| 1. |
- Ahmer, Muhammad
(author)
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Intelligent fault diagnosis and predictive maintenance for a bearing ring grinder
- 2023
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Doctoral thesis (other academic/artistic)abstract
- Predicting the failure of any structure is a difficult task in a mechanical system. However complicated and difficult the prediction might be, the first step is to know the actual condition of the system. Given the complexity of any machine tool, where a number of subsystems of electro-mechanical structures interact to perform the machining operation, failure diagnostics become more challenging due to the high demand for performance and reliability. In a production environment, this results in maintenance costs that the management always strives to reduce. Condition-based machine maintenance (CBM) is considered to be the maintenance strategy that can lead to failure prediction and reducing the maintenance cost by knowing the actual condition of the asset and planning the maintenance activities in advance.Grinding machines and grinding processes have come a long way since the inception of the centuries old grinding technique. However, we still have a number of challenges to overcome before a completely monitored and controlled machine and process can be claimed. One such challenge is to achieve a machine level CBM and predictive maintenance (PdM) setup which is addressed in this thesis. A CBM implementation framework has been proposed which combines the information sampled from sensors installed for the purpose of the process as well as condition monitoring. Accessing the machine's controller information allows the data to be processed with respect to different machine states and process stages. The successful implementation is achieved through a real-time and synchronized data acquisition setup that allows data from multiple sources to be acquired, stored, and consolidated. The dataset thus generated is used in a significant part of this project and is also published in Swedish National Data Service (SND).The thesis also presents the failure diagnostic model based on two step classification approach using benchmarked random forest models. The binary classifier predicts if there is a fault present in the machine based on crucial sensors data from the Idle segment of the grinding cycle. Multi-class random forest classifier diagnosis the fault condition. PdM, knowing when to trigger maintenance action, is achieved through predicting the overall quality of the produced parts from the feature set extracted from sensor data of the Spark-out segment of the grinding cycle. Combining fault diagnosis with the predicted quality information resulted in reliable and actionable maintenance decisions for the bearing ring grinder. The demonstrated setup, based on a production bearing ring grinder, is adaptable to similar machines in production.
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| 2. |
- Chen, Jun
(author)
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Optimizing Ice-Resistant Surfaces: Unifying Self-Healing, Durability, and Functional Design for Superior Anti-/De-Icing Performance
- 2024
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Doctoral thesis (other academic/artistic)abstract
- Snow and ice accumulation on critical infrastructure such as wind power turbines and power lines cause significant challenges and safety hazards in cold climate regions during wintertime. Research into anti-/de-icing technologies has been divided into two main streams, i.e., active, and passive approaches. Active technologies, including electric thermal, photothermal technologies, etc, are widely used in anti-/de-icing fields. Passive technologies, including hydrophobic and slippery surfaces, have gained increasing interest due to their low energy consumption and sustainable profile, but these passive technologies are often limited by relatively short service life and poor mechanical durability. A potential way of improving the anti-/de-icing performance would be to combine different technologies and create electric thermal superhydrophobic surfaces and/or photo-thermal superhydrophobic surfaces. Furthermore, the mechanical durability could be improved by developing self-healing superhydrophobic surfaces and wear-resistant electric thermal surfaces. However, some important studies of relevant mechanisms to achieve this are absent in the literature, such as the influence of self-healing on ice adhesion, and investigation on how to unify the durability and anti-/de-icing performances via molecular structure design. This thesis addresses these questions by focusing on enhancing the wear resistance and anti-/de-icing efficiency of anti-/de-icing materials through innovative material design. We conducted ice-phobic tests in lab environment, and long-term ice-phobic field tests, which helped us to further understand and optimize the design of ice-phobic surfaces. This thesis contributes to developing more durable, efficient, and sustainable anti-/de-icing solutions, addressing the critical need for reliable performance under adverse weather conditions. The key findings were: (1) A novel self-healing and low-ice adhesion poly silicon urea coating was developed, leveraging the intrinsic material structure for creating sufficient wear resistance and self-healing capabilities. The Poly silicon urea coating exhibits below 10kPa ice adhesion strength, which is far lower than the ice-phobic surface request(<100kPa). The molecular structure’s influence on self-healing and ice adhesion are specified in this work.(2) Inspired by the low-icing bonding properties of silicon urea, a graphene-enhanced siloxane urea multi-functional coating was designed, where the low-icing properties were combined with electric and thermal conductivity to achieve both active and passive anti-/de-icing effects. This graphene enhancement coating exhibits 10 minutes of removing all ice accretion under ~570W/m2 electric power on the lab scale test. The field tests, where a graphene enhancement coating surface can keep ice-free under ~310W/m2 during the whole winter in a harsh natural environment. (3) To explore the influence of mechanical durability on ice-phobic, a composite coating which integrates wear resistance and thermal conductive was formulated. Graphene was proven as a suitable additive to enhance thermal conductivity and wear resistance. Compared with the blank control coating and a boron nitride composite coating, the thermal conductivity of a graphene composite coating increased around 3 times, and the anti-wear performance based on wear depth was increased around 1.5 times. The wear mechanism and wear influence on anti-/de-icing behaviour are investigated in this work. (4) This work also explored the impact of surface functional groups on anti-/de-icing performance, uncovering that the force interactions and steric radius of these groups significantly influence surface element distribution and material strength, thereby affecting wettability and wear behaviour. The results show that the hydrophobicity of the groups is not the only factor to influence the surface properties. A smaller steric radius and strong interactions are beneficial for reducing the van der Waals' gap between groups which can inhibit the wetting of the water molecules. The influence of five different typical groups on mechanical durability and ice adhesion is investigated in this work.
