Lubricating Oil Condition Research Articles

Abnormal identification of oil monitoring based on LSTM and SVDD

Oil monitoring has been considered as an essential tool in determining the operational state of an equipment. In other words, the data obtained from monitoring the oil represents the equipment's operational status. So, identifying the abnormal working status of the lubricating oil allows to understand its operating status in advance. Accordingly, the present study aims at proposing an anomaly identification model based on Long Short Term Memory (LSTM) and Support Vector Data Description (SVDD) for time series data of wear state collected by the oil online monitoring system. So, to predict the wear condition of the oil in the future, LSTM and grid search are utilized and a trend prediction model of oil monitoring metrics is constructed. Given that the fault is a gradual process, a multi-dimensional hypersphere classifier is generated by SVDD to train the healthy samples. The classifier is utilized to diagnose the lubricating oil condition data obtained by the LSTM prediction model as well as to realize the abnormal identification of the equipment's lubricating oil. The obtained experimental results corroborated the higher prediction accuracy and the lower prediction loss of the trend prediction model. Therefore, the future anomalies of the device can be argued to be reliably predicted in accordance with its actual performance using the LSTM-SVDD method. Furthermore, with regards to the actual operating conditions of the diesel engine, the alarm rate as well as the false alarm rate, the LSTM-SVDD method was demonstrated to be superior to the LSTM-PCA-SPE method. Hence, the novel model proposed in this study can be used to identify the status parameters of the lubricating oil abnormal wear. Moreover, the model can be argued to improve the accuracy of the abnormal identification as well as the online identification of the lubricating oil.

Contact fatigue properties of NiCrCr3C2 and AlSi coatings for sealing performance of the Wankel engine

NiCrCr3C2 and AlSi coatings were prepared using supersonic plasma equipment. The contact fatigue properties of the coatings were evaluated under dry friction, fuel oil, and lubricating oil conditions. The fatigue life of the NiCrCr3C2 coating was much longer than that of the AlSi coating at the same pressure. Under the same conditions, the NiCrCr3C2 coating had better contact fatigue performance than the AlSi coating, and due to the wettability of Cr3C2 and NiCr alloys, the Cr3C2 hard phase was firmly embedded in the NiCr alloy to ensure hardness and wear resistance at high temperatures.

Analysis of the Impact of Propanol-Gasoline Blends on Lubricant Oil Degradation and Spark-Ignition Engine Characteristics

Alcoholic fuels have recently come to light as a sustainable source for powering today’s vehicles. Various studies have investigated the effects of alcoholic fuels on engine efficiency and emission characteristics. However, scarce literature is available for their effects on lubricant. Therefore, propanol-gasoline fuel mixtures, with concentrations of 9% (P9) and 18% (P18) propanol, were made to compare their engine characteristics and lubricating oil condition with that of pure gasoline (0 percent propanol (P0)). To determine the rate of deterioration, the characteristics of the lubricating oil were evaluated after 100 h of engine operation, as suggested by the manufacturer. When compared with unused lube oil, P18 showed reductions in flash point temperature and kinematic viscosity of 14% and 36%, respectively, at 100 °C. For P18, which contains Fe (27 PPM), Al (11 PPM), and Cu (14 PPM), the highest wear element concentrations in the lubricating oil were found. The moisture in the degraded oil was well within the allowable limit for the three fuel mixtures. With the increase in propanol percentage in the propanol-gasoline blend, the engine performance was increased. Compared to P9 and P0, P18 had the partially unburned emissions.

Use of methanol-gasoline blend: a comparison of SI engine characteristics and lubricant oil condition

ABSTRACT The quest for alternatives to gasoline becomes more urgent day by day because of environmental concerns and exhaustion of fossil fuel reserves. Methanol has proved to be an attractive renewable, alternative fuel due to improvement in engine performance. This research not only addresses performance and emissions from engine but also examines the importance of lubricant oil in engine by monitoring variation in physicochemical properties after 100 hours of operations on gasoline with octane rating 92 (G-92) and also 6% methanol by volume blended with 94% gasoline by volume (M-6). The performance and emission parameters were evaluated for nine different speeds. The accuracy of emission data was then calculated using Weibull distribution within 75% and 95% confidence intervals. On average, M-6 produced 3.72% more brake power than G-92. But M-6 produced 3.40% lower HC emission, and 5.54% lower CO emission in comparison with G-92. The lubricant oil exhibited 4.76%, 1.28%, 6.03%, 0.48%, 0.045%, and 3.49% rise in (KV)40°C, (KV)100°C, TAN, ash content, specific gravity, and flashpoint for M-6, respectively. Although M-6 improves engine performance and reduces carbon emissions, it is also responsible for early degradation of lubricant oil. Therefore, efforts for reducing the degradation rate of lubricant oil will open new avenues for future research.

