Relevant bibliographies by topics / Gearbox Condition Monitoring

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Gearbox Condition Monitoring'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Gearbox Condition Monitoring"

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Liao, G., S. Liu, T. Shi, and G. Zhang. "Gearbox condition monitoring using self-organizing feature maps." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 218, no. 1 (2004): 119–29. http://dx.doi.org/10.1243/095440604322786992.

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This paper proposes a novel technique for the condition monitoring of gearboxes based on a self-organizing feature maps (SOFM) network. In order to visualize the learned SOFM results more clearly, an improved method based on the unified distance matrix (U-matrix) method is presented, in which the overall topological information condensed into the map units is considered so as to project the high-dimensional input vectors into a two-dimensional space and give a better picture of their intrinsic structure than the original U-matrix method. The feature data extracted from industrial gearbox vibration signals measured under different operating conditions are analysed using the proposed technique. The results show that trained with the SOFM network and visualized with the improved method, the feature data are mapped into a two-dimensional space and formed clustering regions, each indicative of a specific gearbox condition. Therefore, the gearbox operating condition with a fatigue crack or broken tooth compared with the normal condition is identified clearly. Furthermore, with the trajectory of the image points for the feature data in two-dimensional space, the variation of gearbox conditions is observed visually, and the development of gearbox early-stage failures is monitored in time.
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Zhong, Xian You, Liang Cai Zeng, Chun Hua Zhao, Jin Zhang, and Shi Qing Wan. "Research of Condition Monitoring and Fault Diagnosis Techniques for Wind Turbine Gearbox." Applied Mechanics and Materials 197 (September 2012): 206–10. http://dx.doi.org/10.4028/www.scientific.net/amm.197.206.

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Wind power industry enormously expanded during the last several years. However, wind turbines are subjected to different sorts of failures, which lead to the increasement of the cost. The wind turbine gearbox is the most critical component in terms of high failure rates and long time to repair. This paper described common failures and root causes of wind turbine gearboxes. Then it focused on fault diagnosis and monitoring techniques for the wind turbine gearbox. The challenges and future research directions were presented, and the simulator rig of wind turbine gearbox was designed to develop condition monitoring and fault diagnosis techniques for wind turbine gearbox.
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Bligh, A. O., Noor A. Ahmed, and Y. Y. Zheng. "Design and Manufacture of a Planetary Gearbox Rig." Applied Mechanics and Materials 397-400 (September 2013): 176–88. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.176.

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Planetary gearboxes are required in many applications but have a high failure rate especially in wind turbines where rapid growth in turbine size is placing larger demands on the gearboxes. Current condition monitoring techniques are less effective at detecting faults in planetary gearboxes due to the complexity of the internal rotating components. A small scale planetary gearbox was, therefore, needed for testing on a gearbox test rig to develop a more effective method of condition monitoring. The detailed computer design and manufacture of the planetary gearbox for this purpose is detailed in this paper.
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Bartelmus, Walter. "New Focus on Gearbox Condition Monitoring for Failure Prevention Technology." Key Engineering Materials 588 (October 2013): 184–91. http://dx.doi.org/10.4028/www.scientific.net/kem.588.184.