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| 3. |
- Marklund, Pär
(author)
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Wet clutch tribological performance optimization methods
- 2008
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Doctoral thesis (other academic/artistic)abstract
- Wet clutches are used in a variety of machinery such as in vehicles where they are used to distribute torque in the drivetrains. Clutches can be located in automatic transmissions or in limited slip differentials. The frictional behavior of a clutch is of great importance for overall vehicle behavior and has to be thoroughly investigated when designing new wet clutch applications. Frictional behavior is normally studied in test rigs where whole friction discs or complete clutches are tested under similar working conditions to those pertaining to the clutches in the drivetrain of the vehicle. However, today clutch behavior may be simulated with regard to some clutch applications and design of the clutch system is not limited to testing. This is an advantage as it is possible to simulate behavior that may not be possible or suitable to study in a test rig. Another advantage is that the design process is faster and more cost efficient than that which is possible when all tests are carried out in a laboratory. The torque transferred by the clutch during engagement can roughly be divided into full film torque and boundary lubrication torque. Full film torque originates from the part of the engagement where the clutch discs are completely separated by a lubricant film and the friction surfaces are not in contact, whereas boundary lubrication torque occurs when the lubricant film is so thin that the surfaces of the clutch discs are in direct contact, only separated by a thin additive film. The distribution between full film torque transfer and boundary lubrication torque transfer differs for different types of wet clutch and for differing operating conditions. When the clutch works in full film regime it is possible to simulate the friction quite well. However, the friction in the boundary lubrication regime is much more difficult to model and simulate since it is very dependent on the additives. Wet clutches are most commonly used in automatic transmissions for vehicles. As a result, most research into wet clutch testing and most simulations concerns wet clutches suitable for such applications. In an automatic transmission the wet clutch is often used to brake a rotating shaft to stand still relative to another shaft and the total engagement has a duration of fractions of a second. During most of the engagement the clutch is working in full film lubrication. In this thesis the focus is on wet clutches working under limited slip conditions: in other words this thesis studies clutches that are working with a small amount of slip over a long period without reaching a state of lock-up. These clutch types can be found, for example, in limited slip differentials. During this type of engagement the clutch mainly works under boundary lubrication conditions and much heat can be generated. The optimum method of designing a new wet clutch would be to simulate the clutch performance without having to do any measurements in the laboratory. This, however, is not yet possible, but an efficient way to design clutches can be achieved by combining simple measurements with efficient computer simulations. In this thesis, simple measurement techniques for wet clutch materials are developed and combined with a temperature simulation of a wet clutch, where the lubricant cooling flow, which is dependent on the surface roughness and groove pattern, is simulated. This method makes it possible to optimize a wet clutch for given working conditions with regard to lubricant, friction material, surface roughness and groove pattern. The simulations are validated to measured data from a test rig in which torque behavior from whole friction discs are investigated. Good agreement between simulations and measurements is achieved.
