Showing papers on "Wavelet packet decomposition published in 2022"

A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm

Abstract: Bearings are widely used in various electrical and mechanical equipment. As their core components, failures often have serious consequences. At present, most parameter adjustment methods are still manual adjustments of parameters. This adjustment method is easily affected by prior knowledge, easily falls into the local optimal solution, cannot obtain the global optimal solution, and requires a lot of resources. Therefore, this paper proposes a new method for bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by a simulated annealing algorithm. Firstly, the original bearing vibration signal is extracted by wavelet packet transform to obtain the spectrogram, and then the obtained spectrogram is sent to the convolutional neural network for parameter adjustment, and finally the simulated annealing algorithm is used to adjust the parameters. To verify the effectiveness of the method, the bearing database of Case Western Reserve University is used for testing, and the traditional intelligent bearing fault diagnosis methods are compared. The results show that the new method for bearing fault diagnosis proposed in this paper has a better and more reliable diagnosis effect than the existing machine learning and deep learning methods.

Effective multi-sensor data fusion for chatter detection in milling process

Abstract: This paper introduces a newly developed multi-sensor data fusion for the milling chatter detection with a cheap and easy implementation compared with traditional chatter detection schemes. The proposed multi-sensor data fusion utilizes microphone and accelerometer sensors to measure the occurrence of chatter during the milling process. It has the advantageous over the dynamometer in terms of easy installation and low cost. In this paper, the wavelet packet decomposition is adopted to analyze both measured sound and vibration signals. However, the parameters of the wavelet packet decomposition require fine-tuning to provide good performance. Hence the result of the developed scheme has been improved by optimizing the selection of the wavelet packet decomposition parameters including the mother wavelet and the decomposition level based on the kurtosis and crest factors. Furthermore, the important chatter features are selected using the recursive feature elimination method, and its performance is compared with metaheuristic algorithms. Finally, several machine learning techniques have been adopted to classify the cutting stabilities based on the selected features. The results confirm that the proposed multi-sensor data fusion scheme can provide an effective chatter detection under industrial conditions, and it has higher accuracy than the traditional schemes.

A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities

Abstract: As a general and rigid mathematical tool, wavelet theory has found many applications and is constantly developing. This article reviews the development history of wavelet theory, from the construction method to the discussion of wavelet properties. Then it focuses on the design and expansion of wavelet transform. The main models and algorithms of wavelet transform are discussed. The construction of rational wavelet transform (RWT) is provided by examples emphasizing the advantages of RWT over traditional wavelet transform through a review of the literature. The combination of wavelet theory and neural networks is one of the key points of the review. The review covers the evolution of Wavelet Neural Network (WNN), the system architecture and algorithm implementation. The review of the literature indicates the advantages and a clear trend of fast development inWNNthat can be combined with existing neural network algorithms. This article also introduces the categories of wavelet-based applications. The advantages of wavelet analysis are summarized in terms of application scenarios with a comparison of results. Through the review, new research challenges and gaps have been clarified, which will serve as a guide for potential wavelet-based applications and new system designs.

Multilevel 2-D Quantum Wavelet Transforms

Abstract: Wavelet transform is being widely used in classical image processing. One-dimension quantum wavelet transforms (QWTs) have been proposed. Generalizations of the 1-D QWT into multilevel and multidimension have been investigated but restricted to the quantum wavelet packet transform (QWPTs), which is the direct product of 1-D QWPTs, and there is no transform between the packets in different dimensions. A 2-D QWT is vital for image processing. We construct the multilevel 2-D QWT's general theory. Explicitly, we built multilevel 2-D Haar QWT and the multilevel Daubechies D4 QWT, respectively. We have given the complete quantum circuits for these wavelet transforms, using both noniterative and iterative methods. Compared to the 1-D QWT and wavelet packet transform, the multilevel 2-D QWT involves the entanglement between components in different degrees. Complexity analysis reveals that the proposed transforms offer exponential speedup over their classical counterparts. Also, the proposed wavelet transforms are used to realize quantum image compression. Simulation results demonstrate that the proposed wavelet transforms are significant and obtain the same results as their classical counterparts with an exponential speedup.

Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM

Abstract: Chatter is one of the biggest unfavorable factors during the high speed machining process of a machine tool. It severely affects the surface finish and geometric accuracy of the workpiece. To address this obstacle and improve the quality and efficiency of products, it is significantly essential to detect chatter during machining. Therefore, a multi-feature recognition system for chatter detection on the basis of the fusion technology of wavelet packet transform (WPT) and particle swarm optimization support vector machine (PSO-SVM) was proposed in this paper. Firstly, the original vibration signals collected from the acceleration sensor were processed through wavelet packet transform (WPT). The noise and the irrelevant information were remarkably decreased. In addition, the wavelet packets containing chatter-emerging information were chosen and reconstructed. The fourteen time–frequency domain characteristics of the reconstructed vibration signal were calculated and chosen as the multi-feature vectors of chatter detection. Finally, to obtain the optimal radial basis function parameter g and penalty parameter C of the SVM prediction model, the optimization algorithms of k-fold cross-validation (k-CV), genetic algorithm (GA), and particle swarm optimization (PSO) were employed in optimizing the model parameters of SVM. It was indicated that the PSO-SVM improved obviously the accuracy of chatter recognition than the others. In addition, we applied the optimized SVM prediction model by PSO for detecting chatter state in end milling machining. Chatter recognition results indicated that the model accurately predicted the slight chatter state in advance.

Feature Extraction of Ship Radiation Signals Based on Wavelet Packet Decomposition and Energy Entropy

Abstract: The analysis of ship radiation signals to identify ships is an important research content of underwater acoustic signal processing. The traditional fast Fourier transform (FFT) is not suitable for analyzing non-stationary, non-Gaussian, and nonlinear signal processing. In order to realize the feature extraction and accurate classification of ship radiation signals with higher accuracy, a feature extraction method of ship radiation signals based on wavelet packet decomposition and energy entropy is proposed in this paper. According to wavelet packet decomposition, the ship radiation signal is decomposed into different frequency bands, and its energy entropy feature is extracted. As for comparisons, the center frequency and permutation entropy are also used as features to be extracted, then the k-nearest neighbor is applied to classify and recognize the extracted results. Based on the comparisons of wavelet packet decomposition, the center frequency, permutation entropy, and the k-nearest neighbor are used for classification and recognition. The experimental results present that, when comparing with center frequency and permutation entropy, the method based on energy entropy has the best availability, with the highest average recognition rate for four types of ship radiation signals, up to 98%.

A Fault Feature Extraction Method Based on LMD and Wavelet Packet Denoising

Abstract: Aiming at the problem of fault feature extraction of a diaphragm pump check valve, a fault feature extraction method based on local mean decomposition (LMD) and wavelet packet transform is proposed. Firstly, the collected vibration signal was decomposed by LMD. After several amplitude modulation (AM) and frequency modulation (FM) components were obtained, the effective components were selected according to the Kullback-Leible (K-L) divergence of all component signals for reconstruction. Then, wavelet packet transform was used to denoise the reconstructed signal. Finally, the characteristics of the fault signal were extracted by Hilbert envelope spectrum analysis. Through experimental analysis, the results show that compared with other traditional methods, the proposed method can effectively overcome the phenomenon of mode aliasing and extract the fault characteristics of a check valve more effectively. Experiments show that this method is feasible in the fault diagnosis of check valve.

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis

Abstract: The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average and values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average (16.55 dB, utilizing db1 wavelet packet) and largest average (41.40%, using fk8 wavelet packet). The highest average and using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both and values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.

Wavelet packet and fuzzy logic theory for automatic fault detection in induction motor

Abstract: In this paper, a method based on the application of the fuzzy logic technique to diagnose the fault of broken rotor bars in an induction machine has been proposed. Through the decomposition into a wavelet packet, we can detect, identify and prognosis failures in all operating conditions of the machine. The energy calculations for each level of decomposition are richer with the necessary information for fault diagnosis. The latter can be used as input to an intelligent diagnostic system based on fuzzy logic for the detection and classification of the broken bars faults. The advantage of this method is the use of a single current sensor. Indeed, we can detect online, the fault and the number of broken bars with a variable load. The obtained results are very satisfactory. Some results were verified by simulations under MATLAB/Simulink and validated experimentally via dSPACE 1104 card.

