in the properties of the lubricant when introducing small amounts of dust or improper oil. On the other hand, a distinct variation in properties appeared when a small amount of moisture was introduced. Absolute viscosity was minimally reduced due to the incorporation of moisture, but dielectric constant was reflected relatively sensitively. Moreover, when varnish contamination occurred, the dielectric constant showed distinct variation compared to that of uncontaminated oil. This was because the incorporation of moisture substantially affected the dipole moment, and the generation of varnish also affected the dipole moment due to oxidation. Through these experimental results, it can be seen that measuring the dielectric constant was an effective means for diagnosing the condition of hydraulic oil. By comparison, because the incorporation of a small amount of dust and improper oil did not have a large effect on the variation in dielectric constant, condition diagnosis through the electric constant was not useful in such cases. Following this experiment, additional confirmation work through field tests is required. Moreover, in order to analyze the results of chemical reactions induced by the foreign materials, tests of longer time period must be performed after the incorporation of foreign materials.

Author Contributions

Conceptualization, S.-H.H. and H.-G.J.; literature review and formal analysis, S.-H.H.; writing—original draft preparation, methodology. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Not applicable.

Acknowledgments

This work was supported by the Dongguk University Research Fund of 2021.

Conflicts of Interest

The authors declare no conflict of interest.

Nomenclature

AVAbsolute viscosity (cP)
CoCapacitance in a vacuum
C’Capacitance of a material (lubricant)
DCDielectric constant of fluid
EElectric field
LAvogadro Number (6.02 × 1023 molecules of oil /mole)
MWMolecular weight (g/mole)
TTemperature in degrees Celsius (°C)
T1Temperature Kelvin
kBoltzmann constant (1.31 × 10−23 joules/degree Kelvin)
αPolarizability
εPermittivity
εoPermittivity in a vacuum
εmPermittivity of a material(lubricant)
μ1Dipole moment
ρDensity of fluid (g/m3)

