Journal articles: 'Oil debris sensor'

1

Macián, Vicente, Bernardo Tormos, Guillermo Miró, and Isaac Rodes. "Experimental assessment and validation of an oil ferrous wear debris sensors family for wind turbine gearboxes." Sensor Review 38, no. 1 (January 15, 2018): 84–91. http://dx.doi.org/10.1108/sr-04-2017-0065.

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Purpose The purpose of this study was to perform a complete experimental assessment of a family of oil ferrous wear debris sensor is performed. The family comprised the original sensor and its re-engineered evolution, which is capable of detecting both amount and size of wear debris particles trapped by the sensor and some predefined oil condition properties. Design/methodology/approach In this work, the first step was to perform a design of experiments for the sensor validation. A specially defined test rig was implemented, and different ferrous wear debris was collected. For each sensor, two different tests were performed. The first test was called a “void test”, where quantified amounts of debris were collided with the sensor without oil. The second one was a dynamic test, where the sensor was installed in the test rig and different amounts of wear debris were added at a constant rate. In addition, specific tests related with oil properties detection were studied. Findings The results show excellent correlation of the sensor output signal with the amount of wear debris and a satisfactory detection of debris size in all ranges. Also, the dynamic test presented adequate representativeness, and sensors performed well in this scenario. Practical implications This paper shows the practical implementation of this type of sensor and the usual detection range and rate of detection for different debris size and quantities. Originality/value This work has a great utility for maintenance managers and equipment designers to fully understand the potential of this type of sensor and its suitability for the application required.

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Wang, Yishou, Zhibin Han, Tian Gao, and Xinlin Qing. "In-situ capacitive sensor for monitoring debris of lubricant oil." Industrial Lubrication and Tribology 70, no. 7 (September 10, 2018): 1310–19. http://dx.doi.org/10.1108/ilt-09-2017-0256.

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Purpose The purpose of this study is to develop a cylindrical capacitive sensor that has the advantages of high resolution, small size and designability and can be easily installed on lubricant pipeline to monitor lubricant oil debris. Design/methodology/approach A theoretical model of the cylindrical capacitive sensor is presented to analyze several parameters’ effectiveness on the performance of sensor. Numerical simulations are then conducted to determine the optimal parameters for preliminary experiments. Experiments are finally carried out to demonstrate the detectability of developed capacitive sensors. Findings It is clear from experimental results that the developed capacitive sensor can monitor the debris in lubricant oil well, and the capacitance values increase almost linearly when the number and size of debris increase. Research limitations/implications There is lot of further work to do to apply the presented method into the application. Especially, it is necessary to consider several factors’ influence on monitoring results. These factors include the flow rate of the lubricant oil, the temperature, the debris distribution and the vibration. Moreover, future work should consider the influence of the oil degradation to the capacitance change and other contaminations (e.g. water and dust). Practical implications This work conducts a feasibility study on application of capacitive sensing principle for detecting debris in aero engine lubricant oil. Originality/value The novelty of the presented capacitance sensor can be summarized into two aspects. One is that the sensor structure is simple and characterized by two coaxial cylinders as electrodes, while conventional capacitive sensors are composed of two parallel plates as electrodes. The other is that sensing mechanism and physical model of the presented sensor is verified and validated by the simulation and experiment.

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Zhang, Hongpeng, Haotian Shi, Wei Li, Laihao Ma, Xupeng Zhao, Zhiwei Xu, Chenyong Wang, Yucai Xie, and Yuwei Zhang. "A Novel Impedance Micro-Sensor for Metal Debris Monitoring of Hydraulic Oil." Micromachines 12, no. 2 (February 3, 2021): 150. http://dx.doi.org/10.3390/mi12020150.

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Hydraulic oil is the key medium for the normal operation of hydraulic machinery, which carries various wear debris. The information reflected by the wear debris can be used to predict the early failure of equipment and achieve predictive maintenance. In order to realize the real-time condition monitoring of hydraulic oil, an impedance debris sensor that can detect inductance and resistance parameters is designed and studied in this paper. The material and size of wear debris can be discriminated based on inductance-resistance detection method. Silicon steel strips and two rectangular channels are designed in the sensor. The silicon steel strips are used to enhance the magnetic field strength, and the double rectangular detection channels can make full use of the magnetic field distribution region, thereby improving the detection sensitivity and throughput of the sensor. The comparison experiment shows that the coils in series are more suitable for the monitoring of wear debris. By comparing and analyzing the direction and the presence or absence of the signal pulses, the debris sensor can detect and distinguish 46 µm iron particles and 110 µm copper particles. This impedance detection method provides a new technical support for the high-precision distinguishing measurement of metal debris. The sensor can not only be used for oil detection in the laboratory, but also can be made into portable oil detection device for machinery health monitoring.

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Mao, Huijie, Hongfu Zuo, and Han Wang. "Electrostatic Sensor Application for On-Line Monitoring of Wind Turbine Gearboxes." Sensors 18, no. 10 (October 22, 2018): 3574. http://dx.doi.org/10.3390/s18103574.

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The oil-line electrostatic sensor (OLES) is a new online monitoring technology for wear debris based on the principle of electrostatic induction that has achieved good measurement results under laboratory conditions. However, for practical applications, the utility of the sensor is still unclear. The aim of this work was to investigate in detail the application potential of the electrostatic sensor for wind turbine gearboxes. Firstly, a wear debris recognition method based on the electrostatic sensor with two-probes is proposed. Further, with the wind turbine gearbox bench test, the performance of the electrostatic sensor and the effectiveness of the debris recognition method are comprehensively evaluated. The test demonstrates that the electrostatic sensor is capable of monitoring the debris and indicating the abnormality of the gearbox effectively using the proposed method. Moreover, the test also reveals that the background signal of the electrostatic sensor is related to the oil temperature and oil flow rate, but has no relationship to the working conditions of the gearbox. This research brings the electrostatic sensor closer to practical applications.

