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The paper aims to present an innovative method for identification of flight modes in the spin maneuver, which is highly nonlinear and coupled dynamic.

To fix the mode mixing problem which is mostly happen in the EMD algorithm, the authors focused on the proposal of an optimized ensemble empirical mode decomposition (OEEMD) algorithm for processing of the flight complex signals that originate from FDR. There are two improvements with the OEEMD respect to the EEMD. First, this algorithm is able to make a precise reconstruction of the original signal. The second improvement is that the OEEMD performs the task of signal decomposition with fewer iterations and so with less complexity order rather than the competitor approaches.

By applying the OEEMD algorithm to the spin flight parameter signals, flight modes extracted, then with using systematic technique, flight modes characteristics are obtained. The results indicate that there are some non-standard modes in the nonlinear region due to couplings between the longitudinal and lateral motions.

Application of the proposed method to the spin flight test data may result accurate identification of nonlinear dynamics with high coupling in this regime.

First, to fix the mode mixing problem in EMD, an optimized ensemble empirical mode decomposition algorithm is introduced, which disturbed the original signal with a sort of white Gaussian noise, and by using white noise statistical characteristics the OEEMD fix the mode mixing problem with high precision and fewer calculations. Second, by applying the OEEMD to the flight output signals and with using the systematic method, flight mode characteristics which is very important in the simulation and controller designing are obtained.

Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables.

Quantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model.

The results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets.

This research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.

The purpose of this paper is to improve the online monitoring level of low-frequency oscillation in the power system. A modal identification method of discrete Fourier transform (DFT) curve fitting based on ambient data is proposed in this study.

An autoregressive moving average mathematical model of ambient data was established, parameters of low-frequency oscillation were designed and parameters of low-frequency oscillation were estimated via DFT curve fitting. The variational modal decomposition method is used to filter direct current components in ambient data signals to improve the accuracy of identification. Simulation phasor measurement unit data and measured data of the power grid proved the correctness of this method.

Compared with the modified extended Yule-Walker method, the proposed approach demonstrates the advantages of fast calculation speed and high accuracy.

Modal identification method of low-frequency oscillation based on ambient data demonstrated high precision and short running time for small interference patterns. This study provides a new research idea for low-frequency oscillation analysis and early warning of power systems.

Early fault detection of bearing plays an increasingly important role in the operation of rotating machinery. Based on the properties of early fault signal of bearing, this paper aims to describe a novel hybrid early fault detection method of bearings.

In adaptive variational mode decomposition (AVMD), an adaptive strategy is proposed to select the optimal decomposition level K of variational mode decomposition. Then, a criterion based on envelope entropy is applied to select the optimal intrinsic mode functions (OIMF), which contains most useful fault information. Afterwards, local tangent space alignment (LTSA) is used to denoising of OIMF. The envelope spectrum of the OIMF is used to analyze the fault frequency, thereby detecting the fault. Experiments are conducted in a simulated signal and two experimental vibration signals of bearings to verify the effect of the new method.

The results show that the proposed method yields a good capability of detecting bearing fault at an early stage. The new method can extract more useful information and can reduce noise, which can provide better detection accuracy compared with the other two methods.

An adaptive strategy based on center frequency is proposed to select the optimal decomposition level of variational mode decomposition. Envelope entropy is used to fault feature selection. Combining the advantage of the AVMD-envelope entropy and LTSA, which suits the nature of the early fault signal. So, the proposed method has better detection accuracy, which provides a good alternative for early fault detection of bearings.

This paper aims to present a new fault detection and classification scheme of both DC faults and AC faults on a DC microgrid network.

To achieve reliable protection, the derivative of DC current signal is decomposed into several intrinsic modes using variational mode decomposition (VMD), which are then used as inputs to the Hilbert–Haung transform technique to obtain the instantaneous amplitude and frequency of the decomposed modes of the signal. A weighted Kurtosis index is used to obtain the most sensitive mode, which is used to compute sudden change in discrete Teager energy (DTE), indicating the occurrence of the fault. A stacked autoencoder-based neural network is applied for classifying the pole to ground (PG), pole to pole (PP), line to ground (LG), line to line (LL) and three-phase line to ground (LLLG) faults. The effectiveness of the proposed protection technique is validated in MATLAB/SIMULINK by considering different test cases.

As the maximum fault detection time is only 5 ms, the proposed detection technique is very fast. A stacked autoencoder-based neural network is applied for classifying the PG, PP, LG, LL and LLLG faults with classification accuracy of 99.1%.

The proposed technique provides a very fast, reliable and accurate protection scheme for DC microgrid system.

To reduce the influence of temperature on MEMS gyroscope, this paper aims to propose a temperature drift compensation method based on variational modal decomposition (VMD), time-frequency peak filter (TFPF), mind evolutionary algorithm (MEA) and BP neural network.

First, VMD decomposes gyro’s temperature drift sequence to obtain multiple intrinsic mode functions (IMF) with different center frequencies and then Sample entropy calculates, according to the complexity of the signals, they are divided into three categories, namely, noise signals, mixed signals and temperature drift signals. Then, TFPF denoises the mixed-signal, the noise signal is directly removed and the denoised sub-sequence is reconstructed, which is used as training data to train the MEA optimized BP to obtain a temperature drift compensation model. Finally, the gyro’s temperature characteristic sequence is processed by the trained model.

The experimental result proved the superiority of this method, the bias stability value of the compensation signal is 1.279 × 10^–3°/h and the angular velocity random walk value is 2.132 × 10^–5°/h/vHz, which is improved compared to the 3.361°/h and 1.673 × 10^–2°/h/vHz of the original output signal of the gyro.