Reweighted-Kurtogram with sub-bands rearranged and ensemble dual-tree complex wavelet packet transform for bearing fault diagnosis

Abstract: The Fast Kurtogram (FK) is a widely used resonance demodulation technique for bearing fault diagnosis. In this paper, a novel method termed Reweighted-Kurtogram with sub-bands rearranged and ensemble dual-tree complex wavelet packet transform (SRE-DTCWPT) is proposed to improve the performance of the FK from the aspects of band division and optimal band selection indicator. To obtain an excellent band division, the SRE-DTCWPT is first developed. It retains the main advantages of DTCWPT and meanwhile addresses the two key issues of frequency sub-bands disorder and frequency bands leakage. Then, a new robust evaluating indicator called reweighted kurtosis is defined. It solves the problem of kurtosis being sensitive to strong impulse interferences. Furthermore, the proposed method involves a set of envelope analysis approaches developed on different cases of fault signals to realize the enhanced identification of the bearing diagnostic information. Two simulated signals and actual bearing signals regarding different practical cases are employed to investigate the effectiveness of the proposed method. In addition, the proposed method is compared with the FK, and the results verify that the proposed method shows high potentials for extracting bearing diagnostic information from complex vibration signals.

Image classification using convolutional neural network with wavelet domain inputs

Abstract: Commonly used convolutional neural networks (CNNs) usually compress high-resolution input images. Although it reduces the computation requirements into a reasonable range, the downsampling operation causes information loss, which affects the accuracy of image classification. How to adopt high-resolution image inputs to improve the quality of input information and thus improve the classification accuracy without changing the overall structure of the pre-defined CNN model or increasing the model parameters is an important issue. Here, a CNN model with wavelet domain inputs is proposed to provide a solving scheme. Specifically, the proposed method applies wavelet packet transform or dual-tree complex wavelet transform to extract information from input images with higher resolutions in the image pre-processing stage. Some subband image channels are selected as the inputs of conventional CNNs where the first several convolutional layers are removed, so that the networks directly learn in the wavelet domain. Experiment results on the Caltech-256 dataset and the Describable Textures Dataset with the ResNet-50 show that the classification accuracy of our method can have a maximum improvement of 2.15% and 10.26%, respectively. These validate the effectiveness of our proposed scheme. This code is publicly available at https://github.com/BeBeBerr/wavelet-cnn.

Investigation of the performance of different wavelet-based fusions of SAR and optical images using Sentinel-1 and Sentinel-2 datasets

Abstract: In this study, the fusion of optical and synthetic aperture radar images with wavelet transform was investigated. Images are obtained from Sentinel-1 and sentinel-2 satellites. Images were decomposed by wavelet transform. The four main coefficients were obtained for different wavelet packages and up to ten decomposition levels. The coefficients were combined taking the maximum, minimum or mean. 1710 Fused images were obtained for all possible combinations in terms of different wavelet packets, decomposition levels and fusion rules. Fused images were evaluated according to the structural similarity index (SSI). It was seen that the missing regions in the optical images were improved in the fused images with the appropriate wavelet packets and highest SSI.

Lung sound classification using wavelet transform and entropy to detect lung abnormality

Abstract: Lung sounds provide essential information about the health of the lungs and respiratory tract. They have unique and distinguishable patterns associated with the abnormalities in these organs. Many studies attempted to develop various methods to classify lung sounds automatically. Wavelet transform is one of the approaches widely utilized for physiological signal analysis. Commonly, wavelet in feature extraction is used to break down the lung sounds into several sub-bands before calculating some parameters. This study used five lung sound classes obtained from various sources. Furthermore, the wavelet analysis process was carried out using Discrete Wavelet Transform (DWT) and Wavelet Package Decomposition (WPD) analysis and entropy calculation as feature extraction. In the DWT process, the highest accuracy obtained was 97.98% using Permutation Entropy (PE), Renyi Entropy (RE), and Spectral Entropy (SEN). In WPD, the best accuracy achieved is 98.99 % when 8 sub-bands and RE are used. These results are relatively competitive compared with previous studies using the wavelet method with the same datasets.

Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features

Abstract: In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. Generally, the classical feature extraction techniques are sensitive to the noisy component of the signal and need more time for training. To deal with these issues, a comparatively new feature extraction technique, referred to as a wavelet scattering transform (WST) is utilized, and incorporated with ML classifiers to design a framework for bearing fault classification in this paper. The WST is a knowledge-based technique, and the structure is similar to the convolution neural network. This technique provides low-variance features of real-valued signals, which are usually necessary for classification tasks. These signals are resistant to signal deformation and preserve information at high frequencies. The current signal data from a publicly available dataset for three different bearing conditions are considered. By combining the scattering path coefficients, the decomposition coefficients from the 0th and 1st layers are considered as features. The experimental results demonstrate that WST-based features, when used with ensemble ML algorithms, could achieve more than 99% classification accuracy. The performance of ANN models with these features is similar. This work exhibits that utilizing WST coefficients for the motor current signal as features can improve the bearing fault classification accuracy when compared to other feature extraction approaches such as empirical wavelet transform (EWT), information fusion (IF), and wavelet packet decomposition (WPD). Thus, our proposed approach can be considered as an effective classification method for the fault diagnosis of rotating machinery.

Improved Brain–Computer Interface Signal Recognition Algorithm Based on Few-Channel Motor Imagery

Abstract: Common spatial pattern (CSP) is an effective algorithm for extracting electroencephalogram (EEG) features of motor imagery (MI); however, CSP mainly aims at multichannel EEG signals, and its effect in extracting EEG features with fewer channels is poor—even worse than before using CSP. To solve the above problem, a new combined feature extraction method has been proposed in this study. For EEG signals from fewer channels (three channels), wavelet packet transform, fast ensemble empirical mode decomposition, and local mean decomposition were used to decompose the band-pass filtered EEG into multiple time–frequency components, and the corresponding components were selected according to the frequency characteristics of MI or the correlation coefficient between its time–frequency components and the original EEG signal. Furthermore, phase space reconstruction (PSR) was performed on the selected components after the three time-frequency decompositions, the maximum Lyapunov index was calculated, and the features were reconstructed; then, CSP projection mapping was used for the reconstructed features. The support vector machine probability output model was trained by the obtained three mappings. Probability outputs by three different support vector machines were then obtained. Finally, the classification of test samples was determined by the fusion of the Dempster–Shafer evidence theory at the decision level. The results showed that the accuracy of the proposed method was 95.71% on data set III of BCI competition II (left- and right-hand MI), which was 2.88% higher than the existing methods. On data set IIb of BCI competition IV, the average accuracy was 86.60%, which was 2.3% higher than the existing methods. This study verified the effectiveness of the proposed method and provided an approach for the research and development of the MI-BCI system based on fewer channels.

Intelligent Fault Diagnosis of Rotating Machines Based on Wavelet Time-Frequency Diagram and Optimized Stacked Denoising Auto-Encoder

Abstract: When a stacked denoising auto-encoder (SDAE) manually sets several parameters, the gradient of neuron weight becomes dispersed, reducing the ability to retrieve sensitive fault feature information from a bearing vibration signal under multiple working conditions and strong noise. A bearing health monitoring and defect diagnostic model based on variational mode decomposition (VMD) combined with continuous wavelet transform (CWT) and SDAE optimized by sparrow search algorithm (SSA) is presented to tackle this problem. The wavelet time-frequency diagram is obtained by VMD and CWT, which maps the fault characteristic information to different local positions in time and scale, and then the wavelet time-frequency diagrams are input into the SDAE for in-depth training. To achieve the ideal structure of SDAE and increase the feature extraction capabilities of SDAE for weak signals, SSA is utilized for the global combination and adaptive selection of several SDAE parameters. The bearing failure diagnostic model based on VMD-CWT-SSA-SDAE outperforms BPNN, SVM, the traditional SDAE, GA-SDAE, PSO-SDAE, and SSA-DBN in diagnosis accuracy, generalization performance, and anti-noise performance when tested on various data sets.

