Condition monitoring wind turbine gearboxes using on-line/in-line oil analysis techniques

Paper examining condition monitoring wind turbine gearboxes using on-line/in-line oil analysis techniques

Detailed state of the art review for the different on-line/in-line oil analysis techniques in context of wind turbine gearboxes

The main driver behind developing advanced condition monitoring (CM) systems for the wind energy industry is the delivery of improved asset management regarding the operation and maintenance of the gearbox and other wind turbine components and systems. Current gearbox CM systems mainly detect faults by identifying ferrous materials, water, and air within oil by changes in certain properties such as electrical fields. In order to detect oil degradation and identify particles, more advanced devices are required to allow a better maintenance regime to be established. Current technologies available specifically for this purpose include Fourier transform infrared (FTIR) spectroscopy and ferrography. There are also several technologies that have not yet been or have been recently applied to CM problems. After reviewing the current state of the art, it is recommended that a combination of sensors would be used that analyze different characteristics of the oil. The information individually would not be highly accurate but combined it is fully expected that greater accuracy can be obtained. The technologies that are suitable in terms of cost, size, accuracy, and development are online ferrography, selective fluorescence spectroscopy, scattering measurements, FTIR, photoacoustic spectroscopy, and solid state viscometers

Magnetic properties of ferromagnetic particles under alternating magnetic fields: Focus on particle detection sensor applications

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. The electromagnetic wear particles detection sensor has been widely studied due to its ability to monitor the wear status of equipment in real time. To precisely estimate the change of the magnetic energy of the sensor coil caused by the wear particles, the magnetic property models of wear particles under the alternating magnetic field was established. The models consider the hysteresis effect and the eddy current effect of the wear particles. The analysis and experimental results show that with the increase of the effective field frequency, the change of the magnetic energy caused by the wear particles gradually decrease, which makes the induced electromotive force output by the sensor reduce with the decrease of the particle speed, so a signal compensation method is presented to obtain a unified signal when the same wear particle passing through the sensor in different speeds. The magnetic coupling effect between the two adjacent wear particles is analyzed. The result illustrates that the change of the magnetic energy caused by the dual wear particles system is larger than the sum of the energy variation caused by two independent wear particles, and with the increase of the interparticle distance, the magnetic coupling effect gradually weakens and disappears

Sensors and Tribological Systems: Applications for Industry 4.0

Purpose: The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers particularly in the developing economies. Real-time and reliable data is an important enabler for implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper attempts to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology related industries and equipment. Design/methodology/approach: A through literature review has been done to study the interrelationships between the availability of tribology related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors employed in generating tribological data is also presented. Some studies related to application of machine learning and Artificial Intelligence are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included. Findings: Industry 4.0 and tribology are interconnected through various means and the various pillars of industry 4.0 such as big data, Artificial Intelligence can effectively be implemented in various tribological systems. Data is an important parameter in effective application of concepts of industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition. Originality: The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of industry 4.0

Development of novel gearbox lubrication condition monitoring sensors in the context of wind turbine gearboxes

Wind power has become established as an alternative power source that forms a significant proportion of national energy generation. An increasing proportion of turbines is being constructed offshore to exploit higher average wind speeds and to avoid development issues associated with onshore wind farms. Isolated locations and unpredictable weather conditions lead to increased access costs for operators when conducting scheduled and unscheduled maintenance and repairs. This has increased interest in condition monitoring systems which can track the current state of components within a wind turbine and provide operators with predicted future trends. Asset management can be improved through condition based maintenance regimes and preventative repairs. Development of novel condition monitoring systems that can accurately predict incipient damage can optimise operational performance and reduce the overall level of wind turbine generation costs. The work described in this thesis presents the development of novel sensors that may be applied to monitor wind turbine gearboxes, a component that experiences relatively high failure rates and causes considerable turbine downtime. Current systems and technology that may be adapted for use in wind turbine condition monitoring are evaluated. Lubrication related monitoring systems have been identified as an area that could be improved and are divided into those that track liberated wear material suspended in the lubricant and those that assess the state of the lubricant itself. This study presents two novel lubrication based gearbox monitoring sensors that potentially offer a low cost solution for continuous data capture. The first demonstrates the potential for active pixel sensors such as those found in digital cameras to capture images of wear particles within gearbox lubricants. Particle morphology was tracked in this system, allowing the type of particles to be correlated with the type of wear that is generated and a potential source. The second sensor uses a targeted form of infra-red absorption spectroscopy to track changes in the lubricant chemistry due to the increase in acidity. Ensuring the lubricant is functioning correctly decreases component stress and fatigue, reducing maintenance requirements.Wind power has become established as an alternative power source that forms a significant proportion of national energy generation. An increasing proportion of turbines is being constructed offshore to exploit higher average wind speeds and to avoid development issues associated with onshore wind farms. Isolated locations and unpredictable weather conditions lead to increased access costs for operators when conducting scheduled and unscheduled maintenance and repairs. This has increased interest in condition monitoring systems which can track the current state of components within a wind turbine and provide operators with predicted future trends. Asset management can be improved through condition based maintenance regimes and preventative repairs. Development of novel condition monitoring systems that can accurately predict incipient damage can optimise operational performance and reduce the overall level of wind turbine generation costs. The work described in this thesis presents the development of novel sensors that may be applied to monitor wind turbine gearboxes, a component that experiences relatively high failure rates and causes considerable turbine downtime. Current systems and technology that may be adapted for use in wind turbine condition monitoring are evaluated. Lubrication related monitoring systems have been identified as an area that could be improved and are divided into those that track liberated wear material suspended in the lubricant and those that assess the state of the lubricant itself. This study presents two novel lubrication based gearbox monitoring sensors that potentially offer a low cost solution for continuous data capture. The first demonstrates the potential for active pixel sensors such as those found in digital cameras to capture images of wear particles within gearbox lubricants. Particle morphology was tracked in this system, allowing the type of particles to be correlated with the type of wear that is generated and a potential source. The second sensor uses a targeted form of infra-red absorption spectroscopy to track changes in the lubricant chemistry due to the increase in acidity. Ensuring the lubricant is functioning correctly decreases component stress and fatigue, reducing maintenance requirements