Experimental Characterization of Chemical Properties of Engine Oil Using Localized Surface Plasmon Resonance Sensing

The chemical properties of engine oil are part of the main parameters to evaluate its condition since oxidation starts chemical reactions that alter its operating conditions. In this work, the chemical properties of engine oil were analyzed based on the standard ASTM E2412 by FT-IR spectroscopy to evaluate the lubricating oil condition. Furthermore, a sensor based on the position of the localized surface plasmon resonance (LSPR) band of silver nanoparticles (AgNP), synthesized by the laser-assisted photoreduction method, was developed. This plasmonic sensor can detect changes in the permittivity of the oil, caused by the modification of the chemical properties of the lubricant. The response of the sensor during the study of degraded oil resulted in a notorious displacement to higher wavelengths of the LSPR band as mileage increases. The results of FT-IR analysis were correlated with the measurements of the proposed sensor presenting linear trends with good correlation (R2 > 0.9491).

A Lubricating Oil Condition Monitoring System Based on Wear Particle Kinematic Analysis in Microfluid for Intelligent Aeroengine.

Lubricating oil monitoring technology is a commonly used method in aeroengine condition monitoring, which includes particle counting technology, as well as spectral and ferrography technology in offline monitoring. However, these technologies only analyze the characteristics of wear particles and rely on physical and chemical analysis techniques to monitor the oil quality. In order to further advance offline monitoring technology, this paper explores the potential role of differences in wear particle kinematic characteristics in recognizing changes in wear particle diameter and oil viscosity. Firstly, a kinematic force analysis of the wear particles in the microfluid was carried out. Accordingly, a microfluidic channel conducive to observing the movement characteristics of particles was designed. Then, the wear particle kinematic analysis system (WKAS) was designed and fabricated. Secondly, a real-time tracking velocity measurement algorithm was developed by using the Gaussian mixture model (GMM) and the blob-tracking algorithm. Lastly, the WKAS was applied to a pin–disc tester, and the experimental results show that there is a corresponding relationship between the velocity of the particles and their diameter and the oil viscosity. Therefore, WKAS provides a new research idea for intelligent aeroengine lubricating oil monitoring technology. Future work is needed to establish a quantitative relationship between wear particle velocity and particle diameter, density, and oil viscosity.

Real-Time and Online Lubricating Oil Condition Monitoring Enabled by Triboelectric Nanogenerator.

An intelligent monitoring lubricant is essential for the development of smart machines because unexpected and fatal failures of critical dynamic components in the machines happen every day, threatening the life and health of humans. Inspired by the triboelectric nanogenerators (TENGs) work on water, we present a feasible way to prepare a self-powered triboelectric sensor for real-time monitoring of lubricating oils via the contact electrification process of oil–solid contact (O–S TENG). Typical intruding contaminants in pure base oils can be successfully monitored. The O–S TENG has very good sensitivity, which even can respectively detect at least 1 mg mL–1 debris and 0.01 wt % water contaminants. Furthermore, the real-time monitoring of formulated engine lubricating oil in a real engine oil tank is achieved. Our results show that electron transfer is possible from an oil to solid surface during contact electrification. The electrical output characteristic depends on the screen effect from such as wear debris, deposited carbons, and age-induced organic molecules in oils. Previous work only qualitatively identified that the output ability of liquid can be improved by leaving less liquid adsorbed on the TENG surface, but the adsorption mass and adsorption speed of liquid and its consequences for the output performance were not studied. We quantitatively study the internal relationship between output ability and adsorbing behavior of lubricating oils by quartz crystal microbalance with dissipation (QCM-D) for liquid–solid contact interfaces. This study provides a real-time, online, self-powered strategy for intelligent diagnosis of lubricating oils.

