Oil Condition Monitoring Research Articles

Wind turbine gearbox oil temperature feature extraction and condition monitoring based on energy flow

Supervisory Control and Data Acquisition (SCADA) data is widely used for wind turbine gearbox condition monitoring (WTGCM) due to its easy access and low cost, thus ensuring the safe and reliable operation of wind turbine gearboxes. However, existing WTGCM methods based on SCADA overlook the fundamental thermal physics and sensor failure. To effectively address the challenges of weak feature generalization and numerous false alarms, we propose a highly robust WTGCM method based on energy flow. Firstly, based on the principles of thermal balance and wind energy conversion, a feature extraction method considering the physical change process of oil temperature is proposed. Secondly, the performance of different feature extraction methods is quantitatively analyzed using data evaluation criteria. The reasons why different feature extraction methods have different effects are revealed through dimensionality reduction analysis. Finally, we compare the proposed method with other feature extraction methods using four models to validate the excellent performance of the proposed method in the early fault warning. The results demonstrate that the proposed method reduces the average root mean square error by 18.21% during the healthy operation of the wind turbine gearboxes. Moreover, the proposed method performs excellently in early warning of mechanical and sensor faults, significantly reducing the number of false alarms. The average warning time for mechanical faults is 32.56 h earlier.

The research on triple-coil inductive debris sensor with electromagnetic shielding

A high sensitivity inductive debris sensor is of great significance for oil condition monitoring and the fault diagnosis of mechanical equipment. However, inductive sensors are susceptible to interference from external electromagnetic fields. In this study, an inductive debris sensor with a shielding layer is studied, which can effectively reduce the influence of external magnetic fields on the sensor. Theoretical and experimental results verify the effectiveness of the shielding layer, and also show that the shielding layer impacts the magnetic field of the sensor, resulting in a decrease in the amplitude of the output signal. Detailed analysis was conducted to investigate the effect of structural parameters on the performance of inductive sensors. These parameters include the distance between the shielding layer and the coils, the thickness of the shielding layer, and the shielding layer material. Based on the theoretical and experimental results, reference suggestions are provided for the design of the shielding layer. This study ensures the performance and reliability of inductive sensors in practical applications.

Spatial-temporal modeling of oil condition monitoring: A review

Lubricating oil plays a vital role as the information carrier for equipment tribological performance. Therefore, oil condition monitoring (OCM) serves as a crucial technology for assessing and predicting state degradation, providing the first-line defense against functional failure. However, limited by random fluctuation and insufficient knowledge, the modeling of OCM has suffered from uncertainty problems, leading to poor applicability and insufficient generalizability. The existing reviews are less elaborated on the information uncertainty for description, characterization, and treatment, which expresses the essence of modeling constraints. To bridge this gap, the paper provides a comprehensive review of the oil state modeling in terms of uncertainty. The existing methods and metrics are reviewed from the perspective of the spatial oil state assessment (OCA) and temporal remaining useful life (RUL) prediction modeling. Further, the existing methods are analyzed to solve the uncertainty problem, and then the solutions in oil state modeling considering uncertainty are discussed. Finally, targeting the challenges of the monitoring technology, the future trends of OCM are presented.

Condition Monitoring of Power Plant Engine Oil Through FTIR Analysis

Condition Monitoring of Power Plant Engine Oil Through FTIR Analysis

Design of an expert system based on fuzzy logic for real-time oil condition monitoring and fault diagnosis in heavy-duty diesel engines

Design of an expert system based on fuzzy logic for real-time oil condition monitoring and fault diagnosis in heavy-duty diesel engines

Conception of a predictive maintenance system for forest harvesters from multiple data sources

For cost-effective use of harvesters, expensive repairs and unplanned downtimes must be reduced as far as possible. The predictive detection of failing systems and the calculation of intelligent service intervals, necessary to avoid these factors, require in-depth knowledge of the machines' behavior. Such know-how needs permanent monitoring of the machine state from different technical perspectives. In this paper three approaches will be presented as they are currently pursued in the publicly funded project PreForst at Ostfalia University of Applied Sciences. These include the intelligent linking of workshop and service data, sensors on the harvester and a special online hydraulic oil condition monitoring system. Furthermore the paper shows potentials as well as challenges for the use of these data in the conception of a predictive maintenance system.

Comparative Analysis of Soft Computing Models for Predicting Viscosity in Diesel Engine Lubricants: An Alternative Approach to Condition Monitoring.