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| 4. |
- Strömbergsson, Daniel
(author)
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Improving detection and diagnosis of bearing failures in wind turbine drivetrains
- 2020
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Doctoral thesis (other academic/artistic)abstract
- Wind power has in the last 20 years grown into one of the main sources of renewable energy in the world, with both the amount and size of the turbines increasing substantially. One of the major challenges for the wind power industry is the premature failures of especially the drivetrain components. These failures cause a lot of turbine downtime, which increases the operation and maintenance costs of the turbines. Failures in the gearbox have been shown to lead to the highest downtime and the multitude of bearings within that subsystem is overrepresented in the total amount of component failures. Vibrationbased condition monitoring is considered the best method to find these types of defects early and avoid prolonged turbine downtime. Previous research has therefore been focused on the different aspects of condition monitoring; i.e. measurement technologies, signal analysis of vibration measurements to improve detection and diagnosis as well as the implementation of machine learning solutions. However, the majority of research work has yet to evaluate the performance of new developments using wind turbine field data, and still no fundamentally new developments have seen a large-scale implementation in the industry. Further, it is known that the positioning of the accelerometer, used to measure the vibrations, affects the ability to detect and diagnose defects. However, it is not known how to optimally position the accelerometers to monitor the individual drivetrain sub-systems. Also, previous research does not show how the influence of the measurement properties of the field data affect the ability to detect and diagnose component failures.Therefore, this thesis provides a comprehensive evaluation of how to improve the detection and diagnosis of bearing failures specifically in wind turbine drivetrains. In this thesis, a simulation model was developed to study how the accelerometer positioning affects the measurement quality. Vibration simulations of a similar sized bearing to ones found in the wind turbine drivetrain show an optimal accelerometer position as close to the primary loaded zone of the bearings as possible. The current placement of the accelerometers in the wind turbine drivetrain are often diametrically opposed to the loaded zone, and the performance of the vibration monitoring with respect to the commonly used signal analysis tools could thereby be increased. Further, wavelet-based signal analysis has been evaluated using historical wind turbine drivetrain field data. A new implementation of the wavelet packet transform to analyse enveloped vibration measurements in the frequency domain was developed, where the measurements were decomposed into packets matching the frequency resolution of the fast Fourier transform, and analysing the packet energy spectra. Finally, an anomaly detection solution utilizing an artificial neural network has been implemented to separate the condition indicator values, used for detection and diagnosis, from their inherent variance due to the dynamic turbine operation seen in the drivetrain rotational speed. The results in this thesis show the inadequacy of the commonly stored vibration measurements to the condition monitoring databases when used in post failure investigations and application of research developments on available field data. Measurements both taken over a long period of time and covering wide frequency range should be stored, instead of the either/or of today. Otherwise, the real-time monitoring of wind turbine drivetrain bearing failures cannot be replicated and monitoring improvements not fully evaluated. By implementing the wavelet packet transform, the detection and diagnosis performance was increased 250% compared to the fast Fourier transform, in an example of gearbox output shaft bearing failure. By implementing the anomaly detection by the artificial neural network, the performance increased further and was able to provide indications in a planet bearing failure case, which was not possible before. For turbine owners, these results provide both practical actions to take and provide an example of an easily implementable signal analysis tool to improve the detection and diagnosis of drivetrain bearing failures. The anomaly detection, which utilizes available historic data from healthy turbines and does not require any amount of labelled data for all considered types of bearing failures, also shows promise to detect failures in the drivetrain components which has been historically problematic. For the research community, the results mainly provides guidance into using historic field data when evaluating new developments. Also, they highlight potential pitfalls one can face using field data and what data properties to look for to successfully show the potential of your work.
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| 5. |
- Vrček, Aleks, 1991-
(author)
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Tribology of Rolling-Sliding Contacts under Mixed Lubrication : With focus on a Crankshaft Roller Bearing Application
- 2020
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Doctoral thesis (other academic/artistic)abstract
- A continuous increase in environmental legislation to reduce CO2 emissions is forcing engineers and scientists to develop more efficient and durable mechanical components, i.e. bearings, crankshafts, gears, etc. Such components are forced to operate under more severe operating conditions, reduced lubrication conditions, and under increased power density. The main failure mode has switched from traditionally subsurface to surface-initiated fatigue, typically caused by surface distress or micro-pitting. In this work, the tribology of rolling/sliding contacts, mimicking rolling bearing contact kinematics, operating under mixed lubrication conditions was studied. A special focus was directed to investigate the feasibility of employing such contacts in a crankshaft rolling bearing application by studying the tribological interface between a crankshaft and a roller. Three bearing steels were identified, selected, and then test specimens were manufactured from these steels for this work to represent a possible crankshaft rolling bearing steel for a four-cylinder light-duty internal combustion engine (ICE), as the current crankshaft steel does not meet the requirements to represent the rolling bearing component. Three tribological screening techniques were selected for this work to understand and investigate the surface performance, i.e. micro-pitting and wear damage of the rolling/sliding contact. The effect of the surface roughness and hardness, steel, and lubricant on the surface performance was investigated. This work was purely experimental, utilizing two tribological test devices: a twin-disc machine and a ball-on-disc machine. The results from this work will allow us to select and optimize the tribology of crankshaft rolling bearing contacts and bearing contacts in general to maximize the surface performance and in turn, contribute to increased efficiency and reliability of mechanical components. Based on the results from this Ph.D. work, micro-pitting and wear damage presents the main damage mode that can be associated with the engine tribology for a crankshaft rolling bearing. Micro-pitting and wear damage were found to be strongly dependent on the surface roughness and hardness combination of both contacting surfaces. A critical hardness difference was found between both surfaces, which is dependent on steel and heat treatments, where wear mode changes from severe fatigue wear to mild wear. A fully formulated state-of-the-art engine oil showed an increased wear component and, in some cases, eliminated micro-pitting damage compared to using only the base oil. Furthermore, it was shown that the ZDDP additive, present in the engine oil, can function as an anti-wear (AW) additive, as intended, or an extreme pressure (EP) additive depending on the contact iv severity. The latter increases the wear component through mild corrosion and delamination of the surface tribolayer, which leads to surface-initiated fatigue.
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