References

  1. Waris, M.; Shahir Liew, M.; Faris Khamidi, M.; Idrus, A. Criteria for the selection of sustainable on site construction equipment. Int. J. Sustain. Built Environ. 2014, 3, 96–110. [Google Scholar] [CrossRef] [Green Version]
  2. Naskoudakis, I.; Petroutsatou, K. A thermatic review of main researches on construction equipment over the recent years. Procedia Eng. 2016, 164, 206–213. [Google Scholar] [CrossRef]
  3. Mishra, A.K.; Aryal, B. Operational maintenance analysis of actively utilized road construction equipments. Mater. Today Proc. 2021, in press. [Google Scholar] [CrossRef]
  4. Premjith, S.P.; Monisha, M. Analysis of equipment maintenance operation and repair in a construction industry. Int. J. Eng. Manag. Res. 2017, 7, 18–23. [Google Scholar]
  5. Hong, S.H. Literature review of machine condition monitoring with oil sensors—Types of Sensors and their functions. Tribol. Lubr. 2020, 36, 297–306. [Google Scholar]
  6. Kumar, M.; Mukherjee, P.S.; Misra, N.M. Advancement and current status of wear debris analysis for machine condition monitoring: A review. Ind. Lubr. Tribol. 2013, 65, 3–11. [Google Scholar] [CrossRef]
  7. Younus, A.M.D.; Yang, B.S. Intelligent fault diagnosis of rotating machinery using infrared thermal image. Expert Syst. Appl. 2012, 39, 2082–2091. [Google Scholar] [CrossRef]
  8. Hong, S.H. Machine Condition Diagnosis Based on Oil Analysis—Fundamental Course, 1st ed.; Hanteemedia: Seoul, Korea, 2021; pp. 94–96. [Google Scholar]
  9. Global Machine Condition Monitoring Market Size, Share & Industry Trends Analysis Report by Offering. Available online: https://www.kbvresearch.com/machine-condition-monitoring-market (accessed on 20 July 2022).
  10. Markova, L.V.; Makarenko, V.M.; Semenyuk, M.S.; Zozulya, A.P. On-line monitoring of the viscosity of lubricating oils. J. Frict. Wear 2010, 31, 433–442. [Google Scholar] [CrossRef]
  11. Markova, L.V.; Myshkin, N.K.; Kong, H.; Han, H.G. On-line acoustic viscometry in oil condition monitoring. Tribol. Int. 2011, 44, 963–970. [Google Scholar] [CrossRef]
  12. Saluja, A.; Kalonia, D.S. Measurement of fluid viscosity at microliter volumes using quartz impedance analysis. AAPS PharmSciTech 2004, 5, e47. [Google Scholar] [CrossRef]
  13. Salzer, S.; Jahns, R.; Piorra, A.; Teliban, I.; Reermann, J.; Höft, M.; Quandt, E.; Knöchel, R. Tuning fork for noise suppression in magnetolectric sensors. Sens. Actuators A Phys. 2016, 237, 91–95. [Google Scholar] [CrossRef]
  14. Heinisch, M.; Voglhuber-Brunnmaier, T.; Reichel, E.K.; Dufour, I.; Jakoby, B. Application of resonant steel tuning forks with circular and rectangular cross sections for precise mass density and viscosity measurements. Sens. Actuators A Phys. 2015, 226, 163–174. [Google Scholar] [CrossRef]
  15. García-Arribas, A.; Gutiérrez, J.; Kurlyandskaya, G.V.; Barandiarán, J.M.; Svalov, A.; Fernández, E.; Lasheras, A.; De Cos, D.; Bravo-Imaz, I. Sensor applications pf soft magnetic materials based on magneto-impedance, magneto-elastic resonance and magneto-electricity. Sensors 2014, 14, 7602–7624. [Google Scholar] [CrossRef] [Green Version]
  16. Zhu, X.; Zhong, C.; Zhe, J. Lubricating oil condition sensors for online machine health monitoring—A review. Tribol. Int. 2017, 109, 473–484. [Google Scholar] [CrossRef] [Green Version]
  17. Du, L.; Zhe, J.; Carletta, J.; Veillette, R.; Choy, F. Real-time monitoring of wear debris in lubricating oil using a microfluidic inductive coulter counting device. Microfluid. Nanofluid. 2010, 9, 1241–1245. [Google Scholar] [CrossRef]
  18. Gastops Long Live Equipment. MetalSCAN MS4000. Available online: http://www.gastops.com/wpcontent/uploads/2016/09/C008850_001.pdf (accessed on 16 December 2020).
  19. Du, L.; Zhe, J. A high throughput inductive pulse sensor for online oil debris monitoring. Tribol. Int. 2011, 44, 175–179. [Google Scholar] [CrossRef]
  20. Du, L.; Zhe, J. Parallel sensing of metallic wear debris in lubricants using under-sampling data processing. Tribol. Int. 2012, 53, 28–34. [Google Scholar] [CrossRef]
  21. Du, L.; Zhu, X.; Han, Y.; Zhao, L.; Zhe, J. Improving sensitivity of an inductive pulse sensor for detection of metallic wear debris in lubricants using parallel LC resonance method. Meas. Sci. Technol. 2013, 24, 75106. [Google Scholar] [CrossRef]