The viability of employing soft computing models for predicting the viscosity of engine lubricants is assessed in this paper. The dataset comprises 555 reports on engine oil analysis, involving two oil types (15W40 and 20W50). The methodology involves the development and evaluation of six distinct models (SVM, ANFIS, GPR, MLR, MLP, and RBF) to predict viscosity based on oil analysis results, incorporating metallic and nonmetallic elements and engine working hours. The primary findings indicate that the radial basis function (RBF) model excels in accuracy, consistency, and generalizability compared with other models. Specifically, a root mean square error (RMSE) of 0.20 and an efficiency (EF) of 0.99 were achieved during training and a RMSE of 0.11 and an EF of 1 during testing, utilizing a 35-network topology and an 80/20 data split. The model demonstrated no significant differences between actual and predicted datasets for average and distribution indices (with P-values of 1.00). Additionally, robust generalizability was exhibited across various training sizes (ranging from 50 to 80%), attaining a RMSE between 0.09 and 0.20, a mean absolute percentage error between 0.23 and 0.43, and an EF of 0.99. This study provides valuable insights for optimizing and implementing machine learning models in predicting the viscosity of engine lubricants. Limitations include the dataset size, potentially affecting the generalizability of findings, and the omission of other factors impacting engine performance. Nevertheless, this study establishes groundwork for future research on the application of soft computing tools in engine oil analysis and condition monitoring.

A Highly Sensitive Wear Debris Sensor Based on Differential Detection

Wear debris in the oil contains a wealth of information about the friction pairs of the mechanical equipment. By analyzing the size and type of wear debris through oil detection technology, condition monitoring and fault diagnosis of mechanical systems can be realized. This paper presents an inductive sensor based on differential detection and its signal conditioning circuit, which can detect metal wear debris in the oil. The sensor adopts the structure of two induction coils embedded in one excitation coil. The differential signal is obtained by reverse connecting two induction coils with the same parameters, which can suppress the common mode interference and eliminate the influence of ambient noise so that the sensor has extremely low noise. Through the designed signal conditioning circuit, the detection signal is phase-sensitive detected, and the information of wear debris is extracted by amplification and filtering. In this paper, the sensing principle of the sensor is derived, the spacing between the two induction coils is optimized using finite element simulation, and the optimal excitation frequency, detection limit, and detection error of the sensor are investigated through experiments. The experiment results show that the sensor can detect 20 μm iron particles and 130 μm copper particles in a 2 mm flow channel, and the detection error of the sensor is less than 22%. The sensor has the advantages of simple structure and high sensitivity and can be applied to detect metal wear debris in hydraulic oil.

Real-time lubricant monitoring sensors

This article is an overview of new trends in oil condition monitoring and the impact of Industry 4.0 on the monitoring of lubricants in real time. New solutions are being introduced into this industry with new advantages in the development of artificial intelligence, as well as machine learning and sensor technologies that are applicable for data-based services. This is called predictive maintenance, which changes the traditional routine maintenance and provides a deep understanding of the operation of equipment. Constant monitoring of the condition of lubricants during operation is necessary to maintain the quality of the equipment in real time since non-operational monitoring can reduce operating costs by eliminating equipment downtime during daily diagnostics. Keywords: lubricating oils, hydraulic fluids, maintenance, operating conditions, sensors

Triboelectric Mechanism of Oil-Solid Interface Adopted for Self-Powered Insulating Oil Condition Monitoring.

The liquid-solid contact electrification mechanism has been explored in the aqueous solution system, but there are few systematic studies on oil-solid triboelectrification. Herein, an oil droplet triboelectric nanogenerator (Oil-droplet TENG) is designed as the probe to investigate the charge transfer properties at oil-solid interface. The charge transfer kinetics process is disclosed by the electrical signals produced, showing that the electron species initially predominated the oil-solid triboelectrification. The molecular structure and electronic properties of oil also affect triboelectric performance. Further, the charge transfer principle in multi-component liquid mixture during the electric double layer (EDL) development process is proposed to explain the component competition effect. As a proof of concept, a tubular-TENG is designed as a self-powered sensor for transformer oil trace water detection. The device demonstrates high water sensitivity with a detection limit of 10µL L-1 and a response range of 10-100µL L-1 . This work not only reveals the oil-solid triboelectric and charge transfer competition mechanism in EDL, but also open up a new channel for real-time online monitoring of trace water in transformer oil, which holds promise for information perception and intelligent operation of transformers in the power industry.

