A CapsNet-Based Fault Diagnosis Method for a Digital Twin of a Wind Turbine Gearbox

2021 ◽  
Author(s):  
Hao Zhao ◽  
Weifei Hu ◽  
Zhenyu Liu ◽  
Jianrong Tan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
High Speed ◽  
Planetary Gear ◽  
Monitoring Data ◽  
Wind Turbine Gearbox ◽  
Digital Twin ◽  
Bearing Damage

Abstract Accurate fault diagnosis of complex energy systems, such as wind turbines, is essential to avoid catastrophic accidents and ensure a stable power source. However, accurate fault diagnoses under dynamic operating conditions and various failure mechanisms are major challenges for wind turbines nowadays. Here we present a CapsNet-based deep learning scheme for data-driven fault diagnosis used in a digital twin of a wind turbine gearbox. The CapsNet model can extract the multi-dimensional features and rich spatial information from the gearbox monitoring data by an artificial neural network named the CapsNet. Through the dynamic routing algorithm between capsules, the network structure and parameters of the CapsNet model can be adjusted effectively to realize an accurate and robust classification of the operational conditions of a wind turbine gearbox, including front box stuck (single fault) and high-speed shaft bearing damage & planetary gear damage (coupling faults). Two gearbox datasets are used to verify the performance of the CapsNet model. The experimental results show that the accuracy of this proposed method is up to 98%, which proves the accuracy of CapsNet model in the case study when this model performed three-state classification (health, stuck, and coupled damage). Compared with state-of-the-art fault diagnosis methods reported in the literature, the CapsNet model has a competitive advantage, especially in the ability to diagnose coupling faults, high-speed shaft bearing damage & planetary gear damage in our case study. CapsNet has at least 2.4 percentage points higher than any other measure in our experiment. In addition, the proposed method can automatically extract features from the original monitoring data, and do not rely on expert experience or signal processing related knowledge, which provides a new avenue for constructing an accurate and efficient digital twin of wind turbine gearboxes.

Fault Diagnosis of Wind Turbines Based on Improved Neural Network

2014 ◽  
Vol 978 ◽  
pp. 78-83
Author(s):  
Qiang Lan ◽  
Peng Da Zhao ◽  
Man Li Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Feature Vector ◽  
Wind Turbine Gearbox ◽  
Vibration Sensors

The gearbox is an important module of wind turbine. In order to diagnosis the fault of wind turbine gearbox, a method based on the improved neural network is proposed. According to the characteristics of the wind turbine gearbox, several vibration sensors are set in the gearbox, so as to acquire the feature vector of gearbox. After training, the improved neural network is verified with some test samples. The result proved that the method is suitable for fault diagnosis in gearbox of wind turbine.. Keywords: wind turbine gearbox, fault diagnosis, particle swam, neural network.

Efficient Transient Analysis of the High-Speed Stage of a Wind Turbine Gearbox by Advanced Model Reduction Techniques and Memory-Effective Discretization

2015 ◽  
Author(s):  
Tommaso Tamarozzi ◽  
Bart Blockmans ◽  
Wim Desmet
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
High Speed ◽  
Dynamic Effects ◽  
Wind Turbine Gearbox ◽  
Premature Failure ◽  
Efficient Simulation ◽  
Model Complex ◽  
Non Linear ◽  
Flexible Multibody

Modern wind turbines are designed to cope with their increased size and capacity. One of the most expensive components of these machines is the gearbox. Its design is more complex than a mere upscaling exercise from predecessors. The stress levels experienced by the different gear stages, the dynamic effects induced by their size and the unparalleled loads transmitted are some of the challenges that design engineers face. Moreover, unexpected events that load the wind turbines such as voltage dips, wind gusts or emergency breaking are expected to be major contributors to the premature failure of these gearboxes. The lack of engineering experience at this scale calls for accurate and efficient simulation tools thereby enabling reliable gearbox design. Standard lumped-parameters models or rigid multibody approaches do not provide a sufficient level of details to study the dynamic effects induced by e.g. gear design modifications (micro-geometry) or to analyze local stress concentrations. More advanced numerical tools are available such as flexible multibody or non-linear FE and allow to model complex contact interactions including all the relevant dynamic effects. Unfortunately the level of mesh refinement needed for an accurate analysis causes these simulations to be computationally expensive with time scales of several weeks to perform a single full rotation of a gear pair. This work introduces a novel efficient simulation tool for dynamic analysis of transmissions. This tool adopts a flexible multibody paradigm but incorporates several advanced features that allows to run simulations up to two orders of magnitudes faster as compared to non-linear FE with the same level of accuracy. A unique non-linear parametric model order reduction technique is used to develop a simulation strategy that is quasi mesh-independent allowing the usage of very fine FE meshes. Finally, in order to limit the memory consumption, a technique is developed to be able to finely mesh only a few of the gears teeth while the remaining gears are coarsely meshed. The main novelty of this approach lies in the possibility to perform full gear rotations without losing spatial resolution as compared to a finely meshed gear. After an accuracy check performed with a sample pair of helical gears, the framework is used to simulate the high speed stage of a three-stage wind turbine gearbox. The combined efficiency and accuracy of the approach is demonstrated by performing a dynamic stress analysis of the high-speed stage with and without a tip-relief modification. Accuracy of the results, simulation time, and memory usage are assessed.

