Application of Hilbert–Huang Transform With Improved Ensemble Empirical Mode Decomposition in Nonlinear Flight Dynamic Mode Characteristics Estimation

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
S. Abolfazl. Mokhtari ◽  
Mehdi. Sabzehparvar
Keyword(s):  
Empirical Mode Decomposition ◽  
Test Data ◽  
Flight Test ◽  
Ensemble Empirical Mode Decomposition ◽  
Dynamic Mode ◽  
Original Signal ◽  
Complex Signals ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Highly Nonlinear

Identification of aircraft flight dynamic modes has been implemented by adopting highly nonlinear flight test data. This paper presents a new algorithm for identification of the flight dynamic modes based on Hilbert–Huang transform (HHT) due to its superior potential capabilities in nonlinear and nonstationary signal analysis. Empirical mode decomposition and ensemble empirical mode decomposition (EEMD) are the two common methods that apply the HHT transform for decomposition of the complex signals into instantaneous mode frequencies; however, experimentally, the EMD faces the problem of “mode mixing,” and EEMD faces with the signal precise reconstruction, which leads to imprecise results in the estimation of flight dynamic modes. In order to overcome (handle) this deficiency, an improved EEMD (IEEMD) algorithm for processing of the complex signals that originate from flight data record was introduced. This algorithm disturbing the original signal using white Gaussian noise, IEEMD, is capable of making a precise reconstruction of the original signal. The second improvement is that IEEMD performs signal decomposition with fewer number of iterations and less complexity order rather than EEMD. This algorithm has been applied to aircraft spin maneuvers flight test data. The results show that implication of IEEMD algorithm on the test data obtained more precise signal extractions with fewer iterations in comparison to EEMD method. The signal is reconstructed by summing the flight modes with more accuracy respect to the EEMD. The IEEMD requires a smaller ensemble size, which results in saving of a significant computational cost.

Spin flight mode identification with OEEMD algorithm

2019 ◽  
Vol 91 (4) ◽  
pp. 582-600
Author(s):  
S. Abolfazl Mokhtari ◽  
Mehdi Sabzehparvar
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Signal ◽  
Complex Signals ◽  
Content Type ◽  
Mode Decomposition ◽  
Flight Mode ◽  
Highly Nonlinear ◽  
Mixing Problem ◽  
Mode Mixing

Purpose The paper aims to present an innovative method for identification of flight modes in the spin maneuver, which is highly nonlinear and coupled dynamic. Design/methodology/approach 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. Findings 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. Practical implications Application of the proposed method to the spin flight test data may result accurate identification of nonlinear dynamics with high coupling in this regime. Originality/value 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.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

Toward a Digital Twin: Time Series Prediction Based on a Hybrid Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Weifei Hu ◽  
Yihan He ◽  
Zhenyu Liu ◽  
Jianrong Tan ◽  
Ming Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Prediction ◽  
Ensemble Empirical Mode Decomposition ◽  
Bayesian Optimization ◽  
Series Data ◽  
Digital Twin ◽  
Mode Decomposition ◽  
Highly Nonlinear

Abstract Precise time series prediction serves as an important role in constructing a digital twin (DT). The various internal and external interferences result in highly nonlinear and stochastic time series. Although artificial neural networks (ANNs) are often used to forecast time series because of their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on an ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components (each component is a single-frequency and stationary signal) and a residual signal. The decomposed signals are used to train the neural networks, in which the hyperparameters are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed EEMD-BO-LSTM neural networks, this paper conducts two case studies (the wind speed prediction and the wave height prediction) and implements a comprehensive comparison between the proposed method and other approaches including the persistence model, autoregressive integrated moving average (ARIMA) model, LSTM neural networks, BO-LSTM neural networks, and EEMD-LSTM neural networks. The results show an improved prediction accuracy using the proposed method by multiple accuracy metrics.

Flight dynamics modeling of elastic aircraft using signal decomposition methods

2019 ◽  
Vol 233 (12) ◽  
pp. 4380-4395 ◽  
Author(s):  
Seyed Amin Bagherzadeh
Keyword(s):  
Empirical Mode Decomposition ◽  
Test Data ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Flight Dynamics ◽  
Flight Test ◽  
Signal Decomposition ◽  
Identification Process ◽  
Mode Decomposition ◽  
Flight Parameters

To improve the precision and accuracy of the flight dynamic models for elastic aircraft, this paper provides a novel method that extracts observable flight modes from flight test data and uses them in the identification process. For this purpose, a gray-box time-domain method is employed with the nonlinear ARX structure and the Levenberg–Marquardt parameter estimation technique. In the proposed method, the components of the flight parameters are extracted by two signal decomposition techniques, namely the singular spectrum analysis and empirical mode decomposition. These components are inputted into the identification process. Flight test data of the active aeroelastic wing is examined by the proposed method. The results indicate that both the singular spectrum analysis-based and empirical mode decomposition-based identification processes have desired performances in dealing with nontrained flight conditions. Thus, these methods may be utilized as an interpolation method to estimate the aircraft flight dynamics within the flight envelope. However, the empirical mode decomposition outperforms the singular mode decomposition because it is more significant in decomposing flight parameters based on the frequency content. Hence, the empirical mode decomposition may be a better tool to be employed for the aeroelastic aircraft system identification.