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Condition monitoring is a tool for detection of faults and failure prevention. Fault andfailure are regarded as inevitable during the machine operation as the process of wear and theprocess of degradation. The question is, if one can influence the wear and degradation process,using condition monitoring. The paper will present technology which demonstrates that the use ofthe proper method can influence the wear and machine degradation process, using proper conditionmonitoring techniques and knowing scenarios of wear and degradation process. In the discussionpresented in the paper as a prerequisite has been taken that machinery works in severe dustyenvironment and varying operation conditions. It has been pointed that degradation process is notjust simply development of one fault. Most research for developing technology for conditionmonitoring is concentrated on one fault development. If one considers condition monitoring for acrack and brakeage of a tooth in gearbox, one should take in consideration that tooth crack andbrakeage is the result of several events, like rolling elements bearing frictional wear, which causesecondary misalignment of shaft and gears. The frictional wear is caused by dust particles whichget into oil from the environment in which a gearbox is operating. To avoid an influence ofcontaminated oil, contamination proactive technology should be used for the assessment of thedegree of contamination and the decision on oil purification or change should be taken. The wholeprocess connected with a gearbox condition change (wear and degradation process) shall bedescribed in the paper. The oil purification or its replacement extends the live of gearboxes butlong live of a gearbox, even with very little contamination causes some frictional wear of bearingsand finally secondary misalignment. To avoid further development of degradation process propertechnology should be used. There is a need to measure the degree of misalignment and makedecision on bearings replacement, in order to avoid further gearbox degradation, like teeth scuffingwhich may leads to crack initiation.
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Su, Hongsheng, Dantong Wang, and Xuping Duan. "Condition Maintenance Decision of Wind Turbine Gearbox Based on Stochastic Differential Equation." Energies 13, no. 17 (2020): 4480. http://dx.doi.org/10.3390/en13174480.

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Maintenance decision analysis is necessary to ensure the safe and stable operation of wind turbine equipment. To address gearboxes with a high failure rate in wind turbines, this paper establishes a new stochastic differential equation model of gearbox state transition to maximize the utilization of gearboxes. This model divides the state of the gearbox into two parts: internal degradation and external random interference. Weibull distribution and polynomial approximation were used to construct the internal degradation model of the gearbox. The external random interference is simulated by Brownian motion. On the basis of the analysis of monitoring data, the parameters of the gearbox state model were solved using the Newton–Raphson iterative method and entropy method. The state change of the gearbox was simulated in MATLAB, and the residual value between the predicted state and the real state was calculated. Compared with the state transformation model constructed by the traditional ordinary differential equation and the gamma distribution, the Weibull polynomial approximation stochastic model can better reflect the state of the device. With reliability set as the decision goal, the maintenance time of the gearbox is predicted, and the validity of the model is verified through case analysis.
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Schirrmacher, S., L. Overmeyer, and S. Lorisch. "Wireless condition monitoring of a marine gearbox." Ship Technology Research 63, no. 1 (2016): 38–49. http://dx.doi.org/10.1080/09377255.2015.1137427.

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Huang, Wei Guo, Zhong Kui Zhu, Cheng Li, and Peng Li. "A Novel Approach of Multiscale Feature Extraction for Gearbox Condition Monitoring." Applied Mechanics and Materials 160 (March 2012): 25–29. http://dx.doi.org/10.4028/www.scientific.net/amm.160.25.

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This paper proposes a novel multiscale slope feature extraction method using wavelet-based multiresolution anlaysis for gearboxes fault identification. The new method mainly includes the discrete wavelet transform (DWT), the variances calculation of multiscale detailed signals, and the wavelet-based multiscale slope features estimation. Experimental results show that the wavelet-based multiscale slope features show excellent clustering for different work conditions and have the merits of high accuracy and stability in classifying different conditions of gearbox.
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Su, Dai Zhong, and Wen Jie Peng. "Remote Machine Condition Monitoring Using Wireless Web Technology." Key Engineering Materials 419-420 (October 2009): 745–48. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.745.

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A remote real-time machine condition monitoring system is reported in this paper, which is applied for diagnosis and prognosis of gearboxes’ working condition. Within the system, the diagnostic classification is performed by pattern recognition using statistic parameters, and remote diagnostic capability is enhanced by applying Wireless Web technology. An online signal-processing scheme is adopted based on time-frequency analysis, digital filtering and statistic parameter algorithm to detect early fault signals of gears and to provide expert advice for decision making for maintenance. The effectiveness of the developed remote diagnostic system is verified via experimental investigation of monitoring a gearbox on a test rig under different conditions.
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Kang, Jian She, Xing Hui Zhang, Lei Xiao, and Xiu Ai Zhang. "Condition Monitoring System of Repaired Gearboxes of Wind Turbine." Applied Mechanics and Materials 556-562 (May 2014): 2970–73. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2970.