scholarly journals Comparative case studies on ring gear fault diagnosis of planetary gearboxes using vibrations and acoustic emissions

2021 ◽  
Author(s):  
Félix Leaman ◽  
Ralph Baltes ◽  
Elisabeth Clausen
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Case Studies ◽  
Vibration Analysis ◽  
Acoustic Emissions ◽  
Wind Turbine Gearbox ◽  
Planetary Gearbox ◽  
Vibration Sensors ◽  
Comparative Case Studies

AbstractThe analysis of vibrations and acoustic emissions (AE) are two recognized non-destructive techniques used for machine fault diagnosis. In recent years, the two techniques have been comparatively evaluated by different researchers with experimental tests. Several evaluations have shown that the AE analysis has a higher potential than the vibration analysis for fault diagnosis of mechanical components for certain cases. However, the distance between the AE sensor and the fault is an important factor that can considerably decrease the potential to detect damage and that has not been sufficiently investigated. Moreover, the comparisons have not yet addressed conditions of slow speed that for example are usual for wind turbine gearboxes. Therefore, in this paper we present two comparative case studies that address both topics. Both case studies consider planetary gearboxes with faults in their ring gears. The first case study corresponds to a small planetary gearbox in which the AE and vibration sensors were installed together at two different positions. The second case study corresponds to a full-size wind turbine gearbox in which three pairs of AE and vibration sensors were installed on the outside of the ring gear from a low-speed planetary stage. The results of the evaluations demonstrate the important influence of the distance between sensors and fault. Despite this, the good results from the AE analysis indicate that this technique should be considered as an important complement to the traditional vibration analysis. The main contribution of this paper is comparing AE and vibration analysis by using not only experimental data from a small planetary gearbox but also from a full-size wind turbine gearbox. The comparison addresses the topics of proximity of the sensor to the fault and low-speed conditions.

Detailed Modeling of Wind Turbine Gear Set by General-Purpose Multibody Dynamics

2014 ◽  
Author(s):  
Eduardo Paiva Okabe ◽  
Pierangelo Masarati
Keyword(s):  
Wind Turbine ◽  
Multibody Dynamics ◽  
Wind Turbines ◽  
Contact Point ◽  
Kinematic Model ◽  
Planetary Gear ◽  
Spur Gear ◽  
Spur Gears ◽  
Wind Turbine Gearbox ◽  
Hydrodynamic Bearing

This work presents the development of a kinematic model of a spur gear pair and the implementation of a hydrodynamic bearing in a multidisciplinary multibody dynamics software. Both models are employed to simulate the behavior of a planetary gear set typically adopted in wind turbines. Geared transmissions have been a popular choice to transmit the rotation of the main rotor to the electrical generator in this type of turbine. Compared to other kinds of transmission, a gearbox is more compact, robust and require low maintenance over its lifetime, which is interesting, since these turbines are usually installed in remote places. The gearbox of a wind turbine is normally composed by a set of spur gears and bearings, assembled in arrangement known as epicyclic. Spur gears generally have an involute profile, which allows a constant transmission of the angular speed. This kinematic constraint between gears is defined by the angle that the surface of their teeth is in contact with. This angle is known as pressure angle and, by design, it should remain constant during operation. However, a variation of the distance between gears changes this angle, which also changes the direction of the transmission of the movement. To account for this effect, the joint is described by the projection of the absolute velocity of the contact point of each gear on the line of action, which is calculated from their position. Another important group of elements are the bearings that support gear and shafts. They can absorb part of the vibration, and compensate misalignments and teeth surface failures. Hydrodynamic bearings are widely employed in turbomachinery, due to their simplicity, long life and good damping properties, which are features that wind turbines can benefit from. Most of the hydrodynamic bearing models are two dimensional, so they have to be adapted to be implemented in a multibody dynamics software. The development of these modifications is also described in this work, so any other hydrodynamic bearing model can be easily adapted using the same procedure. Finally, a model of the wind turbine gearbox is presented, and some of the features of using the aforementioned elements inside a multibody dynamics software can be highlighted.