A review of the application of oil analysis in condition monitoring and life prediction of wind turbine gearboxes

Condition maintenance of wind turbine gearboxes is important because of their high failure probability and the difficulties associated with their maintenance. Diagnosis and prognosis are the two main aspects of condition maintenance. This paper summarises the development of fault diagnosis and life prediction methods for wind power gearboxes. Fault diagnosis methods include single-method analyses such as vibration analysis, acoustic emission (AE) analysis and oil analysis, as well as multi-information testing methods. Oil analysis can be used to monitor early wear and the wear evolution process, providing direct data for the remaining useful life (RUL) prediction of the gearbox and the lubricant. Though wind turbine gearbox RUL prediction has received more attention among these diagnoses, there is still only limited literature available regarding this. Measurement of the lubricating oil condition is one of the most often applied methods for diagnosis and prognosis and within this the oil viscosity is an important parameter. Viscosity estimation has wide application prospects in oil analysis and the tendency is to apply online testing methods. Oil viscosity can be more accurately measured by considering thermal effects, which can be studied using numerical and experimental methods. This viscosity measurement has been increasingly applied in oil analysis, with viscosity sensors. This review focuses on the application of online oil testing and measurement technology in the fault diagnosis and RUL prediction of wind turbine gearboxes. Challenging problems are identified and possible solutions are suggested in this review.

A Novel Multichannel Inductive Wear Debris Sensor Based on Time Division Multiplexing

Inductive wear debris sensor has been proved to be an effective device for lubricant oil condition monitoring and fault diagnosis. However, the narrow internal diameter of current sensing coil, designed for high sensitivity, limits the oil throughput. To improve the throughput without sacrificing the sensitivity, a novel multichannel wear debris detection method based on time division multiplexing is presented in this work. Only one excitation signal is used to excite multiple sensing coils. Then, the signals from multiple sensing coils are combined with a serials of square waves separately, which are self-designed specially. As a result, the peak waveforms are lifted in different timeslots by the high-level voltages in squares waves. After that, the peaks of the signals are cut out and combined into one output signal. Then, we used synchronized sampling method to record the peak values of the output signal. The signals for all sensing channels are finally extracted from the recorded peak values. To validate the feasibility of the proposed method, we designed a ten-channel sensor system using time division multiplexing. Through the pseudo-dynamic and dynamic test, it is proved that the sensor could detect wear debris in different channels simultaneously and independently without sacrificing the sensitivity. In addition, the proposed method has the potential to integrate more channels into one system, which would contribute to high throughput real time lubricant oil detection.

Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering

In order to reduce the costs of wind energy, it is necessary to improve the wind turbine availability and reduce the operational and maintenance costs. The reliability and availability of a functioning wind turbine depend largely on the protective properties of the lubrication oil for its drive train subassemblies such as the gearbox and means for lubrication oil condition monitoring and degradation detection. The wind industry currently uses lubrication oil analysis for detecting gearbox and bearing wear but cannot detect the functional failures of the lubrication oils. The main purpose of lubrication oil condition monitoring and degradation detection is to determine whether the oils have deteriorated to such a degree that they no longer fulfill their functions. This paper describes a research on developing online lubrication oil condition monitoring and remaining useful life prediction using particle filtering technique and commercially available online sensors. It first introduces the lubrication oil condition monitoring and degradation detection for wind turbines. Viscosity and dielectric constant are selected as the performance parameters to model the degradation of lubricants. In particular, the lubricant performance evaluation and remaining useful life prediction of degraded lubrication oil with viscosity and dielectric constant data using particle filtering are presented. A simulation study based on lab verified models is provided to demonstrate the effectiveness of the developed technique.

