Oil Condition Monitoring Research Articles

Remaining useful life prediction of lubricating oil with dynamic principal component analysis and proportional hazards model

Lubricating oil contains a lot of tribological information of the machine and plays an important role in machine health. Oil degrades with serving time and causes severe wear afterwards, which is a complex dynamic process, and difficult to be accurately described by a single property. Therefore, the main purpose of deterioration prediction is to estimate the remaining useful life that the oil can still fulfill its functions by analyzing oil condition monitoring data. With a large amount of oil condition monitoring data collected, a vector autoregressive model is applied to the original oil data to describe the dynamic deterioration process. Then dynamic principal component analysis, an effective dimensionality reduction method, is employed to obtain the principal components capturing the most information of the oil data. The proportional hazards model is then built to calculate the failure risk of the lubricating oil based on the condition monitoring information, where its baseline function represents the aging process assuming to follow the Weibull distribution and its positive link function represents the influence of covariates (the principal components) on the failure risk. Finally, the remaining useful life prediction of lubricating oil can be obtained by explicit formulas of the characteristics such as the conditional reliability function and the mean residual life function. This work provides an approach to assess the health of lubricating oil, and a guidance for oil maintenance strategy.

A neural networks approach of process fault diagnosis using time series collected data through oil condition monitoring

In this paper was used the data set collected in a research project between private companies from Romania and Italy, for the development of a basic approach of artificial neural network techniques, as an application in Matlab, aiming to detect the degree of degradation of oil, an automated installation, measuring online the physicochemical properties of the oil. Physical-chemical parameters measured lead to the creation of generous time series, but accessible by numerical and statistical calculation, for the application of artificial intelligence techniques. Applying neural network techniques to parameters that measure oil degradation, oxidation and humidity have generated the results of this work. The main function of monitoring the state of operation of a mechanical system, machine, or plant is to provide the almost correct diagnosis of the machine’s state and rate of change so that preventive measures can be taken at a given time.

Fluid Sensing Using Quartz Tuning Forks-Measurement Technology and Applications.

We provide an overview of recent achievements using quartz tuning forks for sensing liquid viscosity and density. The benefits of using quartz crystal tuning forks (QTFs) over other sensors are discussed on the basis of physical arguments and issues arising in real world applications. The path to highly accurate and robust measurement systems is described and a recently devised system considering these findings is presented. The performance of the system is analyzed for applications such as the mixing ratio measurement of fuels, diesel-soot contamination for engine oil condition monitoring, and particle size characterization in suspensions. It is concluded that using properly designed systems enables a variety of applications in industry and research.

Engine Oils in the Field: A Comprehensive Chemical Assessment of Engine Oil Degradation in a Passenger Car

For the understanding of oil degradation in a combustion engine, in particular zinc dialkyl dithiophosphate (ZDDP) deterioration and its impact on wear expressed as iron content, a field test with a passenger car was carried out, which covered a distance of 19,800 km and represented an entire oil change interval. Condition monitoring of the SAE 5W-30 engine oil used in the turbocharged petrol engine combined the use of conventional and advanced analytical methods. The conventional data collected from the used oils revealed the progress of additives (antioxidants, base reserve, ZDDP), oil degradation products (oxidation, nitration, sulfation, acids), and contaminations (water, soot, wear, fuel dilution). High-resolution mass spectrometry was included to identify ZDDP additive compounds and their fate during the field test as well as their correlation with wear formation. Dialkyl dithiophosphates as the main ZDDP compounds were rapidly degraded and no longer detected after 6000 km. Dialkyl thiophosphate as intermediate ZDDP degradation product was formed and largely depleted within the first 6000 km. Dialkyl phosphates, phosphoric acid, and sulfuric acid as organic and inorganic ZDDP degradation products were generated early and reached high levels at the end of the field test. The presence of intact ZDDP and its degradation products, notably phosphoric and sulfuric acid, correlated with the oil’s iron content. Wear largely remained at low level as long as intact ZDDP was available for tribofilm formation. The lack of ZDDP along with the formation of inorganic acids from ZDDP resulted in an increase in the wear rate by a factor of four.

RETRACTED: IOT-based oil condition monitoring of wind turbine gear boxes with smart sensor technology

The performance parameters such as viscosity, humidity and pressure of the gear box lubricating oil should be monitored and controlled to improve the efficiency of the windmill. The recent techniqu...

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.

