Drivetrain Dynamics Research Articles

Active Torque Control for Speed Ripple Elimination: A Mechanical Perspective

Torque fluctuations in drivetrains are the result of dynamic excitations and can be unfavorable for the lifetime of the system. Passive ripple suppression methods exist, such as torsional dampers and flywheels, which are often bulky and not always desired. Alternatively, performant active control methods exist; however, their applicability to certain drivetrains is not covered. Therefore, this paper focuses on active control from a mechanical perspective, more specifically, drivetrain dynamics impacting active control effectiveness. A quasi-resonant controller is implemented as an active control method, and its performance and robustness are proven both in simulation on a 3-DOF mechanical model and experimentally at different excitation frequencies. The tests show that active control effectiveness is highly drivetrain-dependent. In particular, the propagation of the torque oscillation is influenced by the elastic filtering properties of the drivetrain, and the speed ripple depends on the inertial attenuation of the drivetrain. High-stiffness, low-inertia drivetrains benefit best from active control for ripple suppression because the inertial attenuation is limited, while high-stiffness elements increase the mechanical bandwidth before dynamic decoupling happens between the inertias of interest. Active control serves as a viable alternative for speed ripple reduction when drivetrain compactness is key, instead of the current passive solutions.

Evaluating the Impact of Drivetrain Vibrations on MPPT Control Performance in Horizontal Axis Wind Turbines

This study investigates the impact of drivetrain shift vibrations on the control performance of Horizontal Axis Wind Turbines (HAWTs) using a Proportional-Integral-Derivative (PID) controller for Maximum Power Point Tracking (MPPT). Traditionally, PID controllers are tested on simplified rigid models, which do not account for the complex mechanical vibrations encountered in real-world applications. These vibrations, particularly those caused by drivetrain shifts, can significantly affect the stability and efficiency of the control system. Through detailed simulations involving both rigid and flexible drivetrain models, this paper evaluates how drivetrain vibrations influence the performance of the PID-based MPPT control algorithm. The results indicate that the flexible model, which incorporates drivetrain dynamics, experiences pronounced overshoot, oscillations, and significant drops in power coefficient (Cp) compared to the rigid model. These findings highlight the challenges of maintaining control stability and efficiency under varying vibration conditions, with the flexible model showing compromised stability and reduced power conversion efficiency during certain key intervals. This study emphasizes the importance of considering drivetrain dynamics in wind turbine control system design and provides insights into developing more robust and resilient PID control strategies.

Investigation of drivetrain dynamics on low-μ ground using the brush model

Investigation of drivetrain dynamics on low-μ ground using the brush model

Investigation of drivetrain dynamics on low-μ ground using the brush model

Investigation of drivetrain dynamics on low-μ ground using the brush model

Predictive Anti-Jerk and Traction Control for V2X Connected Electric Vehicles With Central Motor and Open Differential

V2X connectivity and powertrain electrification are emerging trends in the automotive sector, which enable the implementation of new control solutions. Most of the production electric vehicles have centralized powertrain architectures consisting of a single central on-board motor, a single-speed transmission, an open differential, half-shafts, and constant velocity joints. The torsional drivetrain dynamics and wheel dynamics are influenced by the open differential, especially in split- scenarios, i.e., with different tire-road friction coefficients on the two wheels of the same axle, and are attenuated by the so-called anti-jerk controllers. Although a rather extensive literature discusses traction control formulations for individual wheel slip control, there is a knowledge gap on: a) model based traction controllers for centralized powertrains; and b) traction controllers using the preview of the expected tire-road friction condition ahead, e.g., obtained through V2X, for enhancing the wheel slip tracking performance. This study presents nonlinear model predictive control formulations for traction control and anti-jerk control in electric powertrains with central motor and open differential, and benefitting from the preview of the tire-road friction level. The simulation results in straight line and cornering conditions, obtained with an experimentally validated vehicle model, as well as the proof-of-concept experiments on an electric quadricycle prototype, highlight the benefits of the novel controllers.