Field-Testing of Biodiesel (B100) and Diesel-Fueled Vehicles: Part 2—Lubricating Oil Condition Monitoring

AbstractThis study investigated the use of biodiesel (B100) and baseline diesel in two identical unmodified vehicles to realistically assess different aspects of biodiesel’s compatibility with modern common rail direct injection (CRDI) diesel engines and its effects on lubricating oil degradation and wear. Two identical vehicles were operated for 30,000 km each under identical operating conditions on highway during a field-trial while using biodiesel (B100) and baseline mineral diesel. Exhaustive experimental results from this series of tests were divided into four segments, and this paper covers the second segment showing the effect of long-term usage of biodiesel on the lubricating oil properties and traces of wear metal addition compared to baseline mineral diesel. Lubricating oil samples were drawn periodically from these vehicles for condition monitoring such as lubricating oil viscosity, density, soot content, total base number (TBN), ash content, trace metal concentrations, and thermal stability. The viscosity of lubricating oil samples drawn from biodiesel fueled vehicles were found to be ∼10–15% lower compared to that from diesel-fueled vehicles, whereas density and ash content were relatively lower by ∼5–10%. Carbon residues of lubricating oil samples drawn from B100 fueled vehicles were lower by ∼15–20% compared to that of diesel-fueled vehicles. There was a very strong reduction (∼70%) in the soot content of lubricating oil from biodiesel fueled vehicles. Trace metal analysis to compare wear debris addition was also done for all lubricating oil samples. Thermo-gravimetric analyses of lubricating oil samples from biodiesel fueled vehicles showed lower mass loss with increasing temperature hence relatively higher thermal stability and lower deterioration. Results also suggested that operational and durability issues associated with vegetable oils as alternate fuel were completely eliminated by using them after converting them into biodiesel meeting prevailing biodiesel specifications.

SI Engine Fueled with Gasoline, CNG and CNG-HHO Blend: Comparative Evaluation of Performance, Emission and Lubrication Oil Deterioration

Hydroxy gas (HHO) is one of the potential alternative fuels for spark ignition (SI) engine, notably due to simultaneous increase in engine performance and reduction in exhaust emissions. However, impact of HHO gas on lubrication oil for longer periods of engine operation has not yet been studied. Current study focuses on investigation of the effect of gasoline, CNG and CNG-HHO blend on lubrication oil deterioration along with engine performance and emissions in SI engine. HHO unit produces HHO gas at 4.72 L/min by using 6 g/L of KOH in the aqueous solution. CNG was supplied to the test engine at a pressure of 0.11 MPa using an electronically controlled solenoid valve. Engine tests were carried out at different speeds at 80% open throttle condition and various performance parameters such as brake power (BP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), exhaust gas temperature and exhaust emissions (HC, CO2, CO and NOx) were investigated. In addition, various lubrication oil samples were extracted over 120 h of engine running while topping for drain out volume and samples were analyzed as per ASTM standards. CNG-HHO blend exhibited better performance i.e. 15.4% increase in average BP in comparison to CNG, however, 15.1% decrease was observed when compared to gasoline. CNG-HHO outperformed gasoline and CNG in the case of HC, CO2, CO and brake specific fuel consumption (31.1% decrease in comparison to gasoline). On the other hand, CNG-HHO produced higher average NOx (12.9%) when compared to CNG only. Furthermore, lubrication oil condition (kinematic viscosity, water contents, flash point and total base number (TBN)), wear debris (Iron (Fe), Aluminum (Al), Copper (Cu), Chromium (Cr)) and additives depletion (Zinc (Zn), Calcium (Ca)) presented a significant degradation in the case of CNG-HHO blend as compared to gasoline and CNG. Lubrication oil analyses illustrated 19.6%, 12.8% and 14.2% decrease in average viscosity, flash point and TBN for CNG-HHO blend respectively. However, average water contents, Fe, Al and Cu mass concentration appeared 2.7%, 25×10−6, 19×10−6, and 22×10−6 in lubrication oil for CNG-HHO respectively.