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Kumar, Paras, Harish Hirani, and Atul Kumar Agrawal. "Online condition monitoring of misaligned meshing gears using wear debris and oil quality sensors." Industrial Lubrication and Tribology 70, no. 4 (May 8, 2018): 645–55. http://dx.doi.org/10.1108/ilt-05-2016-0106.

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Purpose This paper aims to investigate the effect of misalignment on wear of spur gears and on oil degradation using online sensors. Design/methodology/approach The misalignment effect on gears is created through a self-alignment bearing, and is measured using laser alignment system. Several online sensors such as Fe-concentration sensor, moisture sensor, oil condition sensor, oil temperature sensor and metallic particle sensor are installed in the gear test rig to monitor lubricant quality and wear debris in real time to assess gearbox failure. Findings Offset and angular misalignments are detected in both vertical and horizontal planes. The failure of misaligned gear is observed at both the ends and on both the surfaces of the gear teeth. Larger-size ferrous and non-ferrous particles are traced by metallic particle sensor due to gear and seal wear caused by misalignment. Scanning electron microscope (SEM) images examine chuck, spherical and flat platelet particles, and confirm the presence of fatigue (pitting) and adhesion (scuffing) wear mechanism. Energy-dispersive X-ray spectroscopy analysis of SEM particles traces carbon (C) and iron (Fe) elements due to gear failure. Originality/value Gear misalignment is one of the major causes of gearbox failure and the lubricant analysis is as important as wear debris analysis. A reliable online gearbox condition monitoring system is developed by integrating wear and oil analyses for misaligned spur gear pair in contact.

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Zhang, Fang Zhou, Ben Dong Liu, Yu De Wu, and De Sheng Li. "The Simulation Research of Detecting Metal Debris with Different Shape Parameters of Micro Inductance Sensor." Advanced Materials Research 791-793 (September 2013): 861–65. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.861.

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A micro inductance sensor model based on the software of Maxwell to detect the debris in oil is presented. The model is built to study the detecting performance of sensors with different turns and enwinding styles. It can be demonstrated that the planar coil with 15 turns has a better sensitivity to detecting metal debris with about 100 micro meters in size. The solenoid coil with 20 turns has a better performance to detect the micro metal debris. This simulation shows that the detecting performance of a sensor is related to its parameters such as the size, turns, style of enwinding.The optimization of coils for the detection of metal debris with 100 in size is presented at last.

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Liu, Liankun, Liang Chen, Saijie Wang, Yi Yin, Dazhuang Liu, Sen Wu, Zhijian Liu, and Xinxiang Pan. "Improving Sensitivity of a Micro Inductive Sensor for Wear Debris Detection with Magnetic Powder Surrounded." Micromachines 10, no. 7 (July 1, 2019): 440. http://dx.doi.org/10.3390/mi10070440.

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The inductive detection of wear debris in lubrication oil is an effective method to monitor the machine status. As the wear debris is usually micro scale, a micro inductive sensor is always used to detect them in research papers or high-tech products. However, the improvement of detection sensitivity for micro inductive sensors is still a great challenge, especially for early wear debris of 20 μm or smaller diameter. This paper proposes a novel method to improve the detection sensitivity of a micro inductive sensor. Regarding the magnetic powder surrounding the sensor, the magnetic field in the core of the sensor where the wear debris pass through would be enhanced due to the increased relative permeability. Thus, the inductive signal would be improved and the detection sensitivity would be increased. It is found that the inductive signal would linearly increase with increasing the concentration of the magnetic powder and this enhancement would also be effective for wear debris of different sizes. In addition, the detection limit of the micro inductive sensor used in our experiment could be extended to 11 μm wear debris by the proposed method.

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8

Tian, Hong Xiang, Chun Hui Zhang, and Yun Ling Sun. "Development of Sensor to Monitor Ferromagnetic Debris Based on Electromagnetic Induction Principle." Applied Mechanics and Materials 336-338 (July 2013): 388–91. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.388.

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Wear is one of the most common failure modes in mechanical machines and the lubricating oil carries a lot of wear information. For monitoring the ferromagnetic debris in oils, a sensor is developed based-on electromagnetic induction principle. The magnetic transient model of sensor coils and particle was established by using Maxwell. The experiment results can reveal the total ferrous debris in an oil sample.

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9

He, Yong Bo, Wen Wen Feng, and Kun Jiang. "Finite Element Analysis on Multi Parameter Characteristics of Inductive Lubricating Oil Wear Debris Sensor." Applied Mechanics and Materials 738-739 (March 2015): 97–102. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.97.

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Large wear debris is an important indication of abnormal wear of aviation engines. Analysis of the relationship between particle parameters and the output signal plays an important role to improve the sensor sensitivity. A three-coil simulating sensor model is constructed using APDL finite element program. After analyzing the influence factors to the induced output voltage of the sensor, such as debris material, location, size, exciting current etc, the fitting characteristic curves are obtained, realizing the quantitative analysis. Simulation results show that the sensor model can effectively distinguish between conductive and non conductive, ferromagnetic and non ferromagnetic debris. The induced voltage can reach 10-4 V for the 500 microns ferromagnetic abrasive particles. The characteristic curves provide important basis for further research on sensor structure optimization.