Condition Monitoring with Prediction Based on Diesel Engine Oil Analysis: A Case Study for Urban Buses

This paper presents a case study and a model to predict maintenance interventions based on condition monitoring of diesel engine oil in urban buses by accompanying the evolution of its degradation. Many times, under normal functioning conditions, the properties of the lubricants, based on the intervals that manufacturers recommend for its change, are within normal and safety conditions. Then, if the lubricants’ oil condition is adequately accompanied, until reaching the degradation limits, the intervals of oil replacement can be enlarged, meaning that the buses’ availability increases, as well as their corresponding production time. Based on this assumption, a mathematical model to follow and to manage the oil condition is presented, in order to predict the next intervention with the maximum time between them, which means the maximum availability.

A neural networks approach of process fault diagnosis using time series collected data through oil condition monitoring

A neural networks approach of process fault diagnosis using time series collected data through oil condition monitoring

Model of a Light Extinction Sensor for Assessing Wear Particle Distribution in a Lubricated Oil System.

Light extinction based optical wear particle counters (OPCs) have been widely used in the industry for oil condition monitoring for several years, and while experiments have tested the benefits and drawbacks of the measurement principle, limited research has been conducted regarding a theoretical approach to evaluate opportunities and limitations of the measurement scheme. In this paper, we present a method for theoretically modelling the output of an OPC based on the light extinction principle in the regime of geometrical optics, with a special focus on the influence of sensor optical design, particle concentration and measurement noise. Moreover, we show that, if only signal amplitude is considered, an algorithm for evaluating sensor output can cause an erroneous assessment of particle contamination level.

Benefits of lubricant oil analysis for maintenance decision support: a case study

Often, maintenance managers face challenges in making maintenance decisions due to the lack of sufficient and accurate information. It’s not uncommon for the current problem to be resolved, but the cause of the problem remains, which is why some types of failures often recur, reducing the equipment availability and generating additional costs. To overcome these challenges, modern maintenance principles are based on the application of the equipment condition monitoring techniques. One of these techniques is used oil analysis also known as lubricant condition monitoring, which yields an insight into the physical and chemical state of the lubricating oil, as well as the condition of the machine elements that come in contact with oil during routine operation. To illustrate the benefits of employing this technique, a case study of asphalt paving machine is presented. In the case study, four basic lubricant parameters (viscosity, water content, solid particulate content and acid number) for the hydraulic system were analysed. The results of the analysis show a sudden increase in the solid particles content, due to which certain maintenance interventions had to be taken to avoid failure of the system and unnecessary maintenance costs. Also, by oil condition monitoring, after two years, maintenance staff received information which is the base for making a decision on the appropriate replacement interval of hydraulic oil.

Monitoring the Oil of Wind-Turbine Gearboxes: Main Degradation Indicators and Detection Methods

Oil condition monitoring is a common practice in the wind industry. However, the published research about oil degradation in wind turbine gearboxes is limited. This paper aims at providing new information on the oil degradation process by analyzing wind turbine gearbox oils aged in the laboratory and in the field. Oil samples were analyzed in the laboratory and two sensors were used to determine the oil condition by means of dielectric constant and conductivity measurements. Additionally, micropitting tests were carried out for three oils with different base stocks. The results of this study show that viscosity changes of the oils from the field were not significant.Extreme pressure additives depletion and the increase of the iron content are among the most relevant degradation indicators. The oil sensors used in this study provided limited information on the oil degradation process. The accuracy of the sensors was affected by the oil type and its measurement range. The results of the micropitting tests showed that even aged oils exhibited a high micropitting resistance.

Dual-channel mid-infrared sensor based on tunable Fabry-Pérot filters for fluid monitoring applications

In this article, the design and application of an optical sensor for fluid monitoring based on two tunable Fabry-Pérot filters is presented. The sensor enables fluid transmission measurements in the spectral ranges from 1818 cm−1 to 1250 cm−1 and from 1250 cm−1 to 952 cm−1 at wavenumber-dependent resolutions between approximately 20 cm−1 and 33 cm−1. A novel method is proposed to ensure a correct wavenumber representation of the obtained spectra, correcting for an oblique light path in the optical system. The sensor shows high linearity and a low noise level for absorbance measurements. As an example for a fluid monitoring application, the transmission spectra of deteriorated automotive engine oil samples were measured and compared to spectra obtained with a Fourier-transform infrared spectrometer. We used partial least squares (PLS) regression to construct calibration models for the prediction of significant oil condition parameters, like oxidation, sulfation, water content and viscosity, from the obtained spectra. The values predicted from the absorption spectra show high correlation with reference values of the oil condition parameters determined in a laboratory according to appropriate standards. These results indicate that the sensor can be a useful supplementary tool for fast and cost-effective engine oil condition monitoring.

