An improved decomposition algorithm of surface topography of machining

Substantial work has shown that the modulation of structural damage on probing guided ultrasonic waves can result in wave components corresponding to different frequencies, causing wave energy transfer from central frequency to other frequency bands. To make use of the damage-induced wave energy transfer in different frequency bands, a data-driven method which combines wavelet packet decomposition (WPD), principal component analysis (PCA) and support vector machine (SVM), is proposed in this study for structural damage identification in both metallic and composite materials. Firstly, WPD is employed to decompose the original signal into different frequency bands, based on which the wave energy at each frequency band can be determined. A wave energy distribution vector is constructed according to the energy proportion of each frequency band. Then, PCA is recalled conducting dimensionality reduction for the energy distribution vectors, in order to improve the computational efficiency for subsequent SVM classification. The compressed energy distribution vectors are used as the input to train an SVM-based classifier for identifying structural damage. To validate the proposed WPD-PCA-SVM method, experiments are performed on both aluminum plate and glass fiber reinforced polymer (GFRP) laminate. According to the experimental results, the embryonic fatigue crack in the (aluminum) plate and the anomaly in the GFRP laminate can be identified by the proposed method, with a detection accuracy of 92.86% for aluminum plate and 95.45% for GFRP laminate, respectively, demonstrating the effectiveness of the proposed method for damage detection in both metallic and composite materials.

The research of an effective denoising algorithm for the actual obtained signals with chaotic characteristics is of great interest to all fields of the subject. To show the true chaotic state and analyze the dynamic characteristics of the chaotic system more accurately, an improved denoising algorithm using wavelet packet is proposed in this paper. Wavelet packet decomposition has an optimal sub-band tree structure, which can be used for local analysis of chaotic signals. Based on the correlation function value differences of wavelet packet coefficients, the algorithm determines the optimal decomposition layer, while the optimal wavelet packet basis is obtained with logarithmic energy entropy as the cost function. Furthermore, on the one hand, it makes efforts to divide wavelet packet coefficients into approximate parts, fuzzy parts and detail parts. On the other, it carries out singular spectrum analysis, the fuzzy threshold and the correlation analysis for the select of these three different types of coefficients in order to retain the dynamic performance of chaotic signals in the greatest extent. To verify the effectiveness of the algorithm, the Lorenz chaotic model is employed to analyzed. Simulation results verify the practicability of the improved denoising algorithm, which can also be well applied to various chaotic signals denoising with different noise levels.

On-line detection of chatter in the cutting process can identify the chatter in time or before the chatter happens, so the initiative to change the cutting parameters can be taken to avoid chatter and improve the surface quality. At present, time-frequency analysis technology is performed to extract the time-frequency features of chatter by scholars. With respect to the modal aliasing problem in the process of empirical mode decomposition (EMD), the chatter identification method of combining EMD and wavelet packets decomposition (WPD) is proposed to eliminate the influence of modal aliasing. To fully extract the main features of signal, the intrinsic mode functions (IMFs) changing consistent with power spectrum or amplitude-frequency are selected for signal reconstruction. Then, WPD is used in the reconstructed signal. The two times reconstruction of signal is based on wavelet packet node with the maximum energy. The distribution of frequency and energy in the time domain is presented by Hilbert Huang Transform (HHT) spectrum, and the mean value and standard deviation of the HHT spectrum are extracted as the feature vectors. The chatter features can be extracted from the original simulation signal by this method. Three groups of experiments with different cutting depth which are on behalf of the three cutting conditions (stable cutting, slight chatter, and severe chatter) were carried out. More cutting tests were carried out under the same cutting condition. Experimental results show that this method can be used to effectively identify the chatter features in milling process.