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10

Xiao, Hong, Xinyu Wang, Hongcheng Li, Jiufei Luo, and Song Feng. "An Inductive Debris Sensor for a Large-Diameter Lubricating Oil Circuit Based on a High-Gradient Magnetic Field." Applied Sciences 9, no. 8 (April 14, 2019): 1546. http://dx.doi.org/10.3390/app9081546.

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Wear is one of the main factors of machine failure. If abnormal wear was not detected in time during the operation of a mechanical system, it probably leads to catastrophic consequences. The wear debris in the lubricating oil circuit contains much information about equipment wear. Consequently, debris detection is regarded as an effective way to detect mechanical faults. In this paper, an inductive debris sensor based on a high-gradient magnetic field is presented for high-throughput lubricating oil circuits. The excitation coil of the sensor is driven by a constant current to generate a high-gradient magnetic field, and the induction coil is wound around the flow path. When wear debris cuts the magnetic line through the flow path, a corresponding induced voltage is generated. The experimental results show that the sensor output signal is linear with the drive current and the wear debris velocity. In addition, the shortest distance between the particles that the sensor output signals can be completely separated is 25 mm. When the distance is smaller, the induced signals are superimposed.

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11

Wu, Sen, Zhijian Liu, Haichao Yuan, Kezhen Yu, Yuefeng Gao, Liankun Liu, and Xinxiang Pan. "Multichannel Inductive Sensor Based on Phase Division Multiplexing for Wear Debris Detection." Micromachines 10, no. 4 (April 13, 2019): 246. http://dx.doi.org/10.3390/mi10040246.

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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.

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12

Li, Yimeng, Jing Wu, and Qiang Guo. "Electromagnetic Sensor for Detecting Wear Debris in Lubricating Oil." IEEE Transactions on Instrumentation and Measurement 69, no. 5 (May 2020): 2533–41. http://dx.doi.org/10.1109/tim.2019.2962851.

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13

Wang, Zhi Juan, Jun Hong Zhao, and Gui Fu Ding. "A Micro-Channel Oil Debris Monitoring Sensor Based on a Planar Coil and Two Solenoids." Advanced Materials Research 898 (February 2014): 814–17. http://dx.doi.org/10.4028/www.scientific.net/amr.898.814.

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A micro-channel oil debris monitoring sensor based on planar coil is presented. The sensor, composed of one planar coil and two solenoids, detects metallic debris passing through its center by monitoring induced electromotive force of the planar coil. Based on the detection principle of the sensor, the detection circuit mainly consists of instrumentation amplifier, filter and amplifier. The testing results showed that the ferromagnetic metal particles under 100μm can be detected.

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14

Cuffaro, Vincenzo, Francesca Curà, and Andrea Mura. "Oil debris monitoring in misaligned spline couplings subjected to fretting wear." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 12 (October 28, 2014): 2261–69. http://dx.doi.org/10.1177/0954406214556401.

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Mechanical components may be subjected to wear damage that may cause the component failure. From the experimental point of view, the wear damage may be detected by analyzing the debris produced by the wear phenomena into the lubrication oil. This technique may be used to monitor the structural integrity of bearings and gear health by means of dedicated sensors. In this work, the oil debris production due to fretting wear in spline couplings has been investigated; in particular, the aim of this work is to identify both entity and onset phase of the surface damage by means of parameters obtained from the oil debris monitoring. Experimental tests have been performed by means of a dedicated test that allows to reproduce the real working conditions on spline coupling specimens, by varying both transmitted torque and misalignment angle. The oil debris production has been monitored by means of an optical sensor, in terms of particles size and numerosity. Results show that the wear damage may be identified by monitoring the variation of both Kurtosis of the particle distribution and amount of the particles production, both for as concerns phenomenon entity and corresponding onset.

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15

Becker, Andrew. "Health indicator metrics applicable to inductive wear debris sensors." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 231, no. 5 (August 16, 2016): 583–93. http://dx.doi.org/10.1177/1350650116665047.

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The Inductive Wear Debris Sensor is a relatively new invention that is increasingly being used for the detection of incipient machinery damage or failures by sensing metallic debris in lubrication systems. This type of sensor is typically used in-line and has a superior particle size detection range compared to traditional techniques such as the ubiquitous spectrometric oil analysis. There is, however, very little in the literature regarding the application and interpretation of data arising from this type of sensor. Unlike other condition monitoring sensors, no data will be generated by an Inductive Wear Debris Sensor in an ideal system; however, in real applications it is necessary to discriminate between occasional particles unrelated to a failure and incipient failure particles. Inductive Wear Debris Sensor data could be misinterpreted if a simple cumulative count limit was applied to the data. A short-term rate of particle generation is sometimes used as an alternative; however, it too can be misleading with short succession particles producing high instantaneous rates possibly causing false alarms. The purpose of this work was to develop a robust metric (or group of metrics) that when applied to Inductive Wear Debris Sensor data would reliably identify a failure event and exclude non-failure related particles. The Health Indicator described herein consists of three subordinate Condition Indices that collectively are shown to reliably detect the onset of rolling contact fatigue. The metrics have been applied to bearing test rig data (seeded fault) and data obtained from a non-seeded fault test of a complex helicopter gearbox.