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Wind power becomes one of the most important cleaner energy in the world. The maintenance problem becomes the main factor preventing the appropriate power price which can be accepted by people. So, many researches contribute to improve the availability of wind farms. Traditionally, most researchers paid attention to the fault diagnosis, prognosis and maintenance strategies optimization to short the down time and the maintenance cost. But they all neglect the condition monitoring work of repaired gearbox of wind turbine before it is delivered to the customer from maintenance center to the wind farm. The life of repaired gearbox is also a very critical factor which influences the availability of wind farm. Good checking methods and good standard can provide a good support of good performance after the gearbox installed on the up tower. So, this paper proposed how to address these issues.
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Miao, Qiang, and Qinghua Zhou. "Planetary Gearbox Vibration Signal Characteristics Analysis and Fault Diagnosis." Shock and Vibration 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/126489.

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Planetary gearboxes are widely used in helicopters, wind turbines, mining machinery, and so forth. The structure and motion type of a planetary gearbox are more complex in comparison with a fixed-shaft one, which makes condition monitoring and fault diagnosis of planetary gearbox a challenging issue in practical applications. In order to understand the fundamental nature of planetary gearbox vibration, this paper conducts an investigation on vibration characteristics of a single-stage planetary gearbox. Assuming that the gearbox and the sensor revolve inversely at the speed of planet carrier, the problem can be transformed into two easier parts: research on fixed-shaft gearbox signal model and research on influence of sensor spinning. Based on this assumption, a vibration signal model of planetary gearbox is obtained. Experimental data are used to validate the model.
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Dissertations / Theses on the topic "Gearbox Condition Monitoring"

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Abdalla, Gaballa M. A. "Planetary gearbox condition monitoring based on modulation analysis." Thesis, University of Huddersfield, 2017. http://eprints.hud.ac.uk/id/eprint/32087/.

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The epicycle gearbox or planetary gearbox (PG) is a central power transmission systems of important machines such as helicopters and wind turbines which are mission critical and high cost systems. Condition monitoring (CM) has been explored extensively in recent years to avoid any unexpected interruptions and severe accidences caused by faults PGs. Although, considerable advancements in CM techniques, there still existed significant deficiency such as insensitivity, false diagnosis and high costs in implementing such techniques in industries. To improve CM techniques, therefore, this thesis focuses on an investigation of advanced signal analysis techniques such as higher order spectra (HOS) in order to achieve full characterisation of the nonlinear modulation processes of PG dynamics and thereby develop accurate diagnostic techniques. The lumped mass model is established for modelling the dynamic behaviour of the PG under investigation, which allows the vibration behaviours to be understood for analysing different abnormalities such as tooth breakages and gear errors. This paves the way for subsequent data analytics and fault diagnostics using modulation signal bispectrum (MSB) that allows the vibration data to be examined through HOS, but it is significantly efficient in characterising the multiple and nonlinear modulations of PG dynamics alongside superior noise reduction performance. Different degrees of misalignments in the PG drive system has been investigated and successfully diagnosed using MSB analysis of vibration measurements. Moreover, the investigation included detection of tooth breakage faults of different severities in both the sun and a planet gear. The tooth faults were diagnosed using the recently developed MSB through accurately representation and estimate of residual sidebands induced by these faults. Consequently, MSB analysis produces an accurate and reliable diagnosis in that it gives correct indication of the fault severity and location for wide operating conditions. Furthermore, these fault diagnosis practices allows the establishment of residual sideband analysis approach. These residual sidebands resulting from the out-of-phase superposition of vibration waves due to asymmetric, multiple meshing sources are much less influenced by gear errors than the in-phase sidebands due to faults or new occurrences of the symmetricity. MSB can provide an accurate characterisation of the residual sidebands and consequently produces consistent diagnosis as confirmed by both simulation and experiment.
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Hamilton, Andrew. "Development of novel gearbox lubrication condition monitoring sensors in the context of wind turbine gearboxes." Thesis, University of Strathclyde, 2015. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=25910.