We consider the problem of electrocardiogram (ECG) denoising using source separation. In this study, a hybrid technique using empirical mode decomposition (EMD) and source separation, is proposed. This technique consists of two steps, the first step consists in applying the EMD to two different mixtures. These mixtures are obtained by corrupting in additive manner, the same ECG signal by a white Gaussian noise with two different values of the signal-to-noise ratio (SNR). The second step consists in computing the entropy of each obtained intrinsic mode function (IMF) and finds the two IMFs having the minimal entropy. These two IMFs are used to estimate the separation matrix of the ECG signal from noise by using the source separation. The proposed technique is evaluated by comparing it to the denoising technique based on source separation in time domain using runica and the technique based on Bionic wavelet transform (BWT) and also using source separation. The obtained results from SNR and the mean square error (MSE) computations, show that the proposed technique outperforms the other two techniques used in this evaluation.

Based on empirical mode decomposition (EMD), the background removal and de-noising procedures of the data taken by polarization interference imaging interferometer (PIIS) are implemented. Through numerical simulation, it is discovered that the data processing methods are effective. The assumption that the noise mostly exists in the first intrinsic mode function is verified, and the parameters in the EMD thresholding de-noising methods is determined. In comparison, the wavelet and windowed Fourier transform based thresholding de-noising methods are introduced. The de-noised results are evaluated by the SNR, spectral resolution and peak value of the de-noised spectrums. All the methods are used to suppress the effect from the Gaussian and Poisson noise. The de-noising efficiency is higher for the spectrum contaminated by Gaussian noise. The interferogram obtained by the PIIS is processed by the proposed methods. Both the interferogram without background and noise free spectrum are obtained effectively. The adaptive and robust EMD based methods are effective to the background removal and de-noising in PIIS.

The scalp electroencephalogram (EEG) signal based epileptic seizure analysis has been comprehensively studied in the past. But existing researches are generally concerned with the seizure/non-seizure detection. Few attention has been paid to the epileptic preictal state classification, which is found to be evidently more important to the injury prevention. In this paper, we study the epileptic preictal state classification for seizure prediction. The one hour preictal EEG signal is divided into non-overlapped equilong segments. Statistical features of the first intrinsic mode function (FIMF) of the empirical mode decomposition (EMD) of the EEG signal as well as the 4-level wavelet packet decomposition (WPD) of the FIMF are extracted for the EEG signal representation. A five-state classification problem is formulated, including one interictal, three preictal, and one seizure states. Experiments on the benchmark epilepsy EEG database collected by the Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) using several popular classifiers are provided for the effectiveness demonstration.

With the rapid development of new energy vehicle technologies, higher demands have been placed on fault diagnosis for automotive transmission gearboxes. To address the poor adaptability of traditional methods under complex operating conditions, this paper proposes a sensor data-driven fault diagnosis method based on improved ensemble empirical mode decomposition (EEMD) combined with convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The method incorporates a dynamic noise adjustment mechanism, allowing the noise amplitude to adapt to the characteristics of the signal. This improves the stability and accuracy of signal decomposition, effectively reducing the instability and error accumulation associated with fixed-amplitude white noise in traditional EEMD. By combining the CNN and BiLSTM modules, the approach achieves efficient feature extraction and dynamic modeling. First, vibration signals of the transmission gearbox under different operating states are collected via sensors, and an improved EEMD method is employed to decompose the signals, removing background noise and nonstationary components to extract diagnostically significant intrinsic mode functions (IMFs). Then, the CNN is utilized to extract features from the IMFs, deeply mining their spatiotemporal characteristics, while the BiLSTM captures the temporal sequence dependencies of the signals, enhancing the comprehensive modeling of nonlinear and dynamic fault features. The combination of these two networks enables efficient adaptation to complex conditions, achieving accurate classification and identification of multiple gearbox fault modes. Results indicate that the proposed approach is highly accurate and robust for identifying gearbox fault modes, significantly exceeding the performance of conventional methods and isolated network models. This provides an efficient and intelligent solution for fault diagnosis of automotive transmission gearboxes.