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Mao, Huijie, Hongfu Zuo, Han Wang, Yibing Yin, and Xin Li. "Debris Recognition Methods in the Lubrication System with Electrostatic Sensors." Mathematical Problems in Engineering 2018 (December 20, 2018): 1–15. http://dx.doi.org/10.1155/2018/8043526.

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The oil-line electrostatic sensor (OLS) is a developing debris monitoring sensor. Previous work has shown that electrostatic charge signals can indicate the debris by calculating the Root Mean Square (RMS) value or the correlation-based indicator, but the precision of these methods is not high. This paper further developed the more accurate methods to obtain detailed debris information. Firstly, to interpret the monitoring principle of OLS and provide the guidance for developing the debris recognition methods, this paper analyzed the possible charge sources in the lubrication system and obtained the characteristics of the OLS by establishing its mathematical model. Further, a new OLS test rig was designed and verified the correctness of the sensor’s characteristics and its mathematical model. Based on the characteristics of the sensor, two new debris recognition methods were proposed. Finally, the effects of the new debris recognition methods were verified by the practical industrial gearbox bench test. Results showed that, compared to the traditional methods, the new methods could recognize the debris effectively and provide more detailed information of the debris.

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Bai, Chenzhao, Hongpeng Zhang, Lin Zeng, Xupeng Zhao, and Laihao Ma. "Inductive Magnetic Nanoparticle Sensor Based on Microfluidic Chip Oil Detection Technology." Micromachines 11, no. 2 (February 10, 2020): 183. http://dx.doi.org/10.3390/mi11020183.

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The wear debris in hydraulic oil or lubricating oil has a wealth of equipment operating information, which is an important basis for large mechanical equipment detection and fault diagnosis. Based on traditional inductive oil detection technology, magnetic nanoparticles are exploited in this paper. A new inductive oil detection sensor is designed based on the characteristics of magnetic nanoparticles. The sensor improves detection sensitivity based on distinguishing between ferromagnetic and non-ferromagnetic wear debris. Magnetic nanoparticles increase the internal magnetic field strength of the solenoid coil and the stability of the internal magnetic field of the solenoid coil. During the experiment, the optimal position of the sensor microchannel was first determined, then the effect of the magnetic nanoparticles on the sensor’s detection was confirmed, and finally the concentration ratio of the mixture was determined. The experimental results show that the inductive oil detection sensor made of magnetic nanoparticle material had a higher detection effect, and the signal-to-noise ratio (SNR) of 20–70 μm ferromagnetic particles was increased by 20%–25%. The detection signal-to-noise ratio (SNR) of 80–130 μm non-ferromagnetic particles was increased by 16%–20%. The application of magnetic nanoparticles is a new method in the field of oil detection, which is of great significance for fault diagnosis and the life prediction of hydraulic systems.

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Du, Li, and Jiang Zhe. "A high throughput inductive pulse sensor for online oil debris monitoring." Tribology International 44, no. 2 (February 2011): 175–79. http://dx.doi.org/10.1016/j.triboint.2010.10.022.

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Gorritxategi, Eneko, Alfredo García-Arribas, and Ana Aranzabe. "Innovative On-Line Oil Sensor Technologies for the Condition Monitoring of Wind Turbines." Key Engineering Materials 644 (May 2015): 53–56. http://dx.doi.org/10.4028/www.scientific.net/kem.644.53.

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A description of a system, developed for the condition monitoring of wind turbines, which combines innovative, real time, and on-line oil sensor technologies is described. The system integrates the measurement of the three main parameters that assess the status of the lubricating oil in the lubrication system using different technologies: the degree of oil degradation using visible absorbance spectroscopy; the water content using near infrared spectroscopy; and the presence of wear debris using image processing technology. The measuring principles, sensor integration and validation test results obtained in artificially degraded oil samples are presented.

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Han, Zhibin, Yishou Wang, and Xinlin Qing. "Characteristics Study of In-Situ Capacitive Sensor for Monitoring Lubrication Oil Debris." Sensors 17, no. 12 (December 8, 2017): 2851. http://dx.doi.org/10.3390/s17122851.

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Wen, Z., X. Yin, and Z. Jiang. "Applications of Electrostatic Sensor for Wear Debris Detecting in the Lubricating Oil." Journal of The Institution of Engineers (India): Series C 94, no. 3 (July 2013): 281–86. http://dx.doi.org/10.1007/s40032-013-0072-2.

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Islam, Tarikul, Mujeeb Yousuf, and Mohd Nauman. "A highly precise cross-capacitive sensor for metal debris detection in insulating oil." Review of Scientific Instruments 91, no. 2 (February 1, 2020): 025005. http://dx.doi.org/10.1063/1.5139925.

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Li, Chuan, and Ming Liang. "Enhancement of oil debris sensor capability by reliable debris signature extraction via wavelet domain target and interference signal tracking." Measurement 46, no. 4 (May 2013): 1442–53. http://dx.doi.org/10.1016/j.measurement.2012.12.001.

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Wu, Tong Hai, Ren Jie Gong, Xiao Gang Zhang, and Chen Xing Sheng. "A Conductivity-Based Sensor for Detecting Micro-Water in On-Line Oil Analysis." Advanced Materials Research 850-851 (December 2013): 279–83. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.279.