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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.
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Stander, Cornelius Johannes. "Condition monitoring of gearboxes operating under fluctuating load conditions." Thesis, University of Pretoria, 2005. http://hdl.handle.net/2263/25604.

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Conventional gearbox vibration monitoring techniques are based on the assumption that changes in the measured structural response are caused by deterioration in the condition of the gears in the gearbox. However, this assumption is not valid under fluctuating load conditions, since the fluctuating load will amplitude modulate the measured vibration signal and cause the rotational speed of the system to change. In general monitoring of machines subject to fluctuating load conditions is dealt with by considering the constant load conditions on gearboxes or during free rotational tests. The need to monitor the condition of large gearboxes in mineral mining equipment has attracted greater interest in order to improve asset management. An inherent need for signal processing techniques, with the ability to indicate degradation in gear condition, under fluctuating load conditions exist. Such techniques should enable the online monitoring of gearboxes that operate under fluctuating load conditions. A continued flow of up to date information should consequently be available for asset and production management. With this research, a load demodulation normalisation procedure was developed to remove the modulation caused by fluctuating load conditions, which obscures the detection of an incipient gear fault conditions. A rotation domain averaging technique is implemented which combines the ability of computer order tracking and time domain averaging to suppress the spectral smearing effect caused by the fluctuation in speed, as well as to suppress the amplitude of the vibration which is not synchronous with the rotation of the gear shaft. It is demonstrated that the instantaneous angular speed of a gearbox shaft can be utilised to monitor the condition of the gear on the shaft. The instantaneous angular speed response measurement is less susceptible to phase distortion introduced by the transmission path when compared to conventional gearbox casing vibration measurements. A phase domain averaging approach was developed to overcome the phase distortion effect of the transmission path under fluctuating load conditions. The load demodulation normalisation and rotation domain averaging signal processing procedures were applied to both the conventional gearbox casing vibration and instantaneous angular speed measurements prior to the calculation of a smoothed pseudo Wigner-Ville distribution of the data. Statistical parameters such as the energy ratio were calculated from the distribution. These parameters could be monotonically trended under different load conditions to indicate the degradation of gear conditions.<br>Thesis (PhD (Mechanical Engineering))--University of Pretoria, 2005.<br>Mechanical and Aeronautical Engineering<br>unrestricted
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Al-Balushi, Khamis Rajab Nasser. "The use of high frequency stress waves for monitoring gears." Thesis, Cranfield University, 1995. http://dspace.lib.cranfield.ac.uk/handle/1826/10575.

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The aim of this research is to investigate the feasibility of using stress waves for condition monitoring of gears. The project involved setting up an experimental rig, carrying out experimental work, acquiring stress waves signatures, and processing the signals. It has been shown that stress waves can successfully be employed for early detection of incipient gear failure. A experimental gearbox was employed during the experiments. Miniature ultrasound transducers, both sensitive and sufficiently small, were manufactured and installed on the stationary outer race of the rolling element bearing of the gearbox to detect stress waves from the meshing gears. The stress waves signals from the transducers were digitised and digitally processed to extract relevant information. The signatures were high-pass filtered at a cut-off frequency of 200 kHz, thus representing exclusive ultrasonic frequencies. A new statistical parameter, Energy Index, was developed and performed on the stress wave signatures which were segmented to represent individual gear teeth. Along with this new parameter, the classical statistical parameters, (Peaks, RMS, Standard Deviation, Kurtosis, etc.) were also performed. Conclusive results are presented in graphical form in terms of Cumulative Energy Indices' and Energy Indices in polar form for individual gear teeth. A new algorithrn was also developed and presented for the envelope detection of signal by iterative peak detection. Although no direct comparison was made between condition monitoring of gears using stress waves and methods such as low frequency vibration analysis and wear debris analysis, it is apparent that stress waves monitoring offers a much earlier warning of incipient gear failure because the technique can detect material defonnations which are precursors to changes in the dynamic properties of gears and the occurrence of wear debris. The technique, therefore, can predict incipient failure much earlier, extending the lead-time before failure, and as a result, minimising sudden failures which may have catastrophic consequences.
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Danielson, Hugo, and Schmuck Benjamin von. "Robot Condition Monitoring : A first step in Condition Monitoring for robotic applications." Thesis, Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-66011.