New insights and best practices for the succesful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms in the decomposition of nonlinear and nonstationary signals

2020 ◽  
Author(s):  
Antonio Cicone ◽  
Angela Stallone ◽  
Massimo Materassi ◽  
Haomin Zhou
Keyword(s):  
Empirical Mode Decomposition ◽  
Best Practice ◽  
Real Life ◽  
Original Signal ◽  
Open Problems ◽  
Nonstationary Signals ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Highly Nonlinear ◽  
Iterative Filtering

<p>Nonlinear and nonstationary signals are ubiquitous in real life. Their time–frequency analysis and features extraction can help in solving open problems in many fields of research. Two decades ago, the Empirical Mode Decomposition (EMD) algorithm was introduced to tackle highly nonlinear and nonstationary signals. It consists of a local and adaptive data–driven method which relaxes several limitations of the standard Fourier transform and the wavelet Transform techniques, yielding an accurate time-frequency representation of a signal. Over the years, several variants of the EMD algorithm have been proposed to improve the original technique, such as the Ensemble Empirical Mode Decomposition (EEMD) and the Iterative Filtering (IF).<br><br></p><p>The versatility of these techniques has opened the door to their application in many applied fields, like geophysics, physics, medicine, and finance. Although the EMD– and IF–based techniques are more suitable than traditional methods for the analysis of nonlinear and nonstationary data, they could easily be misused if their known limitations, together with the assumptions they rely on, are not carefully considered. Here we call attention to some of the pitfalls encountered when implementing these techniques. Specifically, there are three critical factors that are often neglected: boundary effects; presence of spikes in the original signal; signals containing a high degree of stochasticity. We show how an inappropriate implementation of the EMD and IF methods could return an artefact–prone decomposition of the original signal. We conclude with best practice guidelines for researchers who intend to use these techniques for their signal analysis.</p>

The Applied Research of the Hilbert-Huang Transform and Wavelet Transform in the Fault Location of Transmission Line

2013 ◽  
Vol 291-294 ◽  
pp. 2432-2436
Author(s):  
Zhi Bin Li ◽  
Bao Xing Wu ◽  
Yun Hui Xu
Keyword(s):  
Wavelet Transform ◽  
Transmission Line ◽  
Empirical Mode Decomposition ◽  
Fault Location ◽  
Ensemble Empirical Mode Decomposition ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Decomposition Processes ◽  
Before And After ◽  
Better Than

In the process of the Hilbert-Huang transform, empirical mode decomposition (EMD) may result in the end effect and modal aliasing when processing data, so proposing Ensemble Empirical Mode Decomposition (EEMD) instead of EMD, and assessing the accuracy of the two decomposition processes according to the total energy of the signal before and after the decomposition. Take a comparison between the Hilbert-Huang transform and the wavelet transform, the localization showed that the Hilbert-Huang transform is better than wavelet transform in the fault location of transmission line.

scholarly journals Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xianzhao Yang ◽  
Gengguo Cheng ◽  
Huikang Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Observation Interval ◽  
Extreme Points ◽  
Decomposition Algorithm ◽  
Complex Signal ◽  
Complex Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Average Value ◽  
Mode Decomposition

Hilbert-Huang transform is widely used in signal analysis. However, due to its inadequacy in estimating both the maximum and the minimum values of the signals at both ends of the border, traditional HHT is easy to produce boundary error in empirical mode decomposition (EMD) process. To overcome this deficiency, this paper proposes an enhanced empirical mode decomposition algorithm for processing complex signal. Our work mainly focuses on two aspects. On one hand, we develop a technique to obtain the extreme points of observation interval boundary by introducing the linear extrapolation into EMD. This technique is simple but effective in suppressing the error-prone effects of decomposition. On the other hand, a novel envelope fitting method is proposed for processing complex signal, which employs a technique of nonuniform rational B-splines curve. This method can accurately measure the average value of instantaneous signal, which helps to achieve the accurate signal decomposition. Simulation experiments show that our proposed methods outperform their rivals in processing complex signals for time frequency analysis.

The Rolling Bearing Fault Diagnosis Research Based on Improved Hilbert-Huang Transformation

2013 ◽  
Vol 300-301 ◽  
pp. 344-350 ◽  
Author(s):  
Zhou Wan ◽  
Xing Zhi Liao ◽  
Xin Xiong ◽  
Jin Chuan Han
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Hilbert Huang Transform ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Eemd Method

For empirical mode decomposition (EMD) of Hilbert-Huang transform (HHT) exists the problem of mode mixing. An analysis method based on ensemble empirical mode decomposition (EEMD) is proposed to apply to fault diagnosis of rolling bearing. This paper puts forward, after signal pretreatment, applying EEMD method to acquire the intrinsic mode function (IMF) of fault signal. Then according to correlation coefficient for IMFs and the signal before decomposing by EEMD method, some redundant low frequency IMFs produced in the process of decomposition can be eliminated, then the effective IMF components are selected to perform a local Hilbert marginal spectrum analysis, then fault characteristics are extracted. Through the vibration analysis of inner-race fault bearing it shows that this method can be effectively applied to extract fault characteristics of rolling bearing.

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