Analysis of rotorcraft ground resonance with generic inter-blade damper configurations

The paper investigates the effect of inter-blade dampers with generic in-plane and out-of-plane attachment offsets on ground resonance stability proneness. An analytical formulation, considering dampers with radial offsets only is initially proposed. Sensitivity analyses show that the increase of radial offset reduces the cyclic lead-lag damping and stiffness, providing a non-zero contribution to collective terms. The analytical formulation is suitable, in a preliminary design phase, to define the optimal location of the inter-blade attachment points to avoid ground resonance phenomena and to stabilize the engine drive-train dynamics. A more detailed numerical approach is then presented to consider generic in-plane and out-of-plane attachment offsets. Ground resonance stability analyses are performed also for cases with dissimilar dampers. It is found that out-of-plane offset leads to a modification on the blade pitch-lag coupling, acting on the helicopter stability margins. However, to capture these effects it is necessary to include the overall blade motions, considering flap, lag, and pitch dynamics, together with the corresponding generalized aerodynamics forces, usually neglected in classical ground resonance analysis. Finally, the periodic stability with one damper inoperative shows how, with the radial offsets, the hybridized lead-lag collective and cyclic modes may fall into resonance conditions due to super-harmonics.

UKF-based Wind Estimation and Sub-optimal Turbine Control under Waked Conditions

The knowledge of the Effective wind speed (EWS) allows the designing of wind turbine controllers that regulate power production and reduce loads on turbine components. Traditional single-point measurements are known to suffer from high noise and poor correlation with the EWS. As an alternative to overcome these problems, EWS estimators can be designed. The main challenge is the high non-linearity of the wind speed influence on the drive-train dynamics. Therefore, an estimator based on the unscented Kalman filter (UKF) is proposed and compared against an extended Kalman filter (EKF) and the immersion and invariance (I&I) technique. Simulation results are provided and show the superior performances attained by the UKF. Furthermore, the usefulness of the estimated EWS is demonstrated by designing a sliding mode controller (SMC) that can track a desired power reference. In addition, the controller allows operating in sub-optimal conditions, where load reduction is attained at the expense of power maximization. The proposed estimator's and controller's performances are evaluated under wind farm wake conditions via high-fidelity simulations. The findings show that UKF can outperform the EKF and the controller can reduce loads, except under highly waked conditions.

A novel convolutional network with a self-adaptation high-pass filter for fault diagnosis of wind turbine gearboxes

The fault diagnosis of a wind turbine gearbox is helpful for reducing the operating costs and risks of wind power systems. However, existing machine-learning-based gearbox fault diagnosis methods have two shortcomings: (a) data samples of gearbox faults are always scarce; and (b) due to the complex structure of gearboxes, the collected vibration signals often contain a large amount of low-frequency noise, which is detrimental to both feature extraction and fault diagnosis. To solve the above two problems, a combination of deep convolutional generative adversarial networks (DCGANs) and a convolutional network with a high-pass filter (CNHF) is proposed in this paper. Among them, the DCGAN combined with one-dimensional (1D) vibration data converted to a grayscale map is used to expand the fault data to solve the problem of a lack of fault data samples. The CNHF is realized by adding an adaptive high-pass filter to the conventional convolutional layer, and the threshold of the high-pass filter is adaptively set by the 1D convolution according to different data characteristics, thus greatly filtering out the interference of low-frequency noise and realizing the accurate diagnosis of faults. Experiments are performed on a drivetrain dynamics simulator rig to verify the efficacy of the proposed method.

Weak Fault Feature Extraction of Rolling Bearings Based on Adaptive Variational Modal Decomposition and Multiscale Fuzzy Entropy.

The working environment of rotating machines is complex, and their key components are prone to failure. The early fault diagnosis of rolling bearings is of great significance; however, extracting the single scale fault feature of the early weak fault of rolling bearings is not enough to fully characterize the fault feature information of a weak signal. Therefore, aiming at the problem that the early fault feature information of rolling bearings in a complex environment is weak and the important parameters of Variational Modal Decomposition (VMD) depend on engineering experience, a fault feature extraction method based on the combination of Adaptive Variational Modal Decomposition (AVMD) and optimized Multiscale Fuzzy Entropy (MFE) is proposed in this study. Firstly, the correlation coefficient is used to calculate the correlation between the modal components decomposed by VMD and the original signal, and the threshold of the correlation coefficient is set to optimize the selection of the modal number . Secondly, taking Skewness (Ske) as the objective function, the parameters of MFE embedding dimension M, scale factor S and time delay T are optimized by the Particle Swarm Optimization (PSO) algorithm. Using optimized MFE to calculate the modal components obtained by AVMD, the MFE feature vector of each frequency band is obtained, and the MFE feature set is constructed. Finally, the simulation signals are used to verify the effectiveness of the Adaptive Variational Modal Decomposition, and the Drivetrain Dynamics Simulator (DDS) are used to complete the comparison test between the proposed method and the traditional method. The experimental results show that this method can effectively extract the fault features of rolling bearings in multiple frequency bands, characterize more weak fault information, and has higher fault diagnosis accuracy.

