Flow-State Identification of Oil-Based Magnetic Fluid Seal Based on Acoustic Emission Technology

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

Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features

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 Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM

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

An Improved ABC Algorithm and Its Application in Bearing Fault Diagnosis with EEMD

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 Application of Heterogeneous Information Fusion in Misalignment Fault Diagnosis of Wind Turbines

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

How Does Energy Consumption and Economic Development Affect Carbon Emissions? A Multi-Process Decomposition Framework

Against the background that climate warming has become a global challenge, exploring the factors that drive carbon emissions change is important to achieve emission reduction targets. Because of the differences in economic development, resource endowment, and historical accumulation, different countries generally have significant technological heterogeneity in the carbon generation process. Therefore, the heterogeneity-related factors should also be understood, which can help policy making and responsibility attribution more accurate. As such, this study developed a meta-frontier-based production–theoretical decomposition analysis method to track the progress of carbon emission change in 42 countries during 2012–2019 with production heterogeneity between the countries taken into account. The empirical study draws the following three meaningful conclusions: firstly, the carbon emission process of different countries has clear technological heterogeneity, mainly reflected in aspects of their energy-use efficiency and energy-use technology. Secondly, the decomposition analysis results showed that the potential energy intensity effect and the economic activity effect played the dominant role in driving and reducing carbon emissions, respectively. Additionally, this conclusion is right for all types of countries. Thirdly, the attribution analysis showed that different types of countries have significantly different contributions to the influencing factors of carbon emission changes, among which countries with large energy consumption and large economies need to take more responsibility for emission reduction

Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

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

The Application of Dual-Tree Complex Wavelet Transform (DTCWT) Energy Entropy in Misalignment Fault Diagnosis of Doubly-Fed Wind Turbine (DFWT)

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

Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM

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