Author: Sharad JainPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Skidding and Fault Detection in the Bearings of Wind-turbine Gearboxes
Author: Sharad JainCharacterization of the High-Speed-Stage Bearing Skidding of Wind Turbine Gearboxes Induced by Dynamic Electricity Grid Events
Author: Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Bearing behavior is an important factor for wind turbine drivetrain reliability. Extreme loads and dynamic excitations pose challenges to the bearing design and therefore its performance. Excessive skidding of the bearing rollers should be avoided because it can cause scuffing failures. Excitations coming from wind and the electricity grid can subject the drivetrain to fluctuating torque and nontorque loads. Wind-induced excitations have been investigated predominantly in literature. However, modern wind turbines are subjected more and more to grid-induced loads because of stricter electricity grid regulations. For example, during fault-ride-through events, turbines are required to stay connected for a longer period of time during the grid failure. This work investigates the influence of electrically induced excitations on the skidding behaviour of the tapered roller bearings on the high-speed stage of a wind turbine gearbox. This skidding behaviour during dynamic events is described as a potential bearing failure initiator by many researchers; however, only limited full-scale dynamic testing is documented. Therefore, a dedicated gridloss-type event is defined in the paper and conducted in a dynamometer test on a full-scale wind turbine nacelle. During the event, a complete electricity grid failure is simulated while the turbine is at rated speed and predefined torque levels. Particular focus is on the characterization of the high-speed shaft tapered roller bearing slip behavior. Strain-gauge bridges in grooves along the circumference of the outer ring are used to characterize the bearing load zone in detail. It is shown that during the torque reversals of the transient event, roller slip can be induced. This indicates the potential of the applied load case to go beyond the preload of the tapered roller bearing. Furthermore, the relation between the applied torque and skidding level is studied.
Intelligent Fault Diagnosis of Gearboxes and Its Applications on Wind Turbines
Author: Sajid HussainBearing Fault Detection on Wind Turbine Gearbox Vibrations Using Generalized Likelihood Ratio-Based Indicators: Preprint
Author: Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Studies in condition monitoring literature often aim to detect rolling element bearing faults because they have one of the biggest shares among defects in turbo machinery. Accordingly, several prognosis and diagnosis methods have been devised to identify fault signatures from vibration signals. The underlying idea behind traditional indicators often revolves around tracking both cyclostationarity and abnormal impulses in the vibration signals without distinguishing the two. A recently proposed method to capture the rolling element bearing degradation lays out the groundwork for new indicator families utilizing generalized likelihood ratio test. This novel approach exploits the cyclostationarity and the impulsiveness of vibration signals independently in order to estimate the most suitable indicators for a given fault. However, the method has yet to be tested on complex experimental vibration signals such as those of a wind turbine gearbox. In this study, the approach is applied to the NREL Wind Turbine Gearbox Condition Monitoring Round Robin Study data set for bearing fault detection purposes. The data set is measured on an experimental test rig of a wind turbine gearbox, hence the complexity of the vibration signals is similar to a real case. Furthermore, the new indicators are also tested with signals that carry multiple fault signatures. The outcome demonstrates that the proposed method is capable of distinguishing between healthy and damaged vibration signals measured on a complex wind turbine gearbox.
Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-learning Models
Author: Lindy WilliamsPublisher:
ISBN:
Category : Structural analysis (Engineering)
Languages : en
Pages : 0
Book Description
Development of Effective Gearbox Fault Diagnosis Methodologies Utilising Various Levels of Prior Knowledge
Author: Stephan SchmidtPublisher:
ISBN:
Category : Electric fault location
Languages : en
Pages : 0
Book Description
Effective fault diagnosis techniques are important to ensure that expensive assets such as wind turbines can operate reliably. Vibration condition monitoring data are rich with information pertaining to the dynamics of the rotating machines and are therefore popular for rotating machine diagnostics. However, vibration data do not only contain diagnostic information, but operating condition information as well. The performance of many conventional fault diagnosis techniques is impeded by inherent varying operating conditions encountered in machines such as wind turbines and draglines. Hence, it is not only important to utilise fault diagnosis techniques that are sensitive to faults, but the techniques should also be robust to changes in operating conditions. Much research has been conducted to address the many facets of gearbox fault diagnosis e.g. understanding the interactions of the components, the characteristics of the vibration signals and the development of good vibration analysis techniques. The aforementioned knowledge, as well as the availability of historical data, are regarded as prior knowledge (i.e. information that is available before inferring the condition of the machine) in this thesis. The available prior knowledge can be utilised to ensure that e ective gearbox fault diagnosis techniques are designed. Therefore, methodologies are proposed in this work which can utilise the available prior knowledge to e ectively perform fault diagnosis, i.e. detection, localisation and trending, under varying operating