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The industrial world is in constant demand for faster, cheaper and higher quality manufacturing. Robot utilisation and automation has evolved to become a necessary asset to master in order to stay competitive in the global market. With the growing dependency on robots, unexpected downtime and brakedowns can cause devastating loss of revenue. Consequently, this has lead to an increased importance for an accurate condition based way of performing robotic maintenance. As of writing, robots are predominantly maintained through time dependent maintenance. Part replacement is based on statistical models where maintenance is performed without taking the actual robot condition into consideration. As a result an overall level of uncertainty is ensued, where lacking the ability to properly diagnose the robot, also leads to superfluous repairs. Because of the costly impact this has on production, a condition based maintenance approach to robots would yield increased reliability at a lower cost of maintenance. This research focuses on trying to monitor vibrations in a robot, so as to infer about wear and to provide a first step in vibration based Robot Condition Monitoring. This research has been of multidisciplinary nature where robotics, tribology, mechanical component, signal analysis and diagnosis theory have overlapped in several areas throughout the project. The research has provided a vibration baseline and trends of the theoretical bearing defect frequencies for a hypocycloid gearbox installed on an ABB IRB6600 robot. The gearbox was not worn to a level that a severe gearbox degradation was irrefutably detectable and analysable. Accelerometers normally used on wind turbines were used for the project, and are believed to be sufficiently successful in capturing bearing related signals to accredit it for continued use at the preliminary stages of Robot Condition Monitoring development. A worn RV410F hypocycloid gearbox, was dismantled and analysed. Bearings found inside indicate high degrees of moisture corrosion and extensive surface wear. These findings had decisive roles in what future work recommendations where presented. Areas with great potential are condition monitoring through the use of Acoustic Emission and lubrication analysis. Further recommendations include investigating signal analysis techniques such as cepstrum pre-whitening and discrete wavelet transforms.
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Shen, Chia-Hsuan. "Acoustic Based Condition Monitoring." University of Akron / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=akron1341797408.

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Svensson, Gustav, and Mischa Huisman. "Concepts for a suitable condition based monitoring system for a planetary gearbox." Thesis, Linnéuniversitetet, Institutionen för maskinteknik (MT), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-74787.

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In the trends of technical improvements and automatization is it important for companies to keep up with the developments to be competitive on the market. SwePart Transmissions AB is a company that manufacture and develop gearboxes for the currently growing robot arms industry and the main task with this study is to investigate how to apply condition based monitoring on a new gearbox from the company. The work considers vibration analysis and testing new ideas in the oil analysis field. The tests that were performed are based on measuring the difference in impedance or magnetic field due to the increasement of wear. The results of the tests are not clear. This thesis is the beginning of a big project and therefore lies the value of this work in the new ideas and suggestions for further work.
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Paya, Basir Abdul. "Vibration condition monitoring and fault diagnostics of rotating machinery using artificial neural networks." Thesis, Brunel University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390220.

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Rzeszucinski, Pawel. "Development of reliable vibration-based condition indicators and their data fusion for the robust health diagnosis of gearboxes." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/development-of-reliable-vibrationbased-condition-indicators-and-their-data-fusion-for-the-robust-health-diagnosis-of-gearboxes(fa25db2f-89a5-420f-ba56-68ef7da874f9).html.