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Molecular water in lubricants causes unavoidable damage in mechanical tribo-systems. To illustrate the characteristics of water in oil, three typical hybrid states, including dissolved, free, and emulsified states, were discussed focusing on the physical structures, hazards, and electric properties. Aiming at the on-line monitoring of molecular water in engine oil, a conductivity-based sensor was proposed. Two enamel wires, with partial naked surfaces, were winded around an insulated pole in parallel to serve as a probe. A higher cell constant was obtained with a high sensitivity. A multi-channel sensor with four probes was designed for on-line monitoring. The performance of the sensor was examined experimentally. As main result, the sensor has explicit effects on detecting free and emulsified water. The sensor also has a good linearity and repeatability when water content increasing from 0% to 3%, and has a high reliability under the disturbances of air and metal debris.

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Shi, Haotian, Hongpeng Zhang, Wenqi Wang, Lin Zeng, Guangtao Sun, and Haiquan Chen. "An Integrated Inductive-Capacitive Microfluidic Sensor for Detection of Wear Debris in Hydraulic Oil." IEEE Sensors Journal 19, no. 23 (December 1, 2019): 11583–90. http://dx.doi.org/10.1109/jsen.2019.2936328.

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Zhu, Xiaoliang, Chong Zhong, and Jiang Zhe. "A high sensitivity wear debris sensor using ferrite cores for online oil condition monitoring." Measurement Science and Technology 28, no. 7 (June 2, 2017): 075102. http://dx.doi.org/10.1088/1361-6501/aa6adb.

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Liu, Ruochen, Hongfu Zuo, Jianzhong Sun, and Ling Wang. "Electrostatic monitoring of wind turbine gearbox on oil-lubricated system." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 19 (May 9, 2016): 3649–64. http://dx.doi.org/10.1177/0954406216648985.

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The electrostatic sensing technique has been verified to be a viable method for tribo-contact monitoring under laboratory conditions in previous investigations. This paper reports on the evolution of electrostatic monitoring on a real oil-lubricated wind turbine gearbox, using a modified oil-line sensor. In a nominal test and a ramp-up test, features were extracted and the presence of debris can be detected. The permutation entropy was further introduced in an accelerated life test. It can accurately reflect the wear condition of the gearboxes and detect early faults earlier than conventional techniques, which also has a better sensitivity and performance degradation trend than time-domain features.

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Zhu, Xiaoliang, Li Du, and Jiang Zhe. "A 3×3 wear debris sensor array for real time lubricant oil conditioning monitoring using synchronized sampling." Mechanical Systems and Signal Processing 83 (January 2017): 296–304. http://dx.doi.org/10.1016/j.ymssp.2016.06.014.

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Ma, Laihao, Hongpeng Zhang, Weiliang Qiao, Xiaoshuang Han, Lin Zeng, and Haotian Shi. "Oil Metal Debris Detection Sensor Using Ferrite Core and Flat Channel for Sensitivity Improvement and High Throughput." IEEE Sensors Journal 20, no. 13 (July 1, 2020): 7303–9. http://dx.doi.org/10.1109/jsen.2019.2962698.

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Desai, Prathamesh S., Victoria Granja, and C. Fred Higgs. "Lifetime Prediction Using a Tribology-Aware, Deep Learning-Based Digital Twin of Ball Bearing-Like Tribosystems in Oil and Gas." Processes 9, no. 6 (May 24, 2021): 922. http://dx.doi.org/10.3390/pr9060922.

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The recent decline in crude oil prices due to global competition and COVID-19-related demand issues has highlighted the need for the efficient operation of an oil and gas plant. One such avenue is accurate predictions about the remaining useful life (RUL) of components used in oil and gas plants. A tribosystem is comprised of the surfaces in relative motion and the lubricant between them. Lubricant oils play a significant role in keeping any tribosystem such as bearings and gears working smoothly over the lifetime of the oil and gas plant. The lubricant oil needs replenishment from time to time to avoid component breakdown due to the increased presence of wear debris and friction between the sliding surfaces of bearings and gears. Traditionally, this oil change is carried out at pre-determined times. This paper explored the possibilities of employing machine learning to predict early failure behavior in sensor-instrumented tribosystems. Specifically, deep learning and tribological data obtained from sensors deployed on the components can provide more accurate predictions about the RUL of the tribosystem. This automated maintenance can improve the overall efficiency of the component. The present study aimed to develop a deep learning-based digital twin for accurately predicting the RUL of a tribosystem comprised of a ball bearing-like test apparatus, a four-ball tester, and lubricant oil. A commercial lubricant used in the offshore oil and gas components was tested for its extreme pressure performance, and its welding load was measured using a four-ball tester. Three accelerated deterioration tests was carried out on the four-ball tester at a load below the welding load. Based on the wear scar measurements obtained from the experimental tests, the RUL data were used to train a multivariate convolutional neural network (CNN). The training accuracy of the model was above 99%, and the testing accuracy was above 95%. This work involved the model-free learning prediction of the remaining useful lifetime of ball bearing-type contacts as a function of key sensor input data (i.e., load, friction, temperature). This model can be deployed for in-field tribological machine elements to trigger automated maintenance without explicitly measuring the wear phenomenon.

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Sanga, Ramesh, V. S. Srinivasan, M. Sivaramakrishna, and G. Prabhakara Rao. "Deployment of an inductance-based quasi-digital sensor to detect metallic wear debris in lubricant oil of rotating machinery." Measurement Science and Technology 29, no. 7 (May 24, 2018): 075102. http://dx.doi.org/10.1088/1361-6501/aac078.

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Fan, X., M. Liang, and T. Yeap. "A joint time-invariant wavelet transform and kurtosis approach to the improvement of in-line oil debris sensor capability." Smart Materials and Structures 18, no. 8 (June 4, 2009): 085010. http://dx.doi.org/10.1088/0964-1726/18/8/085010.

