Single Intrinsic Mode Function Research Articles

An adaptive chatter detection method for micro milling based on variational mode extraction

Current chatter detection methods using variational mode decomposition (VMD) hardly select appropriate intrinsic mode functions (IMFs) for characterizing chatter in micro milling. This article introduces a novel variational mode extraction (VME) method to isolate a single IMF representing chatter. An adaptive filter removes chatter-independent components, enabling VME to identify the desired IMF with adaptive calculations for center frequency and penalty factor. The Hilbert-Huang transform is then applied to obtain the time-frequency distribution of the IMF, and various features are computed. Finally, two methods are employed: streaming feature selection considers feature interaction, and radial basis function support vector machine selects pertinent features to establish the chatter detection model. Extensive micro milling tests validate the method, achieving an average detection accuracy of 99.25%.

Short Term Wind Forecasting Using Machine Learning Models with Noise Assisted Data Processing Method

In order to address the complex and intermittent nature of wind, this thesis proposes an innovative approach to enhance the accuracy of short-term wind forecasting. By leveraging the strengths of different methods while mitigating their weaknesses, a robust hybrid model is developed. The methodology incorporates Empirical Mode Decomposition (EMD), a data-adaptive denoising technique, to break down the signals into meaningful components called Intrinsic Mode Functions (IMFs), along with a residue. However, EMD is known to suffer from the mode mixing problem, where different scales of signals are erroneously mixed within IMFs, causing signal intermittency. To overcome this challenge, Ensemble Empirical Mode Decomposition is introduced, utilizing an ensemble of white noise to establish a uniform reference frame in the time-frequency space. By doing so, the added noise effectively collates signal portions with similar scales into a single IMF. Subsequently, the IMFs and residue obtained from both EMD and Ensemble Empirical Mode Decomposition are fed into a Convolutional Neural Network (CNN). The performance of this hybrid model is then compared against benchmark models such as Bi LSTM and LSTM. Evaluation is conducted based on two critical factors: performance metrics including MSE, MAE, RMSE, MAPE, and loss, as well as the time required for testing. Through a comprehensive analysis of these factors, the superior performance of the hybrid model is determined, thereby enhancing the prospects of reliable wind forecasting.

A Damage Modes Extraction Method from AE Signal in Composite Laminates Based on DEEMD

An extraction method of damage modes in composite laminates from acoustic emission (AE) signal based on ensemble empirical mode decomposition (EEMD) and decorrelation algorithm is proposed. Based on previous works about damage modes identification of composite laminates using AE, the peak frequency of AE signal could distinguish various damage modes effectively. Energy generated by several damage modes can be included in a single AE signal, the empirical mode decomposition (EMD) could decompose AE signal into a set of intrinsic mode functions (IMFs), the peak frequency of each IMF was chosen to identify damage modes, but the analysis show that a single IMF would include components of dramatically disparate scales. Based on the above problems, the decorrelation ensemble empirical mode decomposition (DEEMD) method is put forward, DEEMD is the more effective solution for extracting all damage modes existing in a single AE signal than EMD, can eliminate mode mixing, would provide practical application guidance for composite laminates structure damage monitoring using AE.

Epileptic Seizure Detection Based on Bandwidth Features of EEG Signals

Detection of seizure from EEG signals may help neurologist analyze the condition of epileptic patients. Variance in types of epileptic seizures provides challenge in recognizing the pattern of seizure signals from normal ones. In this paper, we discuss the classification of seizure and non-seizure conditions in epilepsy based on bandwidth features of EEG signals. These features were extracted using Empirical Mode Decomposition (EMD). This method decomposes signal into eight Intrinsic Mode Functions (IMFs) and its residue. The IMFs is then analyzed by Hilbert transform to obtain amplitude modulation bandwidth (BAM) and frequency modulation bandwidth (BFM). These features are fed as input to Support Vector Machine (SVM). We propose to combine the first four IMFs to extract bandwidth features and we implement this method on two data sets, public data set that contains signals from both extracranial and intracranial EEG, and data set OF Rumah Sakit Universitas Airlangga (RSUA) that contains signals from extracranial EEG only. The results showed that in general, the values of accuracy and specificity using combined features of first four IMFs outperformed those using features of single IMF, for all SVM kernel functions. The best accuracy and specificity on public data set were 97.3% and 98.25% respectively using RBF kernel, while the best sensitivity was gained using polynomial kernel based on only IMF1 bandwidth features by 99.0%. The experiments on RSUA data showed that the best accuracy was 96.5% and the best specificity was 99.38% when using Mexican Hat kernel based on combined features of first four IMFs, while the best sensitivity was 94% when using polynomial kernel based on only IMF1 bandwidth features.