  22. Wang, C.; Bai, C.; Yang, Z.; Zhang, H.; Li, W.; Wang, X.; Zheng, Y.; Ilerioluwa, L.; Sun, Y. Research on High Sensitivity Oil Debris Detection Sensor Using High Magnetic Permeability Material and Coil Mutual Inductance. Sensors 2022, 22, 1833. [Google Scholar] [CrossRef]
  23. Wu, Z.; Liu, Z.; Yuan, H.; Yu, K.; Gao, Y.; Liu, L.; Pan, X. Multichannel inductive sensor based on phase division multiplexing for wear debris detection. Micromachines 2019, 10, 246. [Google Scholar] [CrossRef] [Green Version]
  24. Wang, Y.; Lin, T.; Wu, D.; Zhu, L.; Qing, X.; Xue, W. A new in situ coaxial capacitive sensor network for debris monitoring of lubricating oil. Sensors 2022, 22, 1777. [Google Scholar] [CrossRef] [PubMed]
  25. Xu, C.; Zhang, P.; Wang, H.; Li, Y.; Lv, C. Ultrasonic echo wave shape features extraction based on QPSO-matching pursuit for online wear debris discrimination. Mech. Syst. Signal Process. 2015, 60, 301–315. [Google Scholar] [CrossRef]
  26. Du, L.; Zhe, J. An integrated ultrasonic-inductive pulse sensor for wear debris detection. Smart Mater. Struct. 2013, 22, 25003. [Google Scholar] [CrossRef]
  27. Hamilton, A.; Cleary, A.; Quail, F. Development of a novel wear detection system for wind turbine gearboxes. IEEE Sens. J. 2014, 14, 465–473. [Google Scholar] [CrossRef]
  28. Wu, T.; Wu, H.; Du, Y.; Kwok, N.; Peng, Z. Imaged wear debris separation for on-line monitoring using gray level and integrated morphological features. Wear 2014, 316, 19–29. [Google Scholar] [CrossRef]
  29. Liu, Z.; Liu, Y.; Zuo, H.; Wang, H.; Chen, Z. An oil wear particles inline optical sensor based on motion characteristics for rotating machines condition monitoring. Machines 2022, 10, 727. [Google Scholar] [CrossRef]
  30. Holzki, M.; Fouckhardt, H.; Klotzbucher, T. Evanescent-field fiber sensor for the water content in lubricant oils with sensitivity increase by dielectrophoresis. Sens. Actuators A Phys. 2012, 184, 93–97. [Google Scholar] [CrossRef]
  31. Smiechowski, M.F.; Lvovich, V.F. Iridium oxide sensors for acidity and basicity detection in industrial lubricants. Sens. Actuators B Chem. 2003, 96, 261–267. [Google Scholar] [CrossRef]
  32. Soleimani, M.; Sophocleous, M.; Wang, L.; Atkinson, J.; Hosier, I.L.; Vaughan, A.S.; Taylor, R.I.; Wood, R.J.K. Base oil oxidation detection using novel chemical sensors and impedance spectroscopy measurements. Sensors Actuators B Chem. 2014, 199, 247–258. [Google Scholar] [CrossRef] [Green Version]
  33. Kimura, Y.; Kato, N.; Kataoka, T.; Takajashi, K.; Kikuchi, T. Bubble behavior in engine lubricant. Int. J. Automot. Eng. 2011, 2, 149–153. [Google Scholar] [CrossRef] [Green Version]
  34. Zhu, J.; Eric, B.; David, H. Survey of lubrication oil condition monitoring, diagnostics, prognostics techniques and systems. J. Chem. Sci. Technol. 2013, 3, 100–115. [Google Scholar]
  35. John, D. A novel approach to predictive maintenance: A portable multicomponent MEMS sensor for on-line monitoring of fluid condition in hydraulic and lubricating systems. Tribol. Trans. 2006, 49, 545–553. [Google Scholar]
  36. Kobayashi, S.; Kondoh, J. Properties of engine oil measured using a surface acoustic wave sensor. Jpn. J. Appl. Phys. 2018, 57, 07LD09. [Google Scholar] [CrossRef]
  37. Podunavac, I.; Radonic, V.; Bengin, V.; Jankovic, N. Microwave spoof surface plasmon polariton-based sensor for ultrasensitive detection of liquid analyte dielectric constant. Sensors 2021, 21, 5477. [Google Scholar] [CrossRef] [PubMed]
  38. Esmaeili, K.; Wang, L.; Harvey, T.J.; White, N.M.; Holwager, W. Electrical discharges in oil-lubricated rolling contacts and their detection using electrostatic sensing technique. Sensors 2022, 22, 392. [Google Scholar] [CrossRef] [PubMed]
  39. Jiang, Y.; He, X. Overview of applications of the sensor technologies for construction machinery. IEEE Access 2020, 8, 110324–110335. [Google Scholar] [CrossRef]
  40. Singh, M.; Lathkar, G.S.; Basu, S.K. Failure prevention of hydraulic system based on oil contamination. J. Inst. Eng. 2012, 93, 269–274. [Google Scholar] [CrossRef]