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Hall, D. L., R. J. Hansen, and D. C. Lang. "The Negative Information Problem in Mechanical Diagnostics." Journal of Engineering for Gas Turbines and Power 119, no. 2 (April 1, 1997): 370–77. http://dx.doi.org/10.1115/1.2815584.

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Condition-based maintenance (CBM) is an emerging technology, which seeks to develop sensors and processing systems aimed at monitoring the operation of complex machinery such as turbine engines, rotor craft drivetrains, and industrial equipment. The goal of CBM systems is to determine the state of the equipment (i.e., the mechanical health and status), and to predict the remaining useful life for the system being monitored. The success of such systems depends upon a number of factors, including: (1) the ability to design or use robust sensors for measuring relevant phenomena such as vibration, acoustic spectra, infrared emissions, oil debris, etc.; (2) real-time processing of the sensor data to extract useful information (such as features or data characteristics) in a noisy environment and to detect parametric changes that might be indicative of impending failure conditions; (3) fusion of multi-sensor data to obtain improved information beyond that available to a single sensor; (4) micro and macro level models, which predict the temporal evolution of failure phenomena; and finally, (5) the capability to perform automated approximate reasoning to interpret the results of the sensor measurements, processed data, and model predictions in the context of an operational environment. The latter capability is the focus of this paper. Although numerous techniques have emerged from the discipline of artificial intelligence for automated reasoning (e.g., rule-based expert systems, blackboard systems, case-based reasoning, neural networks, etc.), none of these techniques are able to satisfy all of the requirements for reasoning about condition-based maintenance. This paper provides an assessment of automated reasoning techniques for CBM and identifies a particular problem for CBM, namely, the ability to reason with negative information (viz., data which by their absence are indicative of mechanical status and health). A general architecture is introduced for CBM automated reasoning, which hierarchically combines implicit and explicit reasoning techniques. Initial experiments with fuzzy logic are also described.

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Peng, Yeping, Tonghai Wu, Shuo Wang, Ying Du, Ngaiming Kwok, and Zhongxiao Peng. "A microfluidic device for three-dimensional wear debris imaging in online condition monitoring." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 231, no. 8 (December 15, 2016): 965–74. http://dx.doi.org/10.1177/1350650116684707.

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Three-dimensional morphologies of wear particles are important information sources for machine condition assessment and fault diagnosis. However, existing three-dimensional image acquisition systems, such as laser scanning confocal microscopy and atomic force microscopy, cannot be directly applied in condition-based maintenance of machines. In order to automatically acquire three-dimensional information of wear debris for online condition monitoring, a microfluidic device consisting of an oil flow channel and a video imaging system is developed. This paper focuses on the control of particle motions. A microchannel is designed to ensure the continuous rotation of particles such that their three-dimensional features can be captured. The relationships between running torque and channel height and particle size are analysed to determine the channel height. An infinite fluid field is considered to make sure that the particles rotate around the same axis to capture 360 degree views. Based on this, the cross section of the microchannel is determined at 5 mm × 0.2 mm (height × width) to capture the wear debris under 200 µm. A CMOS sensor is used to image the particles in multiple views and then three-dimensional features of wear debris (e.g. thickness, height aspect ratio and sphericity) are obtained. Two experiments were carried out to evaluate the performances of the designed system. The results demonstrate that (1) the microfluidic device is effective in capturing multiple view images of wear particles various in sizes and shapes; (2) spatial morphological characteristics of wear particles can be constructed using a sequence of multi-view images.

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Xie, Yucai, Haotian Shi, Hongpeng Zhang, Shuang Yu, Lebile Llerioluwa, Yiwen Zheng, Guobin Li, Yuqing Sun, and Haiquan Chen. "A Bridge-Type Inductance Sensor With a Two-Stage Filter Circuit for High-Precision Detection of Metal Debris in the Oil." IEEE Sensors Journal 21, no. 16 (August 15, 2021): 17738–48. http://dx.doi.org/10.1109/jsen.2021.3085361.

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Harkemanne, Etienne, Olivier Berten, and Patrick Hendrick. "Analysis and Testing of Debris Monitoring Sensors for Aircraft Lubrication Systems." Proceedings 2, no. 8 (June 15, 2018): 461. http://dx.doi.org/10.3390/icem18-05360.

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In an aircraft engine, some pieces are describing a rotating movement. These parts are in contact with rotating and non-rotating parts through the bearings and gears. The different contact patches are lubricated with oil. During the lifetime of the engine, mechanical wear is produced between the contacts. This wear of the bearings and gears will produce some debris in the oil circuit of the engine. To ensure the effective operation of the aircraft engines, the debris monitoring sensors play a significant role. They detect and collect the debris in the oil. The analysis of the debris can give an indication of the overall health of the engine. The aim of the paper is to develop, design and model an oil test bench to simulate the oil lubrication circuit of an aircraft engine to test two different debris monitoring sensors. The methodology consists of studying the oil lubrication system of the aircraft engine. The first step is to build the oil test bench. Once the oil test bench is functional, tests are performed on the two debris monitoring sensors. A test plan is followed, three sizes of debris, like the type and sizes of debris found in the aircraft engine oil, are injected in the oil. The test parameters are the oil temperature, the oil flow rate and the mass of debris injected. Each time debris is injected, it is detected and caught by the two sensors. The test results given by the two sensors are similar to the mass debris injected into the oil circuit. The two sensors never detect the total mass of debris injected in the oil. On average, 55%–60% of the mass injected is detected and caught by the two sensors. The sensors are very efficient at detecting debris whose size corresponds to the design range parameters of the sensors, but the efficiency falls when detecting debris whose size lies outside this range.