Estimation of Transformers Health Index Based on the Markov Chain

This paper presents a study on the application of the Markov Model (MM) to determine the transformer population states based on Health Index (HI). In total, 3195 oil samples from 373 transformers ranging in age from 1 to 25 years were analyzed. First, the HI of transformers was computed based on yearly individual oil condition monitoring data that consisted of oil quality, dissolved gases, and furanic compounds. Next, the average HI for each age was computed and the transition probabilities were obtained based on a nonlinear optimization technique. Finally, the future deterioration performance curve of the transformers was determined based on the MM chain algorithm. It was found that the MM can be used to predict the future transformers condition states. The chi-squared goodness-of-fit analysis revealed that the predicted HI for the transformer population obtained based on MM agrees with the average computed HI along the years, and the average error is 3.59%.

A high sensitivity wear debris sensor using ferrite cores for online oil condition monitoring

Detecting wear debris and measuring the increasing number of wear debris in lubrication oil can indicate abnormal machine wear well ahead of machine failure, and thus are indispensable for online machine health monitoring. A portable wear debris sensor with ferrite cores for online monitoring is presented. The sensor detects wear debris by measuring the inductance change of two planar coils wound around a pair of ferrite cores that make the magnetic flux denser and more uniform in the sensing channel, thereby improving the sensitivity of the sensor. Static testing results showed this wear debris sensor is capable of detecting 11 µm and 50 µm ferrous debris in 1 mm and 7 mm diameter fluidic pipes, respectively; such a high sensitivity has not been achieved before. Furthermore, a synchronized sampling method was also applied to reduce the data size and realize real-time data processing. Dynamic testing results demonstrated that the sensor is capable of detecting wear debris in real time with a high throughput of 750 ml min−1; the measured debris concentration is in good agreement with the actual concentration.

Identification of Critical Operation Conditions of Industrial Gearboxes by 24/7 Monitoring of Oil Quality, Oil Aging, and Additive Consumption

The demand for wind energy grows at exponential rates. At the same time improving reliability, reduced operation and maintenance costs are the key priorities in wind tur-bine maintenance strategies [1]. This paper provides information about a novel online oil condition monitoring system to give a solution to the mentioned priorities. The presented sensor system enables damage prevention of the wind turbine gear-box by an advanced warning time of critical operation conditions and an enhanced oil exchange interval realized by a precise measurement of the electrical conductivity, the relative permittivity and the oil temperature. A new parameter, the WearSens® Index (WSi) is introduced. The mathematical model of the WSi combines all measured values and its gradients in one single parameter for a comprehensive monitoring to prevent wind turbines from damage. Furthermore, the WSi enables a long-term prognosis on the next oil change by 24/7 server data logging. Corrective procedures and/or maintenance can be carried out before actual damage occurs. First WSi results of an onshore wind turbine installation compared to traditional vibration monitoring are shown.

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.

Non-Dispersive Infrared Sensor for Online Condition Monitoring of Gearbox Oil.

The condition of lubricating oil used in automotive and industrial gearboxes must be controlled in order to guarantee optimum performance and prevent damage to machinery parts. In normal practice, this is done by regular oil change intervals and routine laboratory analysis, both of which involve considerable operating costs. In this paper, we present a compact and robust optical sensor that can be installed in the lubrication circuit to provide quasi-continuous information about the condition of the oil. The measuring principle is based on non-dispersive infrared spectroscopy. The implemented sensor setup consists of an optical measurement cell, two thin-film infrared emitters, and two four-channel pyroelectric detectors equipped with optical bandpass filters. We present a method based on multivariate partial least squares regression to select appropriate optical bandpass filters for monitoring the oxidation, water content, and acid number of the oil. We perform a ray tracing analysis to analyze and correct the influence of the light path in the optical setup on the optical parameters of the bandpass filters. The measurement values acquired with the sensor for three different gearbox oil types show high correlation with laboratory reference data for the oxidation, water content, and acid number. The presented sensor can thus be a useful supplementary tool for the online condition monitoring of lubricants when integrated into a gearbox oil circuit.

IOT Based Oil Condition Monitoring of Wind Turbine Gearboxes with Smart Sensor Technology