Doppler radar for monitoring vital signals is an emerging tool, and how to remove the noise during the detection process and reconstruct the accurate respiration and heartbeat signals are hot issues in current research. In this paper, a novel radar vital signal separation and de-noising technique based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy (SampEn), and wavelet threshold is proposed. First, the noisy radar signal was decomposed into a series of intrinsic mode functions (IMFs) using ICEEMDAN. Then, each IMF was analyzed using SampEn to find out the first few IMFs containing noise, and these IMFs were de-noised using the wavelet threshold. Finally, in order to extract accurate vital signals, spectrum analysis and Kullback–Leible (KL) divergence calculations were performed on all IMFs, and appropriate IMFs were selected to reconstruct respiration and heartbeat signals. Moreover, as far as we know, there is almost no previous research on radar vital signal de-noising based on the proposed technique. The effectiveness of the algorithm was verified using simulated and measured experiments. The results show that the proposed algorithm could effectively reduce the noise and was superior to the existing de-noising technologies, which is beneficial for extracting more accurate vital signals.

Separation of closely spaced modes by combining complex envelope displacement analysis with method of generating intrinsic mode functions through filtering algorithm based on wavelet packet decomposition

Frequency-modulation continuous-wave (FMCW) Laser radar (Ladar) signals may be polluted by noise, which reduces the recognition precision of the targets. This letter proposes an ensemble empirical modal decomposition (EEMD) denoising method with singular spectrum constraint for FMCW Ladar signals. In our approach, we apply EEMD to adaptively decompose the noisy FMCW Ladar signals into several intrinsic mode functions (IMFs) and decompose these IMFs into several singular value components by using the singular spectrum analysis. The singular values are then used to calculate the energy probability of each IMF, which serves as an indicator to detect the IMFs containing useful signals. The energy probability represents the coherence of the IMFs such that we could sort these IMFs into noisy and useful components according to their coherence differences. To suppress the residual noise that is interfered with the selected IMFs, we use a low-rank approximation to reconstruct the useful signals. Finally, the reconstructed IMFs are stacked together to obtain the denoised signals. Tests on synthetic and real data demonstrate that the proposed method, compared to the EEMD denoising method, could suppress more noise but filter out less useful signals in the FMCW Ladar signal denoising.

Based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) theory, a new adaptive hybrid algorithm for sea clutter denoising is proposed. The chaotic sea clutter signals are decomposed into several intrinsic modal functions (IMF) which start from high-frequency scales to low-frequency scales by using CEEMDAN. According to the relationship between the pretreatment threshold layer and the maximum cross correlation coefficient of the original signal and each IMF corresponding the layer, the proposed algorithm can independently select the wavelet threshold denoising to pretreat. After the pretreatment of original signals, we continue to decompose the signal by CEEMDAN. And later, according to the relationship between the first local minimum corresponding the layers of two adjacent critical IMFs identified by the cross correlation coefficients of the original signal and each IMF, the proposed algorithm adaptively selects the IMFs which need to be filtered. Finally, the IMFs after filtering are reconstructed into a new signal. Rossler, Lorenz system and the measured sea clutter data were selected as examples to study, the result shows that: under the condition of low noise (SNR ≥ 5dB) and high noise (SNR ≤ 0dB), the proposed algorithm can decrease the root mean square error by at least 57% and 72% compared with wavelet threshold denoising etc, the signal to noise ration increased by 3.18-5.64db and 5.73-7.45db. Moreover, the root mean square error after sea clutter signal denoising can be reduced by one order of magnitude, reaching 0.0006147 while the model before denoising only reach 0.0084, which shows that the proposed algorithm is effective for sea clutter signal denoising.

In light of the complex and non-stationary characteristics of misalignment vibration signal, this paper proposed a novel method to analyze in time-frequency domain under different working conditions. Firstly, decompose raw misalignment signal into different frequency bands by wavelet packet (WP) and reconstruct it in accordance with the band energy to remove noises. Secondly, employ empirical mode decomposition (EMD) to the reconstructed signal to obtain a certain number of stationary intrinsic mode functions (IMF). Finally, apply further spectrum analysis on the interested IMFs. In this way, weak signal is caught and dominant frequency is picked up for the diagnosis of misalignment fault. Experimental results show that the proposed method is able to detect misalignment fault characteristic frequency effectively.

