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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.

Due to the fault vibration signal of the rolling bearing is greatly interfered by the background noise, the fault features are easily submerged and result in a low fault diagnosis accuracy. A novel fault diagnosis method of rolling bearing is proposed based on improved VMD-adaptive wavelet threshold combined with noise reduction in this paper. Firstly, the modal components are obtained based on VMD decomposition; Secondly, the dual determination criteria of sample entropy and correlation coefficient are constructed to filter the components; Subsequently, an adaptive wavelet thresholding function is proposed, and quadratic noise reduction is applied to mixed IMFs, which in turn reconstructs each component to achieve joint noise reduction. Finally, based on traditional machine learning and deep learning diagnosis methods, the features of noise reduction signals are extracted to realize fault diagnosis. By verifying and analyzing the simulated signal with the measured signal, noise components, the expression of fault characteristics, and the accuracy of fault diagnosis are eliminated, enhanced, and improved.

Summary: Wind energy is one of the most cost-effective energy sources available today. Some techniques have been developed to scavenge wind energy, but making use of lower flutter velocity while maintaining high conversion efficiency remains challenging. Here, we report a triboelectric nanogenerator composed of two interacting triboelectric films with four flapping modes, enabling an effective work wind velocity as low as 1.6 ms−1 and a high conversion efficiency of 3.23%, which, to our knowledge, are better than previously reported values of wind energy scavenging. The output performance of B-TENG can be determined by device size and flow velocity; the optimized device exhibits an output voltage, current, and power of 175 V, 43 μA, and 2.5 mW, respectively, with dimensions of 3 × 8 × 2 cm3 at a flow velocity of 8 ms−1. The B-TENG may pave the way for future wind scavenging devices for a range of point-of-use applications.

Planetary gearbox faults are the leading causes of downtime in wind turbines (WTs). In recent years, numerous and various vibration-based approaches have been put forward for WT gearbox fault detection. In the vibration-based techniques, order tracking-based methods, which by identifying fault orders in gearbox drivetrains, are regarded as very promising and powerful techniques. In the currently available order tracking methods, auxiliary devices are required to accurately obtain the instantaneous angular speed (IAS) of drivetrain. To tackle this problem, instantaneous angular speed estimation from vibration signals has been studied and some tacho-less order tracking (TLOT) approaches have been developed. However, many vital parameters for IAS estimation in the currently available TLOT algorithms need to be manually selected, which raise the question of user-friendliness, even result in a false diagnosis. As mentioned earlier, aiming at the shortcomings, a novel TLOT method based on adaptive IAS estimation is proposed for WT planetary gearbox fault diagnosis. In the proposed method, the nonlinear mode decomposition (NMD) method is improved, and its computational burden is reduced. And, the tachometer information of the drivetrain is adaptively extracted by the improved NMD method from generator vibration signal for gearbox vibration signal resampling. A field test is conducted, and the vibration signal of WT planetary gearbox with the compound fault is used for further investigation. The experimental validation results demonstrate that the planetary gearbox compound fault can be successfully detected, and the proposed method outperforms the traditional method based on generalized demodulation.

Vibration-based energy harvesting technology is the most promising method to solve the problems of self-powered wireless sensor nodes, but most of the vibration-based energy harvesters have a rather narrow operation bandwidth and the operation frequency band is not convenient to adjust when the ambient frequency changes. Since the ambient vibration may be broadband and changeable, a novel V-shaped vibration energy harvester based on the conventional piezoelectric bimorph cantilevered structure is proposed, which successfully improves the energy harvesting efficiency and provides a way to adjust the operation frequency band of the energy harvester conveniently. The electromechanical coupling equations are established by using Euler-Bernoulli equation and piezoelectric equation, and then the coupled circuit equation is derived based on the series connected piezoelectric cantilevers and Kirchhoff's laws. With the above equations, the output performances of V-shaped structure under different structural parameters and load resistances are simulated and discussed. Finally, by changing the angle θ between two piezoelectric bimorph beams and the load resistance, various comprehensive experiments are carried out to test the performance of this V-shaped energy harvester under the same excitation. The experimental results show that the V-shaped energy harvester can not only improve the frequency response characteristic and the output performance of the electrical energy, but also conveniently tune the operation bandwidth; thus it has great application potential in actual structure health monitoring under variable working condition.

A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs) by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE) is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.

Shock pulse method is a widely used technique for condition monitoring of rolling bearing. However, it may cause erroneous diagnosis in the presence of strong background noise or other shock sources. Aiming at overcoming the shortcoming, a pulse adaptive time-frequency transform method is proposed to extract the fault features of the damaged rolling bearing. The method arranges the rolling bearing shock pulses extracted by shock pulse method in the order of time and takes the reciprocal of the time interval between the pulse at any moment and the other pulse as all instantaneous frequency components in the moment. And then it visually displays the changing rule of each instantaneous frequency after plane transformation of the instantaneous frequency components, realizes the time-frequency transform of shock pulse sequence through time-frequency domain amplitude relevancy processing, and highlights the fault feature frequencies by effective instantaneous frequency extraction, so as to extract the fault features of the damaged rolling bearing. The results of simulation and application show that the proposed method can suppress the noises well, highlight the fault feature frequencies, and avoid erroneous diagnosis, so it is an effective fault feature extraction method for the rolling bearing with high time-frequency resolution.

In order to identify the fault of rotating machine effectively, a new method based on the morphological filter optimized by particle swarm optimization algorithm (PSO) and the nonlinear manifold learning algorithm local tangent space alignment (LTSA) is proposed. Firstly, the signal is purified by the morphological filter; the filter’s structure element (SE) is selected by PSO method. Then the filtered signals are decomposed by the empirical mode decomposition (EMD) method, and the extract features are mapped into the LTSA to extract the character features; then the support vector machine (SVM) model is used to achieve the rotating machine fault diagnosis. The proposed method is evaluated by vibration signals measured from bearings with faults. Results show that the method can effectively remove the noise and extract the fault features, so the rotating machine fault diagnosis can be achieved effectively.

Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM) and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.