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Performing condition monitoring related tasks on any machinery is an essential element of their rational maintenance. Endeavours to detect an incipient fault within a system serve multiple purposes from increasing the safety of people responsible for operating the machines through decreasing the running and operational costs, allowing time to plan for the inevitable repairs and making sure that the downtime of the machine is kept to an absolute minimum. All these tasks gain extra importance in a case when machines are operated in dangerous conditions putting people's lives in potential jeopardy - for instance in the field of operating a helicopter. The robust assessment of the condition of gearboxes used by helicopters has recently been given an increased attention due to a number of accidents which followed an undetected drive train component failure. The majority of the on-board mounted condition monitoring systems use vibration response signals which are specifically processed to obtain a single number which is representative of a condition of a given monitored drive train component. Those signal processing methods are called Condition Indicators (CIs). There are a number of such CIs which are already in use and they seem to adequately indicate faults in most of the cases. However in a number of instances it has been observed that the most popular parameters like Crest Factor or FM4 failed to dependably reflect the true condition of the gear causing serious accidents, some of which resulted in a number of lives being lost. For this reason the presented research is focused on investigating the limitations of the existing CIs and designing a set of improved CIs. The development process is based on overcoming the drawbacks of thetechniques used in existing CIs combined with the intelligence gathered while analysing the acceleration vibration signals which contained a gear or a bearing fault. Five new CIs are proposed and the details of their design are documented. Both the existing and the proposed CIs are applied on the available, uncorrelated datasets. The results of the comparison show that the newly developed CIs are capable of indicating a gear or a bearing fault in a more robust and dependable fashion. Each proposed CI alone may not be the most robust indicator of the actual condition of the monitored component hence the output from all proposed CIs is combined into a single indication through use of a novel data fusion model. The Combined CI created based on the data fusion model is observed to be more robust compared to each CI alone, hence it may increase the confidence level of the decision making routine and is expected to decrease the number of false alarms. The methods of the existing CIs, the proposed CIs and the data fusion techniques as well as the results of the comparison between the different approaches are present in this thesis.
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Wang, Wei Ji. "Gearbox condition monitoring and early damage diagnosis by two and three dimensional vibration signal analysis." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334237.

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Book chapters on the topic "Gearbox Condition Monitoring"

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Bartelmus, Walter, and Radoslaw Zimroz. "Gearbox Condition Monitoring Procedures." In Lecture Notes in Mechanical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39348-8_21.

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Dewangan, Palash, Dada Saheb Ramteke, and Anand Parey. "Model Based Fault Diagnosis in Bevel Gearbox." In Applied Condition Monitoring. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79519-1_7.

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Hmida, Atef, Ahmed Hammami, Fakher Chaari, Mohamed Taoufik Khabou, and Mohamed Haddar. "Modal Analysis of Spur Gearbox with an Elastic Coupling." In Applied Condition Monitoring. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41459-1_15.

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Jabłoński, Adam. "A Practical Recipe for Solving Difficult Epicyclic Gearbox Trains." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62042-8_9.

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Graja, Oussama, Bacem Zghal, Kajetan Dziedziech, et al. "Modeling the Transmission Path Effect in a Planetary Gearbox." In Applied Condition Monitoring. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96181-1_11.

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Graja, Oussama, Bacem Zghal, Kajetan Dziedziech, et al. "On the Research of Extra Characteristic Frequencies in a Planetary Gearbox." In Applied Condition Monitoring. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94616-0_7.

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Ziani, Ridha, Hafida Mahgoun, Semcheddine Fedala, and Ahmed Felkaoui. "Feature Selection Scheme Based on Pareto Method for Gearbox Fault Diagnosis." In Applied Condition Monitoring. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96181-1_1.

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Obuchowski, Jakub, Agnieszka Wylomanska, and Radoslaw Zimroz. "New Criteria for Adaptive Blind Deconvolution of Vibration Signals from Planetary Gearbox." In Applied Condition Monitoring. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20463-5_9.