Real-time and online lubricating oil condition monitoring enabled by triboelectric nanogenerator

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

Active Lubricant Condition Monitoring

Lubrication in a mechanical system is like blood flowing through a human body. It is a great source to detect in advance any troubles in the system. As blood analysis shows the diseases in our body in a machine the oil analysis shows the fault in a system. Active lubricant condition monitoring is a great prediction technique which would help improve the reliability and reduce the maintenance costs for a system. The paper discusses about how to develop an Active oil condition monitoring where oil condition can be monitored online and how to achieve an active maintenance action. It discusses the design and setup of an oil condition monitoring system on a test bench provided. Different oil degradation parameters are analysed and sensors are introduced to monitor this parameters. Integration of microcontroller for wireless communication and use of different software’s to acquire and process the data from the sensors to provide the real time condition of the system is discussed. This includes different actuation system that can be introduced to help the maintenance of the system to minimum and reduce the maintenance or damaging of the system components

On-Line Monitoring of Engine Health Through the Analysis of Contaminants in Engine Lubricant

Monitoring automobile liquids, such as engine lubricants, has received increasing attention recently mainly due to environmental and safety legislation, coat saving measures, and customer demand. Literature review in monitoring engine lubricant condition indicates systems approach, an intellectual discipline method to address complex problem, has never been used to monitor engine performance and health through the engine sub-systems such as lubricant system. The literature review also points toward deficiency in considering lubricant as a source of information for engine performance evaluation, and lack of understanding of engine lubricant as a medium with random properties. Engine lubricant condition reflects the state of health of engine through its properties. Recognition and analysis of the correlation between engine lubricant system based on the lubricant properties and engine performance is crucial to provide insight into engine health. The contribution of this research will be implementation of systems approach to monitor engine performance through engine lubricant using new methodologies of surface plasmon resonance, object shape based optical analysis and statistical optical analysis methodologies to monitor optical properties of lubricant with respect to aging process and contaminants in real time and on-line. Degradation of engine lubricant causes variation in the optical properties of lubricant such as refractive index, absorption, statistical optical characteristics, shape parameters and etc. The purpose of using surface plasmon resonance (SPR) is to study the change in the reflectivity and incidence angle caused by variation in the refractive index and absorption of lubricant due to its degradation and presence of contaminants. Utilization of SPR measurement for characterization of engine lubricant will develop new knowledge which can be used for on-line condition monitoring of lubricant quality. To investigate the variation in statistical optical characteristics of lubricant, this research also introduces two new methodologies. Statistical optic and object shape-based methodologies are based on the optical analysis of the distortion effect when an object image is obtained through a thin random medium. In the object shape-based optical analysis, several parameters of an acquired object image are measured and compared. In the statistical optic analysis methodology, statistical auto and cross-characteristics are used for the analysis of combined object-lubricant images. Both proposed methodologies utilize the comparison of measured and calculated parameters for fresh and contaminated lubricants. Proposed methodologies are verified experimentally showing ability to distinguish lubricant with different contamination individually and in a combined form. Capabilities of the proposed methodologies are extended to establish the linkage between accumulated travelled distance and the change in the optical statistical properties of the lubricant. Also, on board analysis to detect the presence of coolant, gasoline and water (1%-5%) are performed