Search

A magnetic fluid seal is often used in complex working conditions with harsh environmental requirements. Timely and accurate identification of the seal status can help avoid the major economic losses and even casualties caused by the seal failure. However, research on the recognition of magnetic fluid seal status is still at the exploratory stage internationally. Aiming at the problem of inclusion of other components and Gaussian noise when using acoustic emission nondestructive testing technology to detect the magnetic fluid seal status, a new recognition method based on the combination of high-order cumulant image and VGG16 convolutional neural network is proposed to identify the magnetic fluid seal status in this paper. In this method, high-order cumulant images are used for the denoising and feature selecting of detected signals, and the VGG16 convolutional neural network is trained to automatically learn image features to classify and recognize high-order cumulant images representing different sealing states. Experiments show that the accuracy of image recognition using VGG16 is significantly higher than that of other methods. The VGG16 method can identify the magnetic fluid seal state accurately and effectively, with strong robustness and Gaussian noise suppression ability.

At present, most research studies on the changing process of the magnetic fluid seal are analyzed with the pressure signal of each chamber or the magnetic fluid flow photos taken by a camera, which need to change the seal structure. Based on nondestructive acoustic emission technology, a flow-state identification model of the oil-based magnetic fluid seal using the grey wolf optimizer and random forest is proposed in this study. The acoustic emission signal and pressure signal are collected at the same time under static conditions in the two-stage pole shoes oil-based magnetic fluid seal experiment. Through power spectrum analysis of the acoustic emission signal with the aid of pressure signal, the changing process before seal failure is divided into three states: no magnetic fluid flow, the first pole shoe magnetic fluid flow, and two pole shoes magnetic fluid flow together. Then, the time- and frequency-domain features of acoustic emission signal samples are extracted to form feature vectors as inputs, and the flow-state identification model is established based on the grey wolf optimizer and random forest. The experimental results show that the testing accuracy and F1 scores (the index representing the precision and recall at the same weight) of three states are close to or higher than 90%. The effectiveness of oil-based magnetic fluid seal flow-state identification model based on non-destructive acoustic emission technology is proved.

A novel frame-embedded track (NFET) with additional beams between the prefabricated rail seats before was proposed, but its geometric parameters need to be further optimized, in order to solve the problem of difficulty in adjustment, due to the independence of the two prefabricated rail seats during the construction of tram. Based on the finite element method, the geometric parameters of the NFET structure are systematically studied and optimized. The research shows: (1) As the width of the beams and the thickness of the lower slab increases, the mechanical characteristics of the NFET structure does not change significantly; therefore, the recommended design reference value for these two are 240 and 80 mm, respectively. (2) When considering the cable and the drainage facilities, the stress state of the NFET structure is less affected (or even improved), which proves that the layout of the cable and the drainage facilities is feasible. (3) According to the analysis during different construction stages, the stress of the NFET rail seats is generally greater than the stress of the cast-in-place concrete. It is recommended that the intensity of the cast-in-place concrete should be greater than that of the prefabricated frame structure.

To fill the blank in the research on the dynamic performance of track structure under long-term service, the dynamic response study of China Railway Track System Ⅲ type slab ballastless track (CRTSIII SBT) under the action of fatigue for 30 million times and the parting between track slab and self-compacting concrete (SCC) was carried out. By establishing the finite element model of the CRTSIII SBT structure and taking the stiffness change of isolation layer and fastener under fatigue state and the parting during service as the research objects, combined with the full-scale model test, the dynamic response amplitude and vibration law of track structure was analyzed based on the finite element model of axle falling test method. The results show the following: (1) Under the fatigue load, the acceleration of rail and base increases obviously, the longitudinal tensile stress of SCC surface decreases, the longitudinal tensile stress of base surface increases, and the vertical stress of each layer of track structure increases as well. (2) Under the action of the parting, the dynamic response of each structural layer increases, and the change of acceleration and stress of each layer under the action point of axle falling is the most obvious. (3) The fatigue load will weaken the vibration damping performance of the track, and the parting will continue to develop under the action of the falling axle, resulting in partial or total failure of the SCC layer. Both of them will aggravate the dynamic response of the track structure and affect driving safety, which should be paid attention to during maintenance.

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

A misalignment fault is a kind of potential fault in double-fed wind turbines. The reasonable and effective fault prediction models are used to predict its development trend before serious faults occur, which can take measures to repair in advance and reduce human and material losses. In this paper, the Least Squares Support Vector Machine optimized by the Improved Artificial Fish Swarm Algorithm is used to predict the misalignment index of the experiment platform. The mixed features of time domain, frequency domain, and time-frequency domain indexes of vibration or stator current signals are the inputs of the Least Squares Support Vector Machine. The kurtosis of the same signals is the output of the model, and the 3σ principle of the normal distribution is adopted to set the warning line of misalignment fault. Compared with other optimization algorithms, the experimental results show that the proposed prediction model can predict the development trend of the misalignment index with the least prediction error.