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Barszcz, Tomasz, Rafał Gawarkiewicz, Adam Jabłoński, Michał Sękal, and Michał Wasilczuk. "Knocking Sounds in the Wind Turbine Gearbox During Slowing Down—Case Study." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61927-9_22.

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Hmida, Atef, Ahmed Hammami, Mohamed Taoufik Khabou, Fakher Chaari, and Mohamed Haddar. "Dynamic Behavior of Spur Gearbox with an Elastic Coupling Under Acyclism Regime." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61927-9_30.

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Conference papers on the topic "Gearbox Condition Monitoring"

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Luo, Huageng, Mingqi Luo, and Shaobo Zhang. "Synchronous Analysis in Wind Turbine Gearbox Condition Monitoring." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-69161.

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This paper summarizes the application of “synchronous sampling” technique in the digital domain to enhance the signal-to-noise ratio in detection of wind turbine gear and bearing damage signatures. In this technique, only a once-per-revolution Key Phaser or a low-count Encoder signal is required. High-count synchronous sampling is achieved by resampling in the digital domain. Implementation and demonstration of the aforementioned techniques are illustrated in two current wind turbine field operation examples. The improvement in the accuracy and precision of damage feature extractions is demonstrated.
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Mustafa, Albara, Tore Markeset, and Abbas Barabadi. "Wind Turbine Failures Review and Gearbox Condition Monitoring." In Proceedings of the 29th European Safety and Reliability Conference (ESREL). Research Publishing Services, 2020. http://dx.doi.org/10.3850/978-981-14-8593-0_5784-cd.

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Shulian Yang, Wenhai Li, and Canlin Wang. "The intelligent fault diagnosis of wind turbine gearbox based on artificial neural network." In 2008 International Conference on Condition Monitoring and Diagnosis. IEEE, 2008. http://dx.doi.org/10.1109/cmd.2008.4580221.

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Miao, Qiang, Dong Wang, Hong-Zhong Huang, Bin Zheng, and Xianfeng Fan. "Gearbox On-Line Condition Monitoring Using Empirical Mode Decomposition." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86324.

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As a flexible maintenance strategy, Condition Based Maintenance (CBM) has been accepted by industry due to its efficiency and robustness in many engineering practices. Successful implementation of CBM relies on observation of actual health condition of machinery. Therefore, it is crucial to perform condition monitoring in CBM. This paper focuses on quantifying health condition of machinery. Empirical Mode Decomposition (EMD) is employed to decompose signal and extract dominant signatures, which could reflect health condition variation of machinery. Then, a novel index called Health Index (HI) is proposed to describe condition development trends. In order to detect occurrence of early faults, a dynamic threshold is also proposed. In case occurrence of early fault, HI should be higher than its corresponding threshold. This novel condition monitoring method is more appropriate for on-line health monitoring and detection of incipient fault. Two sets of data collected from gearboxes are used to validate the proposed method. The analysis results show that the proposed method is effective in condition monitoring, especially the detection of early faults.
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Wang, Dong, Qiang Miao, Chengdong Wang, and Jingqi Xiong. "Automatic Condition Monitoring and Early Fault Detection of Gearbox." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86469.

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Condition based maintenance (CBM) improves decision-making performances for a maintenance program through machinery condition monitoring. Therefore, it is a key step to trace machinery health condition for CBM. In this paper, a novel method is proposed to establish a health evaluation index named automatic evaluation index (AEI) and its corresponding dynamic threshold using Wavelet Packet Transform (WPT) and Hidden Markolv Model (HMM). In this process, WPT is used to decompose signal into detail signals and exhibits prominent gear fault features. In addition, HMM employed here is to recognize two concerned states of gear in the whole life validation, including normal gear state and early gear fault state. It is also important to build a dynamic threshold to differentiate the two states automatically. The proposed dynamic threshold not only renews by itself according to the history values of AEI but also easily and automatically detects occurrence of gear early fault. Finally, a set of whole life time data ending in gear failure is used to verify the proposed method effectively. Further, some related parameters included in this method are discussed and the obtained results show that condition monitoring performance of the proposed method is excellent in detection of gear failure.
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Singh, KaranVir, Hasmat Malik, and Rajneesh Sharma. "Condition monitoring of wind turbine gearbox using electrical signatures." In 2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS). IEEE, 2017. http://dx.doi.org/10.1109/icmdcs.2017.8211718.