Current Progress on Wind Turbine Gearbox Condition Monitoring and Health Evaluation

2013 ◽  
Author(s):  
Lu Yang ◽  
Lei Xie ◽  
Jie Wang ◽  
Dong Wang ◽  
Qiang Miao
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Vibration Analysis ◽  
Planetary Gear ◽  
Health Evaluation ◽  
Extreme Conditions ◽  
Heavy Duty ◽  
Wind Turbine Gearbox ◽  
Low Speed

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.

Quantitative Analysis of Fault of Wind Turbines Based on Hilbert Space Feature Entropy

2014 ◽  
Vol 971-973 ◽  
pp. 1292-1295
Author(s):  
Xian Ming Sun ◽  
Chang Zheng Chen ◽  
Hao Zhou
Keyword(s):  
Hilbert Space ◽  
Quantitative Analysis ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Information Entropy ◽  
Entropy Theory ◽  
Wind Turbine Gearbox

Aim at the problem of wind turbine gearbox fault diagnosis , a method of Hilbert space feature entropy is proposed. Using the information entropy theory, the faults information obtained in the HHT spectrum quantify to the value of Hilbert space feature entropy, thus achieving quantitative analysis of the failure of the wind turbine gearbox. The method was applied to fault diagnosis of the wind turbine gearboxes, which proved that the method is efficacious.

scholarly journals Dynamic model of a cylindrical roller on a rough surface, with applications to wind turbine gearbox planetary bearings

2019 ◽  
Vol 233 (10) ◽  
pp. 1424-1432
Author(s):  
W James McBride ◽  
Hugh EM Hunt
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
High Speed ◽  
Random Vibration ◽  
Wind Turbine Gearbox ◽  
Bearing Faults ◽  
Power Spectral ◽  
Bearing Race ◽  
Distributed Faults ◽  
Large Wind

Wind turbines of larger power ratings have become increasingly prevalent in recent years, improving the viability of wind energy as a sustainable energy source. However, these large wind turbines have been subjected to higher rates of failure of the wind turbine gearbox, resulting in larger downtime of operation and an increase in cost due to repairs. These failures most frequently initiate in the gearbox’s bearings, especially in the planetary bearings of the planetary stage and high-speed bearings. Currently, most of the research on the detection of planetary bearing faults only addresses the case of localised faults in the outer bearing race, while fewer research considering the detection of distributed bearing faults. The research that does consider distributed bearing faults relies on techniques – such as machine learning for the identification of faulty bearings – that do not account much for the underlying physics of the bearing. In this paper, a model is developed to simulate and analyse the dynamic interaction of a planetary bearing in the presence of surface roughness, which can be used to represent a distributed fault. The model presented uses random vibration theory for simulating the response of the planet bearing induced by distributed faults. The input of the model considers statistical expressions of the roughness geometry using multiple parallel tracks. Numerical simulation of the random vibration of the model is performed using 16 tracks, and the power spectral density of the radial deflection of the roller and the roller–race contact force is determined. The results of the simulation with the multi-track model show that a single-track model significantly overestimates the power spectral densities, and also suggests the stiffness of the bearing race is too high to have an effect on the roller dynamics for a planet bearing.

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

2019 ◽  
Vol 12 (3) ◽  
pp. 277-283
Author(s):  
Jiatang Cheng ◽  
Yan Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Wind Turbine Gearbox ◽  
Bp Network ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

scholarly journals Impact of Typhoons on Floating Offshore Wind Turbines: A Case Study of Typhoon Mangkhut

2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Jiawen Li ◽  
Jingyu Bian ◽  
Yuxiang Ma ◽  
Yichen Jiang
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Safety Evaluation ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Motion Response ◽  
Offshore Wind Turbines ◽  
Floating Offshore Wind Turbines ◽  
Coupling Motion

A typhoon is a restrictive factor in the development of floating wind power in China. However, the influences of multistage typhoon wind and waves on offshore wind turbines have not yet been studied. Based on Typhoon Mangkhut, in this study, the characteristics of the motion response and structural loads of an offshore wind turbine are investigated during the travel process. For this purpose, a framework is established and verified for investigating the typhoon-induced effects of offshore wind turbines, including a multistage typhoon wave field and a coupled dynamic model of offshore wind turbines. On this basis, the motion response and structural loads of different stages are calculated and analyzed systematically. The results show that the maximum response does not exactly correspond to the maximum wave or wind stage. Considering only the maximum wave height or wind speed may underestimate the motion response during the traveling process of the typhoon, which has problems in guiding the anti-typhoon design of offshore wind turbines. In addition, the coupling motion between the floating foundation and turbine should be considered in the safety evaluation of the floating offshore wind turbine under typhoon conditions.

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