Online Oil Condition Monitoring of Four-Stroke Engine

In recent days, Automobiles that make easy transportation of goods play an important role in the economic growth of any country. Transportation necessitates the continuous running of a vehicle without any unwanted break downs. The engine is considered as the heart of an automobile. Vehicle break on may happen due to unexpected or premature failures of some critical parts of an engine. Critical parts of engines are mainly cylinder, piston, crankshaft, timing belt, and clutch. The failure of the above components mainly depends on the lubricating performance of engine oil. Now a day’s oil is being usually replaced based on periodic maintenance schedule designed by vehicle manufacturers. This schedule is framed based on kilometer coverages of vehicles or time period. In this practice, there is the possibility of being oil replaced before completing its life. Even during service work, technicians in the workshop do not follow any method to know the actual condition of oil before replacing it. There is a need for actual driving habits, traffic conditions, engine speed, and load conditions, road conditions (gradients) to predict the condition up to which oil was being utilized. Thus the above is helpful in the prediction of the remaining life of engine oil. This can be done only by monitoring the properties of engine oil continuously during vehicle running. The properties of any lubricating oil are the viscosity, Viscosity index, density, Specific weight, Specific volume, specific gravity, surface tension, and capillarity. Since viscosity place, a vital role in lubrication oil condition can be judged by monitoring it continuously. The monitoring of viscosity helps to identify the actual condition of the oil. This is done by a system that receives signal for the temperature of oil, converts the same into viscosity units and displays it. Therefore vehicle users can identify the exact replacement of engine oil.

Use of CNG and Hi-octane gasoline in SI engine: a comparative study of performance, emission, and lubrication oil deterioration

ABSTRACT The search for alternative fuels (i.e. other than gasoline) is a continuous area of research. In the present work, a comparative study has been conducted by employing a liquid ((G)97 ) and a gaseous (CNG) fuel in a spark ignition engine. Engine performance, emission. and lubrication oil condition tests have been performed for two fuels by varying operating conditions according to comprehensive sampling plans in each area. Speed was incrementally increased in two phases over the investigated range of rpm for brake power and peak torque. For specific fuel consumption and emission tests, speed was increased through a uniform increment rises over the investigated range of rpm. Lubrication oil samples have been collected over 120 h of engine running with topping for drain out quantity and tests were performed as per ASTM standards. (G)97 showed an improved performance, i.e. 28.8% increase in case of brake power as compared to CNG; however, CNG outperformed (G)97 in case of emission contents and brake-specific fuel consumption (17.2% decrease). Moreover, emission data were fitted to Weibull distribution to ascertain the adequacy for a 95% confidence interval. Furthermore, wear debris (Iron (Fe), aluminum (Al), copper (Cu), chromium (Cr)), lubrication oil condition (kinematic viscosity, flash point and total base number (TBN), and additives depletion (zinc (Zn), calcium (Ca)) showed a visible improvement in case of CNG as compared to (G)97 . Lubrication oil analyses depicted 4.9%, 9.5%, and 3.2% reduction in viscosity, flash point, and TBN for (G) 97 , respectively. However, Fe, Al, and Cu decreased by 12.9%, 29.1%, and 37.7% for CNG, respectively. Mechanisms involved in combustion and lubrication oil deterioration have also been discussed for two fuels.

Multichannel Inductive Sensor Based on Phase Division Multiplexing for Wear Debris Detection.

Inductive wear debris sensor has been widely used in real time machine lubricant oil condition monitoring and fault forecasting. However, the small sensing zone, which is designed for high sensitivity, of the existing sensors leads to low throughput. In order to improve the throughput, a novel multichannel wear debris sensor that is based on phase division multiplexing is presented. By introducing the phase shift circuit into the system, multiple sensing coils could work at different initial phases. Multiple signals of sensing coils could be combined into one output without information loss. Synchronized sampling is used for data recording, and output signals of multiple sensing coils are extracted from the recorded data. A four-channel wear debris sensor system was designed to demonstrate our method. Subsequently, crosstalk analysis, pseudo-dynamic testing and dynamic testing were conducted to check the sensing system. Results show that signals of four sensing coils could be simultaneously detected and the detection limit for ferrous wear debris is 33 μm. Using the presented method, real time wear debris detection in multiple channels could be achieved without increasing the number of excitation source and data acquisition equipment.