Design of an expert system based on fuzzy logic for real-time oil condition monitoring and fault diagnosis in heavy-duty diesel engines

Design of an expert system based on fuzzy logic for real-time oil condition monitoring and fault diagnosis in heavy-duty diesel engines

Magnetic Plug Sensor with Bridge Nonlinear Correction Circuit for Oil Condition Monitoring of Marine Machinery

Diesel engines in marine power systems often work in extreme environments. Oil monitoring technology can guarantee the operational safety of diesel engines. In this paper, a magnetic plug sensor for oil debris monitoring is proposed to improve sensitivity and accuracy. Through finite element analysis, absolute deviation is reduced by optimizing the sensor structure. A bridge nonlinear correction circuit is designed to make sensitivity consistent over the entire scale range, which can facilitate calibration and data processing. In order to reduce noise and amplify the signal effectively, a signal post-processing circuit is adopted as well, which consists of a first stage filter circuit, a second stage filter, an active filter module, and an instrumentation amplifier. Therefore, this magnetic plug sensor exhibits better sensitivity and accuracy. Furthermore, a void test and a dynamic test are carried out to investigate its performance. There is a linear relationship between the voltage and the particle mass for the sensor with a bridge nonlinear correction circuit. The results illustrate a minimum of 0.033 mg iron debris with a 1.647 signal-to-noise ratio. Additionally, it can capture and detect 47 μm particles with a debris capture rate of over 90%, which allows it to excel in early fault diagnosis as well.

Engine oil quality deterioration estimation using an integrated sensory system

Engine oil degradation impacts the operation and performance of an internal combustion engine. As a result, it is crucial to compare the degradation level of engine oils, with a particular emphasis on kinematic viscosity, oxidation, nitration, and critical oil testing characteristics. Suppose engine oil changes too soon without first calculating its remaining usable life. In that case, it wastes already scarce resources and has an unfavourable environmental influence. Engine performance may suffer if oil is changed too late and is of poor quality. In the current research, oil testing is performed on vehicles due to maintenance to find the best moment for changing engine oil. Oil samples were taken randomly from cars that came to an authorized service facility for maintenance, comprising a wide range from the first to the fifth servicing. Oil samples were initially evaluated in a laboratory using a viscometer and Fourier transform-infrared spectroscopy in conformity with industry standards. The samples were then evaluated using an integrated sensor system developed and built by the authors. The determination coefficient R2 = 0.97 showed a significant positive association between major engine oil degradation characteristics such as kinematic viscosity, oxidation, and nitration at reasonable sensitivity. Approximately 8% of the total gathered samples were useable. The research identifies several oil degradation sensor systems that give low-cost, on-site convenient options for oil condition monitoring and predicting the remaining usable life of engine oil.

How to get Lab Equivalent Oil Analysis 24/7

The established process for oil condition monitoring is to periodically take a sample and have it analysed in an oil laboratory. These laboratory measurements are governed by various technical standards (ASTM,DIN etc.) and customers rely on this periodic data to react to oil condition trends and/or step functions, to plan servicing and maintenance and to reduce asset downtime from failure. Real-time oil condition monitoring systems based on dielectric or impedance analysis of the oil have been available for some time but they only provide summary parameters that are hard to interpretate as they do not correlate with the laboratory oil analysis. In this paper we discuss the development of Spectrolytic’s Oil Condition Monitoring systems (FluidInspectIR®) and how, by working closely with our customers, we have developed a robust and affordable range of oil condition monitoring systems that gives our clients meaningful and understandable real time data of the same parameters and in the same units as they are commonly receive it from their oil laboratory analysis. These systems provide the customer the comfort of having quantitative and accurate key oil condition data at the touch of a button, while still utilising their standard practices through oil laboratory measurement to validate the predicted key oil parameters by the inline system.

Application of Condition Monitoring for Hydraulic Oil Using Tuning Fork Sensor: A Study Case on Hydraulic System of Earth Moving Machinery.

In this study, we focus on the correctness of oil condition monitoring, specifically of a tuning forks sensor in hydraulic systems. We also aim to analyze the correlation between the online monitoring sensor signal and offline oil analysis by periodically sampling the hydraulic oil. In recent years, condition-based monitoring (CBM) of hydraulic oils has played a key role in extending earthmoving machinery uptime and reducing maintenance costs. We performed rig test and field test to develop a condition monitoring system based on oil analysis for construction equipment. Using the rig test, a reference line for the diagnosis of viscosity and dielectric constant for the new hydraulic oil was derived, and the characteristics of each sensor parameter for artificial contamination and oxidation were confirmed. In order to affirm the validity of oil diagnosis using oil sensors, the oil sensors were applied to four excavators to detect changes in oil conditions over 12 months. It was found that monitoring hydraulic oil with an oil sensor detecting the change in oil properties and contamination can provide reliable information for establishing diagnostic criteria. The finding allows us to predict the remaining oil life and to determine the oil change intervals based on the diagnosis of the oil condition.

Design of an Integrated Micro-Viscometer for Monitoring Engine Oil.