  41. Ng, F.; Harding, J.A.; Glass, J. Improving hydraulic excavator performance through in line hydraulic oil contamination monitoring. Mech. Syst. Signal Process. 2017, 83, 176–193. [Google Scholar] [CrossRef] [Green Version]
  42. Zeng, J.; Zhao, C.; Zhang, M.; Zhao, M. Study condition monitoring of construction equipment based on wireless sensor networks. In Proceedings of the 2010 International Conference on Computer Application and System Modeling, Taiyuan, China, 22–24 October 2010. [Google Scholar]
  43. Marian, M.; Tremmel, S. Current trends and applications of machine learning in tribology—A review. Lubricants 2021, 9, 86. [Google Scholar] [CrossRef]
  44. Sun, H.; Liu, Y.; Tan, J. Research on testing method of oil characteristic based on quartz tuning fork sensor. Appl. Sci. 2021, 11, 5642. [Google Scholar] [CrossRef]
  45. Carey, A.A. The Dielectric Constant of Lubrication Oils. DTIC Document. Available online: https://apps.dtic.mil/sti/citations/ADA347479 (accessed on 12 July 2022).
  46. Nelson, S.O.; Trabelsi, S. Factors influencing the dielectric properties of agricultural and food products. J. Microw. Power Electromagn. Energy 2012, 46, 93–107. [Google Scholar] [CrossRef] [PubMed]
  47. Liu, M.; Qin, X.; Chen, Z.; Tang, L.; Borom, B.; Cao, N.; Barnes, D.; Cheng, K.; Chen, J.; Wang, T.; et al. Frying Oil Evaluation by a Portable Sensor Based on Dielectric Constant Measurement. Sensors 2019, 19, 5375. [Google Scholar] [CrossRef] [PubMed]
  48. Dielectric Constant and Oil Analysis. Available online: https://www.machinerylubrication.com/Read/226/dielectric-constant-oil-analysis (accessed on 12 July 2022).
  49. Sludge and Varnish in Turbine Systems. Available online: http://www.machinerylubrication.com/Read/874/sludge-varnish-turbine (accessed on 3 August 2022).
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Figure 1. Types of machine condition monitoring.
Figure 1. Types of machine condition monitoring.
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Figure 2. Methods of lubricant analysis.
Figure 2. Methods of lubricant analysis.
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Figure 3. Machine condition monitoring market share in 2021 [9].
Figure 3. Machine condition monitoring market share in 2021 [9].
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Figure 4. Interrelation of dielectric constant and AN over time [35].
Figure 4. Interrelation of dielectric constant and AN over time [35].
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Figure 5. Overview of quartz tuning fork sensor (TE FPS 2000).
Figure 5. Overview of quartz tuning fork sensor (TE FPS 2000).
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Figure 6. Monitoring program for integrated oil sensor: (a) setting screen; (b) monitoring screen.
Figure 6. Monitoring program for integrated oil sensor: (a) setting screen; (b) monitoring screen.
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Figure 7. Power and signal circuits.
Figure 7. Power and signal circuits.
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Figure 8. Experimental device.
Figure 8. Experimental device.
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Figure 9. Variation in properties of uncontaminated oil with changes in temperature: (a) absolute viscosity; (b) dielectric constant; (c) density.
Figure 9. Variation in properties of uncontaminated oil with changes in temperature: (a) absolute viscosity; (b) dielectric constant; (c) density.
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Figure 10. Variation in density of uncontaminated oil with changes in temperature (obtained from oil manufacturer).
Figure 10. Variation in density of uncontaminated oil with changes in temperature (obtained from oil manufacturer).
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Figure 11. Variation in properties after dust introduction with changes in temperature: (a) absolute viscosity; (b) dielectric constant.
Figure 11. Variation in properties after dust introduction with changes in temperature: (a) absolute viscosity; (b) dielectric constant.
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Figure 12. Variation in properties after improper lubricant introduction with changes in temperature: (a) absolute viscosity; (b) dielectric constant.