Wavelet-bounded empirical mode decomposition for measured time series analysis

Abstract Empirical mode decomposition (EMD) is a powerful technique for separating the transient responses of nonlinear and nonstationary systems into finite sets of nearly orthogonal components, called intrinsic mode functions (IMFs), which represent the dynamics on different characteristic time scales. However, a deficiency of EMD is the mixing of two or more components in a single IMF, which can drastically affect the physical meaning of the empirical decomposition results. In this paper, we present a new approached based on EMD, designated as wavelet-bounded empirical mode decomposition (WBEMD), which is a closed-loop, optimization-based solution to the problem of mode mixing. The optimization routine relies on maximizing the isolation of an IMF around a characteristic frequency. This isolation is measured by fitting a bounding function around the IMF in the frequency domain and computing the area under this function. It follows that a large (small) area corresponds to a poorly (well) separated IMF. An optimization routine is developed based on this result with the objective of minimizing the bounding-function area and with the masking signal parameters serving as free parameters, such that a well-separated IMF is extracted. As examples of application of WBEMD we apply the proposed method, first to a stationary, two-component signal, and then to the numerically simulated response of a cantilever beam with an essentially nonlinear end attachment. We find that WBEMD vastly improves upon EMD and that the extracted sets of IMFs provide insight into the underlying physics of the response of each system.

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Morphological operation-based bi-dimensional empirical mode decomposition for automatic background removal of fringe patterns

A modified bi-dimensional empirical mode decomposition (BEMD) method is proposed for sparsely decomposing a fringe pattern into two components, namely, a single intrinsic mode function (IMF) and a residue. The main idea of this method is a modified sifting process which employs morphological operations to detect ridges and troughs of the fringes, and uses weighted moving average algorithm to estimate envelopes of the IMF, replacing respective local extrema detection and envelope interpolation of conventional BEMDs. The background intensity of the fringe pattern is automatically removed by extracting the single IMF, thereby relieving the mode mixing problem of the BEMDs. A fast algorithm based on 2D convolution is also presented for reducing the calculation time to several seconds only. This approach is applied to process simulated and real fringe patterns, and the results obtained are compared with Fourier transform, discrete wavelet transform, and other EMD methods. The MATLAB code is downloadable at http://gr.xjtu.edu.cn/web/zhouxiang.

Separating Nonstationary Powerline Interference from ECG Using Empirical Mode Decomposition

Powerline interference (PLI) is a significant source of noise in Electrocardiogram (ECG). It often exhibits variations in frequency and amplitude along with relatively lower level than that of ECG signal in battery-operated ECG system, whose separation remains a challenging task. The use of masking signal-aided empirical mode decomposition is presented to deal with this problem in this study. Simulation results show that our method can effectively decompose the time-varying PLI into a single intrinsic mode function (IMF) at various interference levels.

Hoist Machinery Fault Monitoring Based on HHT-DDKICA and Support Vector Data Description Method

The shortcomings of the existing feature extraction and machine fault detection methods are analyzed.Combining HHT-DDKICA with support vector data description(SVDD) method,a new fault monitoring algorithm for hoist machine is proposed.Vibration signals of hoist machine are filtered into multiple interesting frequency bands and intrinsic mode functions(IMFs) are obtained through empirical mode decomposition(EMD).Then HHT denoising method and a signal energy criterion are adopted to select effective IMFs.Since single IMF may consist of some nonlinear vibration sources,an alternative data dependent kernel independent component analysis(DDKICA) method is presented to separate source signals.The method can determine a proper kernel function according to training samples,and the optimal model parameter can be also achieved by solving a DDKICA model selection criterion in the empirical feature space.Considering distributions of features extracted by DDKICA,SVDD is adopted to extablish new statistics and confidence limits.Hoist machinery application shows the efficiency of the proposed method.

Ensemble Empirical Mode Decomposition: Image Data Analysis with White-noise Reflection

During the last decade, Zhaohua Wu and Norden E. Huang announced a new improvement of the original Empirical Mode Decomposition method (EMD). Ensemble Empirical Mode Decomposition and its abbreviation EEMD represents a major improvement with great versatility and robustness in noisy data filtering. EEMD consists of sifting and making an ensemble of a white noise-added signal, and treats the mean value as the final true result. This is due to the use of a finite, not infinitesimal, amplitude of white noise which forces the ensemble to exhaust all possible solutions in the sifting process. These steps collate signals of different scale in a proper intrinsic mode function (IMF) dictated by the dyadic filter bank. As EEMD is a time–space analysis method, the added white noise is averaged out with a sufficient number of trials. Here, the only persistent part that survives the averaging process is the signal component (original data), which is then treated as the true and more physically meaningful answer. The main purpose of adding white noise was to provide a uniform reference frame in the time–frequency space. The added noise collates the portion of the signal of comparable scale in a single IMF. Image data taken as time series is a non-stationary and nonlinear process to which the new proposed EEMD method can be fitted out. This paper reviews the new approach of using EEMD and demonstrates its use on the example of image data analysis, making use of some advantages of the statistical characteristics of white noise. This approach helps to deal with omnipresent noise.