This study proposes a multi-dimensional processing method, which treats different noises separately, effectively protects the low-frequency characteristics and provides a high-precision training set for drift modeling. TFPF can be optimized by SEVMD parallel processing in reducing noise and retaining static characteristics, MEA algorithm can search for better threshold and connection weight of BP network and improve the model’s compensation effect.

Laying the acoustic decoupling material on the surface of underwater structures is an effective noise reduction technology. The underwater sound radiation experiment of finite stiffened double cylindrical shell with separate‐sound and decoupled tile is carried out with the aim of finding out the most effective laying condition.

The segmentation power function interpolation method and vertex extreme value envelope continuation method are introduced into basic theory of empirical mode decomposition (EMD). The original measured sound pressure signals are decomposed to intrinsic mode function (IMF) group through EMD, and the high‐frequency components are filtered out. Because the mechanical noise of submarine is mainly at low frequency, the IMFs in low frequency are researched through power spectrum analysis. The noise reduction effects of different separate‐sound and decoupled tile laying conditions are compared.

The sound pressure signal components' amplitudes, periods and phases are obtained through EMD. The test data show that the double cylindrical shell entirely covered with separate‐sound and decoupled tile is the most effective laying condition in noise reduction.

With reference to the case study, this is believed to be the first application of the EMD in sound radiation time‐frequency characteristics of double cylindrical shell. The evaluation of separate‐sound and decoupled tile laying conditions is of great importance in engineering applications.

The purpose of this paper is to present the application of Hilbert–Huang transform (HHT) for fatigue damage feature characterisation in the time–frequency domain based on strain signals obtained from the automotive coil springs.

HHT was employed to detect the temporary changes in frequency characteristics of the vibration response of the signals. The extraction successfully reduced the length of the original signal to 40 per cent, whereas the fatigue damage was retained. The analysis process for this work is divided into three stages: signal characterisation with the application of fatigue data editing (FDE) for fatigue life assessment, empirical mode decomposition with Hilbert transform, an energy–time–frequency distribution analysis of each intrinsic mode function (IMF).

The edited signal had a time length of 72.5 s, which was 40 per cent lower than the original signal. Both signals were retained statistically with close mean, root-mean-square and kurtosis value. FDE improved the fatigue life, and the extraction did not affect the content and behaviour of the original signal because the editing technique only removed the minimal fatigue damage potential. HHT helped to remove unnecessary noise in the recorded signals. EMD produced sets of IMFs that indicated the differences between the original signal and mean of the signal to produce new components. The low-frequency energy was expected to cause large damage, whereas the high-frequency energy will cause small damage.

HHT and EMD can be used in the strain data signal analysis of the automotive component of a suspension system. This is to improve the fatigue life, where the extraction did not affect the content and behaviour of the original signal because the editing technique only removed the minimal fatigue damage potential.

The purpose of this paper is to prevent the destruction of other parts of a wind energy conversion system because of faults, the diagnosis of insulated-gate bipolar transistor (IGBT) faults has become an essential topic of study. Demand for sustainable energy sources has been prompted by rising environmental pollution and energy requirements. Renewable energy has been identified as a viable substitute for conventional fossil fuel energy generation. Because of its rapid installation time and adaptable expenditure for construction scale, wind energy has emerged as a great energy resource. Power converter failure is particularly significant for the reliable operation of wind power conversion systems because it not only has a high yearly fault rate but also a prolonged downtime. The power converters will continue to operate even after the failure, especially the open-circuit fault, endangering their other parts and impairing their functionality.

The most widely used signal processing methods for locating open-switch faults in power devices are the short-time Fourier transform and wavelet transform (WT) – based on time–frequency analysis. To increase their effectiveness, these methods necessitate the intensive use of computational resources. This study suggests a fault detection technique using empirical mode decomposition (EMD) that examines the phase currents from a power inverter. Furthermore, the intrinsic mode function’s relative energy entropy (REE) and simple logical operations are used to locate IGBT open switch failures.

The presented scheme successfully locates and detects 21 various classes of IGBT faults that could arise in a two-level three-phase voltage source inverter (VSI). To verify the efficacy of the proposed fault diagnosis (FD) scheme, the test is performed under various operating conditions of the power converter and induction motor load. The proposed method outperforms existing FD schemes in the literature in terms of fault coverage and robustness.

This study introduces an EMD–IMF–REE-based FD method for VSIs in wind turbine systems, which enhances the effectiveness and robustness of the FD method.

The purpose of this manuscript is to detect heart fault using Electrocardiogram. Mutually low and high frequency noises such as electromyography (EMG) and power line interference (PLI) degrades the performance of ECG signals.

The ECG record depicts the procedural electrical movement of the heart, which is non-invasive foot age obtained by placing surface electrodes on designated locations of the patient’s skin. The main concept of this manuscript is to present a novel filtering method to cancel the unwanted noises in ECG signal. Here, intrinsic time scale decomposition (ITD) is introduced to suppress the effect of PLI from ECG signals.

In the existing ITD, the gain control parameter is a constant value; however, in this paper it is an adaptive feature that varies according to certain constraints. Simulation outcomes show that the proposed method effectively reduces the effect of PLI and quantitatively express the effectiveness with different evaluation metrics.

The results found by the proposed method are compared with Fourier decomposition technique and eigen value decomposition methods (EDM) to validate the effectiveness of the proposed method.