This paper proposes a novel integrated micro-viscometer for engine-oil monitoring. The final solution consists of a capacitive micromachined ultrasonic transducer (CMUT) and an application-specific integrated circuit (ASIC). The CMUT is used to generate and capture acoustic waves while immersed in engine oil. The low power transceiver ASIC is interfaced with the CMUT structure for actuation and reception. An integrated charge pump boosts the supply voltage from 3.3 to 22 V to generate the DC polarization voltage of the CMUT. The receiver has a power consumption of 72 µW with an input-referred noise current of 3.2 and a bandwidth of 7 MHz. The CMUT array occupies an area of 3.5 × 1 mm, whereas the ASIC has a chip area of 1 × 1 mm. The system was tested using engine oils of different types and ages at different temperatures. Measurement results show a significant frequency shift due to the dynamic viscosity change that occurs as oil ages. A shift of −1.9 kHz/cP was measured, which corresponds to a shift of 33 Hz/mile. This work paves the way for high accuracy-integrated solutions for oil condition monitoring and is expected to play a significant role in a more economic and environmentally friendly usage of oil.

An Ultrasensitive Microsensor Based on Impedance Analysis for Oil Condition Monitoring

Oil condition monitoring is an important way to achieve mechanical equipment health assessment, condition early warning, fault diagnosis, and intelligent maintenance. A microsensor integrated with inductance-resistance and capacitance sensing units is presented, which can comprehensively detect metal debris, air bubbles, and moisture in oil based on impedance analysis. The inductance-resistance sensing unit not only detects traditional inductance parameter, but also proposes a resistance detection method based on coil to further improve the detection sensitivity for nonferromagnetic metal debris. The capacitance sensing unit can detect air bubbles, and a new moisture measurement method is also proposed. Based on the signal characteristics of inductance, resistance and capacitance detection results, the microsensor can effectively distinguish 16–500 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m iron debris, 60–500 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m copper debris, >170 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m air bubbles and moisture in oil. The ultrasensitive microsensor can obtain more oil status information, which has important value and wide engineering application prospect for modern fault diagnosis and prediction theory based on information fusion.

Comprehensive detection method for multi-contaminants in hydraulic oil based on inductance-resistance-capacitance analysis

Oil condition monitoring technology is a practical method for mechanical fault diagnosis, because oil contamination can directly reflect the lubrication and wear state. A comprehensive detection method based on integrated sensor consisting of an inductance-resistance sensing unit and a capacitance sensing unit is introduced in this article. Inductance parameter differentiates metallic debris, and the coil-based resistance parameter can detect smaller non-ferromagnetic metallic debris. Capacitance parameter is used to measure air bubbles and moisture, and the moisture measurement is not interfered by solid impurities. The simulation analysis showed that annular channel reduces the instability of inductive-resistive detection caused by position effect. The experimental results proved that the integrated inductive-resistive-capacitive sensor detects air bubbles, moisture, ferromagnetic debris and non-ferromagnetic debris by comparing multi-parameter signals. This method expands the detection range for oil contamination, and reduces the probability of incorrect particle counting and incorrect size measurement, which provides technical support for the intelligent operation and maintenance of mechanical equipment.

An Oil Multipollutant Detection Sensor With High Sensitivity and High Throughput

Oil is rich in information about the operating status of machinery and equipment, and oil condition monitoring is essential for maintaining the safety and reliability of machinery and equipment. Most sensors not only have the problem of small detection throughput and low detection efficiency, but also large-sized contaminants can easily block or even scratch the detection channel. A sensor with high sensitivity and high throughput for detecting multiple oil contamination is designed in this paper. The sensor comprises four glass capillary detection channels and four detection units, including a planar coil, solenoid coil, cylindrical parallel plate capacitor and rectangular parallel plate capacitor. Detection flux of the sensor is increased by 12 times its initial value, which significantly improves the detection efficiency. Through experimental research, in the inductance mode, the best excitation frequency is 2 MHz, and the voltage is 2 V. In the capacitance mode, the best excitation frequency is 1.8 MHz, and the voltage is 2 V. In addition, the sensor has a higher detection capability for Fe than Cu in the inductance mode, and a higher detection capability for water droplets than air bubbles in the capacitance mode. By analyzing the signal characteristics of each sensor unit, the sensor can distinguish between Fe of 38&#x007E;900 &#x03BC;m and Cu of 88&#x007E;900 &#x03BC;m, as well as water droplets greater than 130 &#x03BC;m and air bubbles greater than 100 &#x03BC;m. This research provides a new idea for online oil monitoring.

The role of sustainable oil maintenance on lubrication system reliability

This article discusses the value of sustainable oil maintenance by using submicron high-tech offline filtration based on oil condition monitoring to reduce wear particles and improve system reliability. These activities incorporate broader goals of establishing a proactive maintenance approach, defined as continuous monitoring and controlling of the root causes of machine failure. Among these causes, contamination is the industry's most severe, popular, and most recognized failure cause. In order to ensure all necessary proactive maintenance activities, this paper uses practical analysis for oil contamination monitoring based on ISO 4405. The author applied a 0.1-micron high-tech offline filtration for lubrication systems of ball mills in the Sungun copper mine as an oil maintenance activity to control the system lubrication contamination as a root cause of failures.