Fuzzy-reasoning-based robust vibration controller for drivetrain mechanism with various control input updating timings

For active vibration control of powertrain oscillations, the purpose of this research is to present a simple fuzzy-reasoning-based compensation strategy for a time-varying control cycle limitation of an actuator. A simplified drivetrain dynamics model is shown, and the simulation configuration with the control cycle limitation is constructed. A model predictive compensation using a sampled-data controller is employed to tackle the maximal phase delay of the control input due to the time-varying control cycle. The control input update timing, which is perturbed from that of the fixed periodical controller, is expressed as fuzzy sets such as “Approximately past step” and “Approximately future step”. Based on these fuzzy sets, the Tsukamoto-type fuzzy reasoning with only four intuitive rules can flexibly infer unknown control inputs updated at various timings. Simulation verifications demonstrate that the 2-norm of the vibration performance by the proposed method is reduced by more than 27.8% compared to other previous controllers. Moreover, it is revealed that the better transient performance can be maintained even with 10% plant parameter variations, indicating the high robustness of the proposed damping controller.

Effects of mooring line failure on the dynamic responses of a semisubmersible floating offshore wind turbine including gearbox dynamics analysis

Floating offshore wind turbines (FOWTs) are located in complex ocean environments; therefore, they typically exhibit more significant dynamic responses than land-based wind turbines. Examples from the marine and offshore industries have shown that mooring line systems may fail under mild to severe sea conditions during their life cycle, possibly changing the global responses of the FOWTs and affecting their internal drivetrain dynamics. Therefore, this paper considered an OC4 DeepCwind semisubmersible FOWT as a representative model to investigate the influence of one broken mooring line on the steady-state and transient responses of the system, as well as on the internal drivetrain responses when the turbine operated normally. A fully coupled wind turbine model was constructed to analyze the global response, and then a decoupled method was applied to analyze the internal drivetrain responses. Furthermore, a set of load cases were considered to perform the simulations. The results showed that accidental fracture of the upwind line significantly affected platform motions, turbine structural loads, and tensions in the remaining lines. However, the effects of upwind line failure on the electrical power output and the internal gearbox dynamics were relatively small due to the almost unaffected nacelle yaw errors and the actions of pitch-torque control.

Research on the influence of system parameters on the electromechanical dynamics of a large wind turbine drivetrain

A wind turbine (WT) is a complex system integrating mechanical and electrical controls, and WTs can be coupled and interact with each other. This paper established an electromechanical model of a WT drivetrain and explored the influence of the system parameters of the WT drivetrain on the dynamic response and current harmonics. The results show that the introduction of a generator produces an electromagnetic frequency component in the mechanical system and that the mechanical parameters directly determine whether the generator undergoes periodic motion, quasiperiodic motion or chaotic motion, which affects the power generation efficiency of a WT. The research results can be used as a reference for the mechanical design of WT drivetrains and the selection of generators.