Most conventional wind turbine fault diagnosis techniques only use a single type of signal as fault feature and their performance could be limited to such signal characteristics. In this paper, multiple types of signals including vibration, temperature, and stator current are used simultaneously for wind turbine misalignment diagnosis. The model is constructed by integrated methods based on Dempster–Shafer (D–S) evidence theory. First, the time domain, frequency domain, and time–frequency domain features of the collected vibration, temperature, and stator current signal are respectively taken as the inputs of the least square support vector machine (LSSVM). Then, the LSSVM outputs the posterior probabilities of the normal, parallel misalignment, angular misalignment, and integrated misalignment of the transmission systems. The posterior probabilities are used as the basic probabilities of the evidence fusion, and the fault diagnosis is completed according to the D–S synthesis and decision rules. Considering the correlation between the inputs, the vibration and current feature vectors’ dimensionalities are reduced by t-distributed stochastic neighbor embedding (t-SNE), and the improved artificial bee colony algorithm is used to optimize the parameters of the LSSVM. The results of the simulation and experimental platform demonstrate the accuracy of the proposed model and its superiority compared with other models.

Due to the harsh working environment of wind turbines, various types of faults are prone to occur during long-term operation. Misalignment faults between the gearbox and the generator are one of the latent common faults for doubly-fed wind turbines. Compared with other faults like gears and bearings, the prediction research of misalignment faults for wind turbines is relatively few. How to accurately predict its developing trend has always been a difficulty. In this paper, a combined forecasting model is proposed for misalignment fault prediction of wind turbines based on vibration and current signals. In the modelling, the improved Multivariate Grey Model (IMGM) is used to predict the deterministic trend and the Least Squares Support Vector Machine (LSSVM) optimized by quantum genetic algorithm (QGA) is adopted to predict the stochastic trend of the fault index separately, and another LSSVM optimized by QGA is used as a non-linear combiner. Multiple information of time-domain, frequency-domain and time-frequency domain of the wind turbine’s vibration or current signals are extracted as the input vectors of the combined forecasting model and the kurtosis index is regarded as the output. The simulation results show that the proposed combined model has higher prediction accuracy than the single forecasting models.

The Ensemble Empirical Mode Decomposition (EEMD) algorithm has been used in bearing fault diagnosis. In order to overcome the blindness in the selection of white noise amplitude coefficient e in EEMD, an improved artificial bee colony algorithm (IABC) is proposed to obtain it adaptively, which providing a new idea for the selection of EEMD parameters. In the improved algorithm, chaos initialization is introduced in the artificial bee colony (ABC) algorithm to insure the diversity of the population and the ergodicity of the population search process. On the other hand, the collecting bees are divided into two parts in the improved algorithm, one part collects the optimal information of the region according to the original algorithm, the other does Levy flight around the current global best solution to improve its global search capabilities. Four standard test functions are used to show the superiority of the proposed method. The application of the IABC and EEMD algorithm in bearing fault diagnosis proves its effectiveness.

The misalignment of the drive system is one of the important factors causing damage to gears and bearings on the high-speed output end of the gearbox in doubly-fed wind turbines. How to use the obtained information to determine the types of the faults accurately has always been a challenging problem for researchers. Under the restriction that only one kind of signal is used in the current wind turbine fault diagnosis, a new method based on heterogeneous information fusion is presented in this paper. The collected vibration signal, temperature signal, and stator current signal are used as original sources. Their time domain, frequency domain and time-frequency domain information are extracted as fault features. Taking into account the correlation between the features, t-distributed Stochastic Neighbor Embedding (t-SNE) is used to reduce the dimensionality of the original combinations. Then, the fusion features are put into the Least Square Support Vector Machine (LSSVM), which is optimized by artificial bee colony (ABC) algorithm. The simulation tests show that this method has higher diagnostic accuracy than other methods.

Misalignment is one of the common faults for the doubly-fed wind turbine (DFWT), and the normal operation of the unit will be greatly affected under this state. Because it is difficult to obtain a large number of misaligned fault samples of wind turbines in practice, ADAMS and MATLAB are used to simulate the various misalignment conditions of the wind turbine transmission system to obtain the corresponding stator current in this paper. Then, the dual-tree complex wavelet transform is used to decompose and reconstruct the characteristic signal, and the dual-tree complex wavelet energy entropy is obtained from the reconstructed coefficients to form the feature vector of the fault diagnosis. Support vector machine is used as classifier and particle swarm optimization is used to optimize the relevant parameters of support vector machine (SVM) to improve its classification performance. The results show that the method proposed in this paper can effectively and accurately classify the misalignment of the transmission system of the wind turbine and improve the reliability of the fault diagnosis.