Effect of lubricating oil on thermal aging of nitrile rubber

Nitrile rubber (NBR) was thermally aged in air, a commercial lubricating oil, and its corresponding base oil. During thermal aging, changes in the volatile components of NBR, including additives from NBR and the commercial lubricating oil, and the chemical structure of NBR, including functional groups and crosslinking density, were studied by using pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS), attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) and time domain nuclear magnetic resonance (TD-NMR). The migration of additives and the degree of oxidation and crosslinking of NBR in different media were compared. Three effects of lubricating oil—a barrier effect, consumption of oxygen, and permeation—on thermal aging of NBR were examined. Base oil protected the rubber from serious oxidation and crosslinking due to the barrier effect and consumption of oxygen. In contrast, the commercial oil accelerated the aging of NBR by extracting and reacting with the additives from NBR and permeating into the rubber to promote oxidation and crosslinking of the rubber chains. The presence of additives in the commercial oil was the main reason for the different performances. These results were meaningful for using NBR products in lubricating oil conditions.

Synergistic wear-corrosion analysis and modelling of nanocomposite coatings

Abstract This paper presents analysis and modelling of synergistic wear-corrosion performance of Nickel-Graphene (Ni/GPL) nanocomposite coating and compares it with un-coated steel 1020 under reciprocating-sliding contact. A novel synergistic wear-corrosion prediction model incorporating Archard description with nano-mechanics and electrochemistry was developed for Ni/GPL and steel 1020. The model is equally applicable to any kind of nanocomposite coating and bulk material like metals. For various nanocomposite coatings; their respective mechanical parameters should be used as inputs such as poisons ratio (v), Elastic Modulus (E), Hardness (H), Coefficient of Thermal Elastic mismatch (CTE) and intrinsic grain size (Do). The synergistic wear-corrosion effects were significantly-prominent in steel compared to Ni/GPL especially under contaminated lubricating oil conditions. This behaviour of Ni/GPL attributes to compact, refined grain structure leading to minimal grain pull-out during wear cycles which was also assured by less severe micro-ploughing in Ni/GPL compared to severe micro-cutting in steel. The predictions and experimental results were in good-agreement. Modelling of synergistic effects of wear-corrosion applied to nano-composite coatings have never been presented prior to this research. The significance of this work in terms of precision based wear-corrosion synergistic analysis, modelling and predictive techniques is evident from various industrial applications. This work will bring impacts for both in-situ and remote sensor based condition monitoring techniques to automotive, locomotive, aerospace, precision manufacturing and wind turbine industries.

Characteristics Study of In-Situ Capacitive Sensor for Monitoring Lubrication Oil Debris.

As an essential part of engine health monitoring (EHM), online lubrication oil debris monitoring has recently received great attention for the assessment of rotating and reciprocating parts in aero-engines, due to its high integration, low cost and safe characteristics. However, it is be a challenge to find a suitable sensor operating in such a complex environment. We present an unconventional novel approach, in which a cylinder capacitive sensor is designed and integrated with the pipeline of an engine lubrication system, so that the capacitive sensor can effectively detect changes in the lubrication oil condition. In this paper, an attempt to illustrate the performance characteristics of the developed cylinder capacitive sensor is made, through an experiment system that simulates a real scenario of a lubrication oil system. The main aim of the research was to qualitatively describe the relationship between the sensor parameter and the lubrication oil debris. In addition, the effect of the temperature and flow rate of the lubrication oil on capacitance change was performed by several experiments and we figured out a compensation method. The experimental results demonstrated that the cylinder capacitive sensor can potentially be used for lubrication oil debris monitoring of the health condition of an aero-engine.

Latest technologies and novel approaches in coal seam gas centrifugal compressor trains in Australia

Latest guidelines, novel technical notes, new technical knowledge and recent lessons learned are discussed for emerging market of coal seam gas (CSG) centrifugal compressor trains. Optimum configurations and novel arrangements for coal seam gas (CSG) centrifugal compressor trains driven by gas turbines are addressed. Untreated and saturated nature of CSG need special considerations. Operation, performance, degradation, reliability and maintenance issues of centrifugal compressors, gas turbines, gear units and couplings are also explained. The focus is on new technical knowledge, latest experiences and recent lessons learned on these topics. New recommendations regarding centrifugal compressor train components, gas turbines, machinery arrangement, auxiliaries, gear units, lubrication oil systems, bearings, casing and condition monitoring are also presented. A case study is discussed.

オンライン色相判別装置による潤滑油状態監視法

オンライン色相判別装置による潤滑油状態監視法