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Becker, Andrew, Sylvester Abanteriba, Scott Dutton, David Forrester, and Glen Rowlinson. "On the impact of fine filtration on spectrometric oil analysis and inductive wear debris sensors." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 230, no. 1 (June 26, 2015): 78–85. http://dx.doi.org/10.1177/1350650115592917.

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López de Calle, Kerman, Susana Ferreiro, Constantino Roldán-Paraponiaris, and Alain Ulazia. "A Context-Aware Oil Debris-Based Health Indicator for Wind Turbine Gearbox Condition Monitoring." Energies 12, no. 17 (September 2, 2019): 3373. http://dx.doi.org/10.3390/en12173373.

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One of the greatest challenges of optimising the correct operation of wind turbines is detecting the health status of their core components, such as gearboxes in particular. Gearbox monitoring is a widely studied topic in the literature, nevertheless, studies showing data of in-service wind turbines are less frequent and tend to present difficulties that are otherwise overlooked in test rig based works. This work presents the data of three wind turbines that have gearboxes in different damage stages. Besides including the data of the SCADA (Supervisory Control And Signal Acquisition) system, additional measurements of online optical oil debris sensors are also included. In addition to an analysis of the behaviour of particle generation in the turbines, a methodology to identify regimes of operation with lower variation is presented. These regimes are later utilised to develop a health index that considers operation states and provides valuable information regarding the state of the gearboxes. The proposed health index allows distinguishing damage severity between wind turbines as well as tracking the evolution of the damage over time.

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Yu, Bing, Nan Cao, and Tianhong Zhang. "A novel signature extracting approach for inductive oil debris sensors based on symplectic geometry mode decomposition." Measurement 185 (November 2021): 110056. http://dx.doi.org/10.1016/j.measurement.2021.110056.

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Zeng, Lin, Hongpeng Zhang, Qiang Wang, and Xingming Zhang. "Monitoring of Non-Ferrous Wear Debris in Hydraulic Oil by Detecting the Equivalent Resistance of Inductive Sensors." Micromachines 9, no. 3 (March 8, 2018): 117. http://dx.doi.org/10.3390/mi9030117.

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Marques, Mario Monteiro, Victor Lobo, A. Pedro Aguiar, J. Estrela Silva, J. Borges de Sousa, Maria de Fátima Nunes, Ricardo Adriano Ribeiro, Alexandre Bernardino, Gonçalo Cruz, and Jorge Salvador Marques. "An Unmanned Aircraft System for Maritime Operations: The Automatic Detection Subsystem." Marine Technology Society Journal 55, no. 1 (January 1, 2021): 38–49. http://dx.doi.org/10.4031/mtsj.55.1.4.

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AbstractThis paper addresses the development of an integrated system to support maritime situation awareness based on unmanned aerial vehicles (UAVs), emphasizing the role of the automatic detection subsystem. One of the main topics of research in the SEAGULL project was the automatic detection of sea vessels from sensors onboard the UAV, to help human operators in the generation of situational awareness of maritime events such as (a) detection and geo-referencing of oil spills or hazardous and noxious substances, (b) tracking systems (e.g., vessels, shipwrecks, lifeboats, debris), (c) recognizing behavioral patterns (e.g., vessels rendezvous, high-speed vessels, atypical patterns of navigation), and (d) monitoring environmental parameters and indicators. We describe a system composed of optical sensors, an embedded computer, communication systems, and a vessel detection algorithm that can run in real time in the embedded UAV hardware and provide to human operators vessel detections with low latency, high precision rates (about 99%), and suitable recalls (>50%), which is comparable to other more computationally intensive state-of-the-art approaches. Field test results, including the detection of lifesavers and multiple vessels in red-green-and-blue (RGB) and thermal images, are presented and discussed.

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Li, Chuan, Juan Peng, and Ming Liang. "Enhancement of the Wear Particle Monitoring Capability of Oil Debris Sensors Using a Maximal Overlap Discrete Wavelet Transform with Optimal Decomposition Depth." Sensors 14, no. 4 (March 28, 2014): 6207–28. http://dx.doi.org/10.3390/s140406207.

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Vats, Sudeep. "Health Monitoring of New and Aging Pipelines- Development and Application of Instrumented Pigs." Advanced Materials Research 433-440 (January 2012): 6121–27. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6121.

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More than 3 million kilometers of high pressure liquid and gas pipelines are installed all over the world.Usually steel is the safest means to transport large quantities of oil and oil related products and natural gas however, just like any other technical component, pipelines also deteriorate with time, result of which, flaws appear and they grows until the pipeline fails. That is why Pipeline operators worldwide maintain the good health of buried cross-country pipelines with a combination of good quality external coating and cathodic protection. Beside this various other techniques like cleaning and debris removal by scrapper pigging at intervals depending upon the life of pipeline and the products being transported through the same are used. Use of corrosion inhibitors and internal coating is also done to protect the internal surface from corrosion. It is of greatest importance to ensure the safety, efficiency,environmental integrity and regulatory compliance of the worldwide pipeline infrastructure. Achieving this objective entails the need for effective inspection technologies, incorporating the accuracy and reliability required for optimized maintenance strategies. Intelligent pigs are used for inline inspection of buried pipelines to monitor their thorough health,assess the risks associated with their operation,pilferage checking and cracks etc. These IPIGS are capable of detecting metal loss upto 5-10 percent of wall thickness on inner as well as external surfaces of pipeline. IPIGS based on ultrasonic and eddy current sensors are capable of detecting cracks also.[10] On the basis of the wall thickness loss and the intensity of defects, pipeline risk assessment is done and preventive and corrective measures are planned. This paper presents the technical details of instrumented pig with its advantages and disadvantages over conventional methods of pipeline protection and inspection. It also elaborates the concept of multiple technology intelligent pig and scope for future research on intelligent pigging. (abstract)