Early Fault Warning and Identification in Condition Monitoring of Bearing via Wavelet Packet Decomposition Coupled With Graph

Abstract: It has been well known that the fault of rolling element bearings (REBs) is one of the biggest causes for machine breakdown; thus, the early detection and type identification of a fault during REBs successive operations is necessary to help users take predictive maintenance action and/or schedule in order to avoid major machine failures. Wavelet packet decomposition (WPD) is a widely used technique to analyze vibration signal for health monitoring of REBs, but the resulting wavelet packet coefficients (WPCs) are still in high dimensionality, making them impossible for the direct use in practical usage. Keeping along the research line of high-level analysis for WPD enhancement, this article presents a new dynamic modeling approach, called graph-modeled wavelet packet coefficients (GMWPCs), that integrates WPD and graph theory in order to extract the correlation information between WPCs. By means of an adaptive input weight fusion, the GMWPCs can automatically confirm the weight of the frequency sub-band where the fault-induced information is more evident. By virtue of GMWPCs, a new two-phase framework for early warning detection and fault identification is proposed finally, which is able to not only detect the time location of the fault in its very early stage but also identify the fault type. We conducted different experiments to validate the early warning detection and fault identification separately. Experimental results, outperforming the state of the arts, verify the adequacy and effectiveness of the proposed framework and meanwhile reveal its appropriate use for real engineering applications.

A fault diagnosis method for electric vehicle power lithium battery based on wavelet packet decomposition

Abstract: This paper proposes a fault diagnosis method for electric vehicle power lithium battery based on wavelet packet decomposition. Firstly, the original voltage signal is decomposed into the low-frequency part and high-frequency part based on wavelet packet decomposition. For the high-frequency part, after filtering the noise using wavelet packet energy noise reduction method, the time domain voltage signal is obtained by wavelet packet reconstruction, and the characteristic parameters reflecting the battery fault are extracted by using sparse autoencoder; for the low-frequency part, the characteristic parameters reflecting the battery inconsistency are obtained by using singular value decomposition. The similarity between each individual cell and the average cell is then measured using the discrete Fréchet distance algorithm. Finally, the outlier detection method based on the Chauvenet criterion is used to detect the faulty cells using the obtained curve similarity. The effectiveness of the proposed method is verified by the data of two thermal runaway vehicles. • The original voltage signal is processed by wavelet packet decomposition. • Feature extraction using autoencoder and singular value decomposition. • The similarity between feature parameters is calculated by Discrete Fréchet distance. • The outlier filter is designed based on the Chauvenet criterion.

A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing

Abstract: Wavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars’ research interest and produced many effective strategies. However, with the convenience of seismic data acquisition, the existing wavelet selection methods are unsuitable for the big dataset. Therefore, we proposed a novel wavelet selection method considering the big dataset for seismic signal intelligent processing. The relevance r is calculated using the seismic waveform’s correlation coefficient and variance contribution rate. Then values of r are calculated from all seismic signals in the dataset to form a set. Furthermore, with a mean value μ and variance value σ2 of that set, we define the decomposition stability w as μ/σ2. Then, the wavelet that maximizes w for this dataset is considered to be the optimal wavelet. We applied this method in automatic mining-induced seismic signal classification and automatic seismic P arrival picking. In classification experiments, the mean accuracy is 93.13% using the selected wavelet, 2.22% more accurate than other wavelets generated. Additionally, in the picking experiments, the mean picking error is 0.59 s using the selected wavelet, but is 0.71 s using others. Moreover, the wavelet packet decomposition level does not affect the selection of wavelets. These results indicate that our method can really enhance the intelligent processing of seismic signals.

Damage Severity Quantification Using Wavelet Packet Transform and Peak Picking Method

Abstract: This article aims to present the results of an investigation on detection and quantification of damage location and severity for steel structures using wavelet packet transform for denoisin...