Automatic Interference Term Retrieval From Spectral Domain Low-Coherence Interferometry Using the EEMD-EMD-Based Method

This paper proposes a novel fault classification method with application to induction motors, which is based on integrating and combining with receiver operating characteristic (ROC) curve and tdistribution stochastic neighbor embedding (t-SNE). According to the feature selection methods of ReliefF, symmetrical uncertainty (SU), and fast correlation-based filter (FCBF), the significant features were verified. Additionally, support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT) are also considered as classifiers to identify the simulation results. To begin with, the current signals obtained from distinctive four topologies of working conditions of the motor, which includes healthy, bearing damage, broken rotor bar, and short circuit in stator windings, respectively. The potential feature set is extracted by using Hilbert-Huang transform (HHT) technique. Then, three feature selection methods are adopted to select three optimal feature subsets from the original feature set. Finally, the classification accuracy (ACC) and ROC curve are used to demonstrate the capability of classifiers' recognition. The results showed that the optimal feature subsets significantly reduce the number of selected features and improve the classification ACC and area under the curve (AUC) compared with the original feature set. In conclusion, the proposed method can downgrade the data, demonstrate the scatter plot more intuitively, and identify various types of faults, unlike with other fault diagnosis literature.

show abstract

“…This analysis method has better results for unstable or nonlinear signals. It mainly focuses on the following steps [25], [26]:…”

Section: A Hilbert-huang Transformmentioning

confidence: 99%

Induction Motor Fault Classification Based on ROC Curve and t-SNE

Lee

Lin

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…EEMD will separate the noise in different IMF from the original signal components [34], thus eliminating the noise-mode mixing phenomenon. In recent years, the application of EEMD has attracted the attention of many researchers and scholars [27,[35][36][37][38][39][40][41][42][43][44]. In order to solve the problem that noise in practical applications makes interference term retrieval difficult, Zhang et al [35] proposed a technique based on EEMD and EMD to achieve automatic interference term retrieval from the spectral domain low-coherence interferometry.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

Yang

Xiao

2020

Complexity

View full text Add to dashboard Cite

The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value.

show abstract

“…The received signals were processed based on empirical mode decomposition (EMD) [12]. This method has been used to analyze non-stationary and nonlinear signals [13]. EMD adaptively decomposes a signal into a series of intrinsic mode functions (IMFs) in descending order of frequency and a residual trend mode [13].…”

Section: Introductionmentioning

confidence: 99%

“…This method has been used to analyze non-stationary and nonlinear signals [13]. EMD adaptively decomposes a signal into a series of intrinsic mode functions (IMFs) in descending order of frequency and a residual trend mode [13]. The IMFs and residual trend mode can be used to represent the noise which can then be deleted, resulting in an improved SNR [14].…”

Section: Introductionmentioning

confidence: 99%

A novel time of arrival estimation algorithm using an energy detector receiver in MMW systems

Liang

Zhang

Lyu

et al. 2017

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

This paper presents a new time of arrival (TOA) estimation technique using an improved energy detection (ED) receiver based on the empirical mode decomposition (EMD) in an impulse radio (IR) 60 GHz millimeter wave (MMW) system. A threshold is employed via analyzing the characteristics of the received energy values with an extreme learning machine (ELM). The effect of the channel and integration period on the TOA estimation is evaluated. Several well-known ED-based TOA algorithms are used to compare with the proposed technique. It is shown that this ELM-based technique has lower TOA estimation error compared to other approaches and provides robust performance with the IEEE 802.15.3c channel models.

show abstract

Automatic Interference Term Retrieval From Spectral Domain Low-Coherence Interferometry Using the EEMD-EMD-Based Method

Cited by 6 publications

References 21 publications

Induction Motor Fault Classification Based on ROC Curve and t-SNE

Induction Motor Fault Classification Based on ROC Curve and t-SNE

A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

A novel time of arrival estimation algorithm using an energy detector receiver in MMW systems

Contact Info

Product

Resources

About