Figure 12. Variation in properties after improper lubricant introduction with changes in temperature: (a) absolute viscosity; (b) dielectric constant.
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Figure 13. Data acquisition using the integrated oil sensor in field tests.
Figure 13. Data acquisition using the integrated oil sensor in field tests.
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Figure 14. Variation in properties when introducing 3000 ppm or less of moisture with changes in temperature: (a) absolute viscosity; (b) dielectric constant.
Figure 14. Variation in properties when introducing 3000 ppm or less of moisture with changes in temperature: (a) absolute viscosity; (b) dielectric constant.
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Figure 15. Variation in properties when introducing more than 3000 ppm of moisture with changes in temperature: (a) absolute viscosity; (b) dielectric constant.
Figure 15. Variation in properties when introducing more than 3000 ppm of moisture with changes in temperature: (a) absolute viscosity; (b) dielectric constant.
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Figure 16. Variation in dielectric constant with changes in temperature at 2000 ppm introduced moisture.
Figure 16. Variation in dielectric constant with changes in temperature at 2000 ppm introduced moisture.
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Figure 17. MPC test of used oil (4156 h).
Figure 17. MPC test of used oil (4156 h).
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Figure 18. Variation in absolute viscosity with changes in temperature for unused and used oil.
Figure 18. Variation in absolute viscosity with changes in temperature for unused and used oil.
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Figure 19. Variation in dielectric constant with changes in temperature for unused and used oil.
Figure 19. Variation in dielectric constant with changes in temperature for unused and used oil.
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Table
Table 1. Specifications of TE FPS 2000.
Table 1. Specifications of TE FPS 2000.
Measurement PropertiesMeasurement RangeAccuracy
Absolute viscosity (cP)0.5~50±2%
Temperature (°C)−40~150±0.1 °C
Density (g/cm3)0.65~1.5±1%
Dielectric constant1.0~6.0±1%
Table
Table 2. Properties of hydraulic oil before testing.
Table 2. Properties of hydraulic oil before testing.
DensityKinematic
Viscosity (cSt)		Viscosity
Index		Flash
Point (°C)		Pour
Point (°C)
(g/L @15 °C)@ 40 °C@ 100 °C
845.746.377.97144253−45
Table
Table 3. Components of artificial dust.
Table 3. Components of artificial dust.
Ingredients% of WeightIngredients% of Weight
SiO269.0~77.0CaO2.5~5.5
Al2O38.0~14.0MgO1.0~2.0
Fe2O34.0~7.0TiO20~1.0
Na2O1.0~4.0K2O2.0~5.0
Table
Table 4. Results of off-line oil analysis.
Table 4. Results of off-line oil analysis.
Measurement ItemsUnused OilUsed Oil (4156 h)
Viscosity @ 40 ℃ (cSt)48.2846.36
Viscosity @ 100 ℃ (cSt)8.237.90
Viscosity Index145.0141.0
Magnesium, Mg (ppm)0.90.3
Calcium, Ca (ppm)74.567.6
Phosphorus, P (ppm)463.2400.0
Zinc, Zn (ppm)725.8566.3
Silicon, Si (ppm)0.01.5
Boron, B (ppm)0.00.1
Sodium, Na (ppm)0.12.2
Potassium, K (ppm)0.00.3
Iron, Fe (ppm)0.132.2
Lead, Pb (ppm)0.10.3
Copper, Cu (ppm)0.03.8
Tin, Sn (ppm)0.01.0
Aluminum, Al (ppm)0.00.6
Molybdenum, Mo (ppm)0.20.4
Titanium, Ti (ppm)0.00.2
Antimony, Sb (ppm)1.32.0
Manganese, Mn (ppm)0.00.8
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Hong, S.-H.; Jeon, H.-G. Monitoring the Conditions of Hydraulic Oil with Integrated Oil Sensors in Construction Equipment. Lubricants 2022, 10, 278. https://doi.org/10.3390/lubricants10110278

AMA Style

Hong S-H, Jeon H-G. Monitoring the Conditions of Hydraulic Oil with Integrated Oil Sensors in Construction Equipment. Lubricants. 2022; 10(11):278. https://doi.org/10.3390/lubricants10110278

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Hong, Sung-Ho, and Hong-Gyu Jeon. 2022. "Monitoring the Conditions of Hydraulic Oil with Integrated Oil Sensors in Construction Equipment" Lubricants 10, no. 11: 278. https://doi.org/10.3390/lubricants10110278

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