conditions. It is necessary to design di erent methodologies to accommodate the di erent kinds of historical data (e.g. healthy historical data or historical fault data) that can be encountered and the di erent signal analysis techniques that can be used. More speci cally, a methodology is developed to automatically detect localised gear damage under varying operating conditions without any historical data being available. The success of the methodology is attributed to the fact that the interaction between gear teeth in a similar condition results in data being generated which are statistically similar and this prior knowledge may be utilised. Therefore, a dissimilarity measure between the probability density functions of two teeth can be used to detect a gear tooth with localised gear damage. Three methodologies are also developed to utilise the available historical data from a healthy machine for gearbox fault diagnosis. Firstly, discrepancy analysis, a powerful novelty detection technique which has been used for gear diagnostics under varying operating conditions, is extended for bearing diagnostics under varying operating conditions. The suitability of time-frequency analysis techniques and di erent models are compared for discrepancy analysis as well. Secondly, a methodology is developed where the spectral coherence, a powerful second-order cyclostationary technique, is supplemented with healthy historical data for fault detection, localisation and trending. Lastly, a methodology is proposed which utilises narrowband feature extraction methods such as the kurtogram to extract a signal rich with novel information from a vibration signal. This is performed by attenuating the historical information in the signal. Sophisticated signal analysis techniques such as the squared envelope spectrum and the spectral coherence are also used on the novel signal to highlight the bene ts of utilising the novel signal as opposed to raw vibration signal for fault diagnosis. Even though a healthy state is the desired operating condition of rotating machines, fault data will become available during the operational life of the machine. Therefore, a methodology, centred around discrepancy analysis, is developed to utilise the available historical fault data and to accommodate fault data becoming available during the operation of the machine. In this investigation, it is recognised that the machine condition monitoring problem is in fact an open set recognition problem with continuous transitions between the healthy machine condition and the failure conditions. This is explicitly incorporated into the methodology and used to infer the condition of the gearbox in an open set recognition framework. This methodology uses a di erent approach to the conventional supervised machine learning techniques found in the literature. The methodologies are investigated on numerical and experimental datasets generated under varying operating conditions. The results indicate the bene ts of incorporating prior knowledge into the fault diagnosis process: the fault diagnosis techniques can be more robust to varying operating conditions, more sensitive to damage and easier to interpret by a non-expert. In summary, fault diagnosis techniques are more e ective when prior knowledge is utilised.
Dynamics and Vibration Analyses of Gearbox in Wind Turbine
Author: Qingkai HanPublisher: Springer
ISBN: 9811027471
Category : Technology & Engineering
Languages : en
Pages : 168
Book Description
This book explores the dynamics and vibration properties of gearboxes, with a focus on geared rotor systems. It discusses mechanical theories, finite-element based simulations, experimental measurements and vibration signal processing techniques. It introduces the vibration-resonance calculation method for the geared rotor system in wind turbines and load sharing of the planetary gear train, and offers a method for calculating the vibrations of geared rotor systems under either internal excitations from gear sets or external loads transferred from wind loads. It also defines and elaborates on parameter optimization for planetary gear systems based on the torsional dynamics of wind-turbine geared rotor systems. Moreover, it describes experimental measurements of vibrations on the wind-turbine gearbox performed on the test rig and on site, and analyzes the vibration signals of different testing points, showing them in both time and frequency domains. Lastly, it lists the gear coupling frequencies and fault characteristic frequencies from the vibrations of the gearbox housing. The technologies and results presented are valuable resources for use in dynamic design, vibration prediction and analysis of gearboxes and geared rotor systems in wind turbines as well as many other machines.
Analysis of the Premature Failure of Wind Turbine Gearbox Bearings
Author: Thomas BruceAn Intelligent System for Fault Diagnosis in Gearboxes
Author: Jital Dwarkesh ShahPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Gearboxes are commonly used in rotating machinery for power transmission. A gearbox consists of shafts, gears, and bearings, each component having specific mechanical dynamics and fault properties. Reliable gearbox fault detection and health monitoring techniques are critically needed in industries for more efficient predictive maintenance applications. The objective of this work is to develop a new technology for health monitoring of gearboxes. Firstly, a new wavelet analysis method is technique for analysis of gear faults in a gearbox with demodulation from other rotating components such as shaft and bearings. Secondly, a mode decomposition technique is proposed to highlight bearing fault features in a gearbox. Thirdly, a new evolving neuro-fuzzy (eNF) classifier is developed to integrate the merits of different fault detection techniques for real-time health condition monitoring of gear systems. The effectiveness of the proposed techniques is verified by simulation and experimental tests.
Author: Ian Martinez