In the working process of Double-Fed Wind Turbines (DFWT), it is very important to monitor and predict the temperature of the high-speed output shaft of the gearbox timely and effectively. Support vector machine has more advantages in the temperature prediction of wind turbines. Least squares support vector machine is suitable for online prediction due to reducing the computational complexity of support vector machine. In order to solve the sparsity of least squares support vector machine, an improved least squares support vector machine based on pruning algorithm is proposed in this paper to predict the temperature of the high-speed output shaft of gearbox using the practical data of Double-Fed Wind Turbines. At the same time, in order to improve the prediction accuracy and to solve the problem of few links between different feature parameters in common normalization method, the paper uses the method of joint normalization to preprocess the data. The principal component analysis is used to reduce the dimension of the data. Particle swarm optimization algorithm is used to optimize the parameters of the pruning least squares support vector machine. The proposed model that is established in this paper is a new model to forecast the temperature of the high-speed output shaft. The results show that its prediction accuracy is higher than that of other algorithms.

The misalignment of the drive system of the DFIG (Doubly Fed Induction Generator) wind turbine is one of the important factors that cause damage to the gears, bearings of the high-speed gearbox and the generator bearings. How to use the limited information to accurately determine the type of failure has become a difficult study for the scholars. In this paper, the time-domain indexes and frequency-domain indexes are extracted by using the vibration signals of various misaligned simulation conditions of the wind turbine drive system, and the time-frequency domain features—energy entropy are also extracted by the IEMD (Improved Empirical Mode Decomposition). A mixed-domain feature set is constructed by them. Then, SVM (Support Vector Machine) is used as the classifier, the mixed-domain features are used as the inputs of SVM, and PSO (Particle Swarm Optimization) is used to optimize the parameters of SVM. The fault types of misalignment are classified successfully. Compared with other methods, the accuracy of the given fault isolation model is improved.

Misalignment is an important cause for the early failure of large doubly-fed wind turbines (DFWT). For the non-stationary characteristics of the signals in the transmission system of DFWT and the reality that it is difficult to obtain a large number of fault samples, Solidworks and Adams are used to simulate the different operating conditions of the transmission system of the DFWT to obtain the corresponding characteristic signals. Improved empirical mode decomposition (IEMD), which improves the end effects of empirical mode decomposition (EMD) is used to decompose the signals to get intrinsic mode function (IMF), and the IEMD energy entropy reflecting the working state are extracted as the inputs of the support vector machine (SVM). Particle swarm optimization (PSO) is used to optimize the parameters of SVM to improve the classification performance. The results show that the proposed method can effectively and accurately identify the types of misalignment of the DFWT.

Wind power generation systems require complex control systems with multiple working conditions and multiple controllers. Under different operating conditions, switching without disturbancebetweenthesub-controllersplaysacriticalroleinensuringthestabilityofpowersystems. The sub-controllers of two typical cases in the permanent magnet direct drive (PMDD) wind turbine running process are studied, one is the proportional integral (PI) controller in the maximum power points tracking (MPPT) stage, the other is the fuzzy pitch angle controller in the constant power stage. The switching strategy of the two sub-controllers is the emphasis in this research. Based on the active disturbance rejection control (ADRC), the switching mode of the sub-controllers is proposed, which can effectively restrain the sudden changes of the rotor current during the switching process, and improve the quality of power generation. The feasibility and effectiveness of the sub-controller switching strategy is verified by Matlab/Simulink simulation for a 2 MW PMDD wind turbine.

In order to meet the requirements of high precision and fast response of permanent magnet direct drive (PMDD) wind turbines, this paper proposes a fuzzy proportional integral (PI) controller associated with a new control strategy for wind turbine converters. The purpose of the control strategy is to achieve the global optimization for the quantization factors, ke and kec, and scale factors, kup and kui, of the fuzzy PI controller by an improved particle swarm optimization (PSO) method. Thus the advantages of the rapidity of the improved PSO and the robustness of the fuzzy controller can be fully applied in the control process. By conducting simulations for 2 MW PMDD wind turbines with Matlab/Simulink, the performance of the fuzzy PI controller based on the improved PSO is demonstrated to be obviously better than that of the PI controller or the fuzzy PI controller without using the improved PSO under the situation when the wind speed changes suddenly.