This paper investigates the use of advance signal processing techniques for the classification of four common reciprocating compressor conditions. Non-stationary vibration signals from the compressor are decomposed based on WPT (Wavelet Packet Transform) and EMD (Empirical Mode Decomposition) and harmonic changes of the optimal components of each technique is used for condition monitoring and diagnostics of the machine. Performance comparison of WPT and EMD is done for the envelope analysis of the optimal wavelet packet and the IMF (Intrinsic Mode Function) component that can extract salient features of the four compressor conditions including one normal and three fault conditions (intercooler leakage, discharge valve leakage and the combination of intercooler and discharge valve leakage). Harmonic changes produced after envelope analysis of the best packet and IMF component are used for classification and results show that WPT technique is more robust and effective in fault classification compared to EMD.

The performance of tissue imaging is improved by tissue harmonic imaging (THI) to expand and enhance the range and level of clinical diagnosis for many diseases. The separation based on High-pass filtering (S_HPF) is a commonly used method for extracting harmonic components from ultrasonic echo signals. However, the cut-off frequency, order and algorithm to realize a high-pass filter have a great influence on the separation accuracy of harmonic signals. In present study, a novel separation method (S_CEEMDAN) based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is proposed for adaptively separating harmonic components from ultrasonic echo signals. First, we calculate the ensemble size of CEEMDAN adaptively based on the added noise level, and draw a sum of intrinsic mode functions (IMFs) from the ultrasonic echo signals by CEEMDAN. Then, the spectrum of each IMF is computed and evaluated, and the IMFs containing both fundamental and harmonic components are further decomposed by using the CEEMDAN algorithm. This separation process is end until all of IMFs have been divided into either fundamental or harmonic categories. Finally, the corresponding fundamental and harmonic echo signals are yielded by accumulating separately these two categories. In experiments, simulated ultrasonic echo signals with a center frequency of 3.5MHz are separated by the proposed S_CEEMDAN method, and the results are compared with those processed by S_HPF. The edge of the harmonic image by S_CEEMDAN marginally better defined than S_HPF. The indices for the harmonic signals separated by S_CEEMDAN and S_HPF, respectively, are as follows: the center frequencies of 6.66 MHz and 6.65 MHz, 3 dB bandwidths of 1.04 MHz and 1.05 MHz, 6 dB bandwidths of 1.59 MHz and 1.55 MHz, signal-to-noise ratios (SNRs) of 14.00 and 13.63, and image contrasts of 7.01 and 7.06. In conclusion, due to good adaptive characteristics, and lower reconstruction errors, the proposed S_CEEMD method is superior to S_HPF in the performance of spectral accuracy and harmonic imaging. This method could be potentially alternative to the current method for the ultrasonic harmonic separation.

Accurate power load forecasting plays an important role in smart grid analysis. To improve the accuracy of forecasting through the three-level “decomposition–optimization–prediction” innovation, this study proposes a prediction model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the improved sparrow search algorithm (ISSA), a convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM). A series of simpler intrinsic mode functions (IMFs) with different frequency characteristics can be decomposed by CEEMDAN from data, then each IMF is reconstructed based on calculating the sample entropy of each IMF. The ISSA introduces three significant enhancements over the standard sparrow search algorithm (SSA), including that the initial distribution of the population is determined by the optimal point set, the position of the discoverer is updated by the golden sine strategy, and the random walk of the population is enhanced by the Lévy flight strategy. By the optimization of the ISSA to the parameters of the CNN-BiLSTM model, integrating the prediction results of the reconstructed IMFs in the sub-models can obtain the final prediction result of the data. Through the performance indexes of the designed prediction model, the application case results show that the proposed combined prediction model has a smaller prediction error and higher prediction accuracy than the eight comparison models.