<p class="MsoNormal" style="text-align: justify;"><span style="font-size: 9.0pt; font-family: 'Arial','sans-serif'; mso-bidi-font-style: italic;">The performance parameters such as viscosity, humidity and pressure of the gear box lubricating oil should be monitored and controlled to improve the efficiency of the windmill. The recent technique like IOT is used for the continuous transmission of bulk data of these parameters from one machine to other machine which is situated in remote locations .Also the development of mobile technology and web made the live monitoring easier. Thus transmission and sharing of data takes place without any time delay. The technology also helps to completely eliminate the assistance nearby the site.<em> </em><em><span style="font-style: normal;">The large wind farms in coastal areas are easily and continuously monitored and the data are recorded from remote locations with the mobile technology having internet connections. This paper also reports the </span></em></span><em><span style="font-size: 9.0pt; font-family: 'Arial','sans-serif'; font-style: normal;">design of MEMS viscosity sensor</span></em><em><span style="font-size: 9.0pt; font-family: 'Arial','sans-serif'; font-style: normal; mso-bidi-font-style: italic;">,</span></em><em></em><em><span style="font-size: 9.0pt; font-family: 'Arial','sans-serif'; font-style: normal; mso-bidi-font-style: italic;">MEMS humidity sensor and MEMS pressure sensor for continuous monitoring of the lubricating system .The main advantage of implementing MEMS devices is to reduce the size and cost and also the sensitivity is improved. Along with IOT, smart sensor technology is also introduced in the monitoring which avoids the serious problems that occur in wind mills.</span></em><em></em></p><p class="MsoNormal" style="text-align: justify;"><span style="font-size: 9.0pt; font-family: 'Arial','sans-serif'; mso-bidi-font-style: italic;">The performance parameters such as viscosity, humidity and pressure of the gear box lubricating oil should be monitored and controlled to improve the efficiency of the windmill. The recent technique like IOT is used for the continuous transmission of bulk data of these parameters from one machine to other machine which is situated in remote locations .Also the development of mobile technology and web made the live monitoring easier. Thus transmission and sharing of data takes place without any time delay. The technology also helps to completely eliminate the assistance nearby the site.<em></em><em><span style="font-family: 'Arial','sans-serif'; font-style: normal; mso-bidi-font-style: italic;">The large wind farms in coastal areas are easily and continuously monitored and the data are recorded from remote locations with the mobile technology having internet connections. This paper also reports the </span></em></span><em><span style="font-size: 9.0pt; font-family: 'Arial','sans-serif'; font-style: normal;">design of MEMS viscosity sensor</span></em><em><span style="font-size: 9.0pt; font-family: 'Arial','sans-serif'; font-style: normal; mso-bidi-font-style: italic;">,</span></em><em></em><em><span style="font-size: 9.0pt; font-family: 'Arial','sans-serif'; font-style: normal; mso-bidi-font-style: italic;">MEMS humidity sensor and MEMS pressure sensor for continuous monitoring of the lubricating system .The main advantage of implementing MEMS devices is to reduce the size and cost and also the sensitivity is improved. Along with IOT, smart sensor technology is also introduced in the monitoring which avoids the serious problems that occur in wind mills.</span></em><em style="mso-bidi-font-style: normal;"></em></p>

Preventive Measurement and Online Insulation Evaluation of Power Transformer

Cellulose based non conducting materials and mineral oils have widely been used in liquid filled transformers. Environmental and fire-safety concerns have accelerated the use of Natural Esters (NE) as a sustainable alternative to mineral oil. To ensure a safe and long-term operation of the vegetable oil filled transformers, it is need to clarify the knowledge gaps with respect to the ageing of vegetable oil impregnated cellulose and insulation diagnostic methods. The condition monitoring of mineral oil filled transformers has been well characterised, due to the numerous laboratory and field studies conducted during the past several decades. The application of these available condition monitoring techniques for NE filled transformers is yet to be validated as the use of NE for power transformers is in the tested stage. In this paper the performance of NE as insulation in transformers by evaluating the ageing of NE impregnated cellulose pressboard is presented. For this purpose, an accelerated ageing experiment has been carried out in sealed tubes at 120 ⁰C. The ageing of oil impregnated pressboard has been characterised by decrease in the degree of polymerisation (DP) of pressboard, and the formation of both 2-furfuraldehyde and dissolved carbon dioxide (CO2) in the oil. The structural changes of cellulose have been supported by fuzzy logic algorithm. Moreover, the results have been compared with cellulose insulation samples aged under similar conditions in conventional mineral oil.

Monitoring Physical Fluid Properties Using a Piezoelectric Tuning Fork Resonant Sensor

For fluid analysis applications, such as oil condition monitoring, fuel quality, or gas concentration measurements, resonant sensors deliver an outstanding performance when signal processing is optimized and the fluid-mechanical model of the electromechanical resonator is suitable and accurate for the particular resonator. By combining recent advancements, significant improvements in accuracy, measurement speed, dynamic range, and suppression of cross-sensitivities could be achieved. These features enable the development of new solutions for a variety of measurement issues in industry and bio technology. In this contribution the performance of a highly universal evaluation system is demonstrated using a commercially available quartz crystal tuning fork resonator as sensing element for liquid viscosity and mass density. The obtained results are quantified with respect to an accurate lab bench viscosity and mass density meter. A significant advantage of this system is that it operates reliably and accurately even for strongly damped resonators. Therefore, the sensor elements can be used in a larger viscosity range than with alternative evaluation methods.