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Yang, Lu, Lei Xie, Jie Wang, Dong Wang, and Qiang Miao. "Current Progress on Wind Turbine Gearbox Condition Monitoring and Health Evaluation." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-63945.

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As a type of clean and renewable energy source, wind power is growing fast as more and more countries lay emphasis on it. At the end of 2011, the global wind energy capacity reached 238 GW, with a cumulative growth of more than 20% per year, which is certainly a respectable figure for any industry. There is an exigent need to reduce the costs of operating and maintaining wind turbines while they became one of the fastest growing sources of power production in the world today. Gearbox is a critical component in the transmission system of wind turbine generator. Wind turbine gearbox operates in the extreme conditions of heavy duty, low speed and non-stationary load and speed, etc., which makes it one of the components that have high failure rate. To detect the fault of gearbox, many methods have been developed, including vibration analysis, acoustic emission, oil analysis, temperature monitoring, and performance monitoring and so on. Vibration analysis is widely used in fault diagnosis process and many efforts have been made in this area. However, there are many challenging problems in detecting the failure of wind turbine gearbox. The gearbox transforms low-speed revolutions from the rotor to high-speed revolutions, for example, from 20 rpm to 1500 rpm or higher. Usually one or more planetary gear stages are adopted in a gearbox design because the load can be shared by several planet gears and the transmission ratio can get higher. One disadvantage with the planetary gear stage is that a more complex design makes the detection and specification of gearbox failure difficult. The existing fault diagnosis theory and technology for fixed-shaft gearbox cannot solve the issues in the fault diagnosis of planetary gearbox. The planetary stage of wind turbine gearbox consists of sun gear, ring gear and several planet gears. The planet gears not only rotate around their own centers but also revolve around the sun gear center, and the distance between each planet gear to the sensor varies all the time. This adds complexity to vibration signals and results in difficulty in finding the fault-related features. The paths through which the vibration propagates from its origin to the sensors are complex, and the gears of other stage vibrate at the same time. This makes fault features be buried in noises. Further, the extreme conditions of heavy duty, low speed, and non-stationary workload lead to evidently non-stationary phenomena in the collected vibration. Methods to assess fault severity of a gearbox should be developed so as to realize fault prognosis and estimate of the remaining useful life of gearbox. Finally, other issues like signal analysis based on multi-sensor data fusion are also considered. This paper gives a comprehensive investigation on the state-of-the-art development in the wind turbine gearbox condition monitoring and health evaluation. The general situation of wind energy industry is discussed, and the research progresses in each aspects of wind turbine gearbox are reviewed. The existing problems in the current research are summarized in the end.
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Xie, Yuan, Gao Zhifei, Tang Ke, Zeng Mingjie, and Wang Yonghai. "Wind turbine gearbox condition monitoring system based on vibration signal." In 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI). IEEE, 2015. http://dx.doi.org/10.1109/icemi.2015.7494242.

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Wang, Yuechen, Zhiliang Zhu, Hang Song, and Kai Shi. "Wind turbine gearbox condition monitoring based on extreme gradient boosting." In IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2017. http://dx.doi.org/10.1109/iecon.2017.8217044.

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Zhao, Hongshan, Huihai Liu, Hui Ren, and Hongyang Liu. "The condition monitoring of wind turbine gearbox based on cointegration." In 2016 IEEE International Conference on Power System Technology (POWERCON). IEEE, 2016. http://dx.doi.org/10.1109/powercon.2016.7753906.

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Reports on the topic "Gearbox Condition Monitoring"

1

Sheng, Shuangwen. Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1048981.

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