A review of wind turbine drivetrain loads and load effects for fixed and floating wind turbines

AbstractDrivetrain is one of the important subsystems in the wind turbine and faults and damages in the drivetrain significantly affect the wind turbine's downtime and nonavailability. Understanding the drivetrain dynamics and load effects that results in failures has been a major research area due to challenges in the drivetrain operations and maintenance. A systematic literature review of wind turbine drivetrains is presented according to key research areas of drivetrains, such as modeling and load effects on the drivetrain. Special emphasis is given for the floating wind turbine drivetrains. A review on the state‐of‐art modeling techniques of the drivetrain is presented. Studies concerning the aeroelastic load effects on the drivetrain components especially the gearbox and the bearings are discussed. Several key aspects such as aero‐hydro‐elastic interaction load effect and platform motion excitations on the floating wind turbine drivetrain dynamics are reviewed in detail. Finally, challenges related to floating wind turbine drivetrain are also discussed.This article is categorized under: Wind Power > Science and Materials Wind Power > Systems and Infrastructure Energy Research & Innovation > Science and Materials

Load prediction of hingeless helicopter rotors including drivetrain dynamics

In contrast to analyses with constrained hub speed, the present study includes the dynamic response of coupled rotor-drivetrain modes in the aeromechanic simulation of rotor blade loads. The structural model of the flexible Bo105 rotor-drivetrain system is coupled to aerodynamics modeled by an analytical formulation of unsteady blade element loads combined with a generalized dynamic wake or a free wake, respectively. For two flight states, i. e. cruise flight and large blade loading, a time-marching autopilot trim of the rotor-drivetrain system in wind tunnel configuration is performed. The simulation results are compared to those of a baseline case with constant rotor hub speed. The comparison reveals a major change in the blade passage frequency harmonics of the lead-lag loads. Beside the full drivetrain model, reduced models are shown to accurately represent the drivetrain influence on blade loads, if the eigenfrequency of the coupled second collective lead-lag/drivetrain mode is properly predicted. In a sensitivity analysis, this eigenfrequency is varied by stiffness modification of a reduced drivetrain model. The resulting changes in blade loads are correlated to this eigenfrequency, which serves as a simple though accurate classification of the drivetrain regarding its influence on vibratory blade loads. Finally, the potential to improve lead-lag load predictions by application of a drivetrain model is demonstrated through the comparison of simulated loads with measurements from a wind tunnel test.

Dynamic Analysis of Planetary Gear with Root Crack in Sun Gear Based on Improved Time-Varying Mesh Stiffness

The time-varying mesh stiffness (TVMS) is the crucial parameter of the dynamic model of the gear system. Accurate calculation of TVMS is essential for effective fault diagnosis of the gear system. A mesh stiffness improved method considering both the gear tooth transition curve and deformation of the gear body is presented in this paper, and the stiffness calculation expressions under healthy and crack states are given, respectively. Based on the lumped parameter method, the dynamic model of planetary gear with crack in sun gear is constructed, and the vibration response is solved. The simulation results show when the tooth root cracks appear, the vibration response of the tooth has obvious shock response characteristics. The characteristic frequency and frequency multiplication of sun gear fault can be found obviously by envelope analysis. The simulation signal and the test signal obtained by the drivetrain dynamics simulator gearbox test rig are compared and verified. The comparison and verification results show that the proposed TVMS calculation method and the dynamic model are accurate, which can provide a certain theoretical basis for the fault diagnosis of planetary gear with crack fault. The test results are consistent with the numerical analysis results, which provides a theoretical basis for the fault diagnosis of tooth crack.

A New Penalty Domain Selection Machine Enabled Transfer Learning for Gearbox Fault Recognition

The various structures and working conditions make gearbox fault recognition (GFR) more challenging. This article presents a new penalty domain selection machine (PDSM) enabled transfer learning for GFR study. The domain selection rules are designed using the band-selective independent component analysis to obtain the relation between different sensor locations and fault components for signal separation. Meanwhile, the initial penalty factors are calculated to speed up the PDSM process. For PDSM, the domain/signal penalty factors are added to the objective error function of original domain selection machine (DSM) to adapt varying working conditions and different sensor locations simultaneously. To solve the mixed optimization problem involved in PDSM, the Karush-Kuhn-Tucker conditions are utilized to transform it to a two-layer single problem. Experiments using drivetrain dynamics simulator prove that PDSM has higher diagnostic accuracy than other domain adaptation models. Meanwhile, it indicates faster convergence and stronger clustering capability than DSM.