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Bamford, Holly A., and Carol Kavanagh. "The National Ocean Service: Positioning America for the Future." Marine Technology Society Journal 49, no. 2 (March 1, 2015): 10–22. http://dx.doi.org/10.4031/mtsj.49.2.13.

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AbstractThe National Ocean Service (NOS), a line office of the National Oceanic and Atmospheric Administration (NOAA), is the nation's ocean and coastal agency. Our activities span a broad range that includes charting our nation's coastline; defining the National Spatial Reference System; providing the national network of coastal tide and water level sensors; serving as the lead federal agency of the U.S. Integrated Ocean Observing System; administering the Coastal Zone Management Program; providing the scientific foundation and socioeconomic information to local, state, and regional decision makers to adapt to the impacts of coastal hazards and climate change; serving as the authoritative resource for science related to debris, oil, and chemical spills; managing marine sanctuaries; and supporting the management of estuarine research reserves, coral reefs, and marine protected areas. Today, our coasts and coastal communities face increasingly significant impacts of higher intensity coastal storms; changing sea levels and Great Lakes levels; increased coastal development; increased demand on natural resources and infrastructure; and increased demands on our marine transportation system. In response to these issues, NOS aligns its activities along three priorities: (1) supporting coastal resilience; (2) advancing coastal intelligence; and (3) promoting place-based conservation. NOS relies on coastal observations and data products to carry out our mission. Characteristics of future coastal observations include lower cost coupled with greater efficiency, diverse platforms, multiuse data collection, and crowdsourcing. Data products will need to be increasingly geographically tailored; result from a greater degree of coordination and integration; and result in greater data access.

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Talebi, Abolfazl, Seyed Vahid Hosseini, Hadi Parvaz, and Mehdi Heidari. "Design and fabrication of an online inductive sensor for identification of ferrous wear particles in engine oil." Industrial Lubrication and Tribology ahead-of-print, ahead-of-print (May 19, 2021). http://dx.doi.org/10.1108/ilt-12-2020-0439.

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Purpose The presence of ferrous wear debris in lubricating oil may cause progressive damage in the internal combustion engines. Online monitoring of the size and concentration of these particles in the oil is a way to optimize the engine performance and its life cycle. Design/methodology/approach In this study, an online sensor was designed and fabricated to identify ferrous wear particles in the engine oil based on the induction method. The diameter of the sensor outlet duct was designed as small as possible to generate a high-intensity magnetic induction and achieve a proper sensitivity in the sensor. The experiments were designed and performed in offline mode. Furthermore, to evaluate the actual performance of the sensor in presence of iron particles in the oil, online tests were performed at different sizes and concentrations. Findings It was concluded from offline tests that the highest sensitivity of the sensor occurs at the frequency and voltage of 2.5 kHz and 120 V, respectively. According to the results of the online tests, the larger the particle size, the higher the peaks at the sensor output. Also, a high density of the peaks was observed in the sensor output graphs as the concentration of particles was increased. Originality/value The proposed sensor was able to identify ferrous wear particles larger than 125 µm separately, which is the failure limit in the internal combustion engines.

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Muthuvel, P., Boby George, and G. A. Ramadass. "A Highly Sensitive In-line Oil Wear Debris Sensor Based on Passive Wireless LC Sensing." IEEE Sensors Journal, 2020, 1. http://dx.doi.org/10.1109/jsen.2020.3036154.

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Du, Ying, Chaoqun Duan, and Tonghai Wu. "Lubricating oil deterioration modeling and remaining useful life prediction based on hidden semi-Markov modeling." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, August 27, 2021, 135065012110381. http://dx.doi.org/10.1177/13506501211038106.

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Lubricating oil, which carries information about machine’s health condition, is of great importance to the performance of machines in the full life cycle. The main purpose of oil deterioration modeling and its remaining useful life prediction is to determine the exact time that the lubricating oil has degraded and it is no longer able to maintain its functions. Generally, lubricating oil deterioration can be partially detected by condition monitoring based on wear debris analysis, and thus can be categorized into three states. In our paper, vector data, which contain wear debris concentration and carry information about the state of lubricating oil, are obtained by an on-line visual ferrograph sensor from a four-ball tester at regular sampling epochs. The oil’s state process is described by a hidden semi-Markov model, and its sojourn times in each state are assumed to be Erlang distributed. A vector autoregressive method based on time series modeling is presented to obtain residual observations, which are regarded as the observable process of oil information in the hidden semi-Markov model framework. The unknown parameters of the hidden semi-Markov model are then estimated by using expectation-maximization algorithm. Afterward, a Bayesian updating approach is presented to derive the explicit formulas of the conditional reliability and mean residual life. To validate the proposed approach, a real case study of lubricating oil deterioration is demonstrated and a comparison with the hidden Markov model is given to illustrate the effectiveness of the new developed remaining useful life prediction approach for lubricating oil.

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Journal articles on the topic 'Oil debris sensor'

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