Simplified design criteria for drivetrains in direct-drive wind turbines

Nominal power of wind turbines constantly increases. This goes along with an increase of the generator size, usually accompanied with an increase in mass. To limit at the same time the tower top mass, the mass increase of the generator needs to be restricted. Changing the design concept of the generator to reduce mass, influences its modal behavior and thereby possibly the dynamics of the whole drivetrain. This work identifies simplified criteria for the parameter space restrictions of drivetrain designs, while taking into account the interaction of the generator electromagnetic and structural characteristics. The drivetrain is analysed as an integrated system using a simplified two mass model to evaluate the dependency of system parameters and system stability. For two degrees of freedom an analytical solution is determined. Increasing the number of degrees of freedom to four, the parameter study is carried out using a full factorial analysis and a numerical solution approach. The results point out the necessity to take into account magnetic stiffness when designing the drivetrain to ensure system stability.

Effects of bedplate flexibility on drivetrain dynamics: Case study of a 10 MW spar type floating wind turbine

This paper deals with the effect of bedplate flexibility on drivetrain dynamics of a 10 MW spar type floating wind turbine. The 10 MW drivetrain bedplate is designed based on extreme design loads and ultimate limit state (ULS) design criteria. A decoupled analysis approach is employed. Global dynamic analysis of the 10 MW floating turbine is firstly conducted using an aero-hydro-servo-elastic code, then the global response is used as input to the drivetrain dynamic analysis. Load effects and fatigue damage of gears and bearings in the rigid and flexible bedplate models in different environmental conditions are compared. In addition, sensitivity of the drivetrain fatigue damage to varying fidelity in the bedplate modelling is studied. The results indicate that the bedplate flexibility would increase the load effects on bearings inside the gearbox, while it would reduce the load effects on the main bearings. Reasonable bedplate modelling fidelity is of great importance, because it could save a great deal of computational costs without loss of the drivetrain dynamic response accuracy. The present work provides a reference for a proper drivetrain design and dynamic analysis in the future, by accounting for bedplate flexibility. • A 10 MW drivetrain bedplate is designed for floating wind turbine based on extreme loads and ULS design criteria. • A high-fidelity drivetrain dynamic model is established and the coupled rotor-drivetrain-bedplate-tower eigenmodes are studied. • Load effects and fatigue damage of drivetrain gears and bearings in the rigid and flexible bedplate models are compared. • Sensitivity of drivetrain fatigue damage to bedplate modelling fidelity is studied.

Exploring Sample/Feature Hybrid Transfer for Gear Fault Diagnosis Under Varying Working Conditions

AbstractUnknown environmental noise and varying operation conditions negatively affect gear fault diagnosis (GFD) performance. In this paper, the sample/feature hybrid transfer learning (TL) strategies are adopted for GFD under varying working conditions, where source working conditions are considered to help the learning of target working conditions. Here, a multiple domains-feature vector is extracted where certain insensitive features offset the adverse effects of varying working conditions on sensitive features, including time domain, frequency domain, noise domain, and torque domain. Before TL, the signed-rank and chi-square test-based similarity estimation frame is adopted to select source data sets, aiming to reduce the possibility of negative transfer. Then, the hybrid transfer model, including the fast TrAdaBoost and partial model-based transfer (PMT) algorithm, is carried out, whose weights are allocated in sample and feature, respectively. Related experiments were conducted on the drivetrain dynamics simulator, which proves that feature transfer is more suitable for low-quality source domains while sample transfer is more suitable for high-quality source domains. Compared with non-transfer strategy, transfer learning is a useful tool to solve a practical GFD problem when facing with multiple working conditions, thus enhancing the universality and application value in fault diagnosis field.

A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

Most gear fault diagnosis (GFD) approaches suffer from inefficiency when facing with multiple varying working conditions at the same time. In this paper, a non-negative matrix factorization (NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix, aiming to offer a fast multi-tasking solution. The short-time Fourier transform (STFT) is first used to obtain the time-frequency features from the gear vibration signal. Then, the optimal clustering numbers are estimated using the Bayesian information criterion (BIC) theory, which possesses the simultaneous assessment capability, compared with traditional validity indexes. Subsequently, the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks. Finally, the parameters involved in BIC and NMF algorithms are determined using the gradient ascent (GA) strategy in order to achieve reliable diagnostic results. The Spectra Quest’s Drivetrain Dynamics Simulator gear data sets were analyzed to verify the effectiveness of the proposed approach.