Wavelet Packet Energy Entropy Research Articles

The feature extraction method based on quadratic wavelet packet energy entropy and t-SNE for bearing fault diagnosis

The feature extraction method based on quadratic wavelet packet energy entropy and t-SNE for bearing fault diagnosis

Fault diagnosis of axial movement for harmonic drive based on deep belief network by using current data of driving servomotor

PurposeHarmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the common faults of a harmonic drive is the axial movement of the input shaft. In such a case, its input shaft moves in the axial direction relative to the body of the harmonic drive. The purpose of this study is to propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives.Design/methodology/approachIn the two proposed fault diagnosis methods, the wavelet threshold algorithm is firstly used for filtering noises of the motor current signal. Then, the feature of the denoised current signal is extracted by the empirical mode decomposition (EMD) method and the wavelet packet energy-entropy (WPEE) theory, respectively, obtaining two kinds of feature sets. After a deep learning model based on the deep belief network (DBN) is constructed and trained by using these feature sets, we finally identify the normal harmonic drives and the ones with the axial movement fault.FindingsIn contrast to the traditional back propagation (BP) neural network model and support vector machine (SVM) model, the fault diagnosis methods based on the combination of the EMD (as well as the WPEE) and the DBN model can obtain higher accuracy rates of fault diagnosis for axial movement of harmonic drives, which can be greater than or equal to 97% based on the data of the performed experiment.Originality/valueThe authors propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives, which are verified by the experiment. The presented study may be beneficial for the development of self-diagnosis and self-repair systems of different robots and precision machines using harmonic drives.

Research on wavelet packet energy entropy extraction method for acoustic signal of tunnel lining cavity

Abstract Tunnel lining cavity is a common disease in high-speed railway tunnels, which seriously affects the safe operation of the line.At present, the commonly used method is manual knocking inspection, but it relies on manual judgment, subjective factors have great influence, and there are errors. This paper proposes a feature extraction method of acoustic vibration signal of tunnel lining cavity based on wavelet packet energy entropy and uses wavelet packet energy entropy to realize the identification of cavity defects.The full-scale simulation model of tunnel with a radius of 6m was constructed by COMSOL and different cavity conditions were set up. The wavelet packet energy entropy characteristics of acoustic signals were extracted and analyzed. The mapping relationship between cavity depth and cavity size and wavelet packet energy entropy was explored. The wavelet packet energy entropy and frequency band distribution characteristics were extracted to realize the identification of cavity defects.The local model which can characterize the whole tunnel is designed and the embedded defects are verified by experiments. The results show that the wavelet packet energy entropy can be used as the identification feature of tunnel lining cavity defects. When the wavelet packet energy entropy is less than 2.53, the existence of tunnel lining cavity defects is characterized.When the wavelet packet energy entropy is in the range of 2.53-2.94, it characterizes the existence of the transition boundary between tunnel lining cavity defect and no disease.This index can be used to realize the rapid and effective identification of cavity defects.

Predicting Screening Efficiency of Probability Screens Using KPCA‐GRNN with WP‐EE Feature Reconstruction

The screening system is a nonlinear and non‐Gaussian complex system. To better characterize its attributes and improve the prediction accuracy of screening efficiency, this study involves the acquisition of the vibration signals and screening efficiency data under various operational conditions. Subsequently, empirical mode decomposition energy entropy (EMD‐EE), variational mode decomposition energy entropy (VMD‐EE), and wavelet packet energy entropy (WP‐EE) features are extracted from the time series vibration signals, and three single input energy entropy‐generalized regressive neural network (GRNN) prediction accuracy models are established and compared. Furthermore, we introduce the kernel principal component analysis (KPCA)‐WP‐EE feature reconstruction‐GRNN prediction algorithm. This approach involves reconstructing the feature vector by optimizing WP‐EE‐GRNN prediction under varying parameters. The parameterized GRNN model is then predicted and analyzed through secondary reconstruction involving KPCA dimensionality reduction features. The results show that WP‐EE‐GRNN achieves superior prediction accuracy compared to box dimension (d)‐GRNN, box dimension‐back propagation neural network (BPNN), and d‐weighted least squares support vector machine, WP‐d‐GRNN, WP‐EE‐BPNN, EMD‐EE‐GRNN, and VMD‐EE‐GRNN. Additionally, the WP‐EE feature reconstruction‐GRNN algorithm exhibits higher prediction accuracy than the single‐input WP‐EE‐GRNN algorithm. The WP‐EE‐GRNN prediction algorithm using KPCA dimensionality reduction and secondary reconstruction not only achieves higher prediction accuracy than prior to KPCA dimensionality reduction but also improves prediction efficiency. Following the extraction of two core principal components, model parameters when KPCA’s σ2 = 0.85, the optimal parameter of GRNN model Spread = 0.051, and the optimal number of training samples N = 19, the average prediction error is 1.434%, the minimum prediction error reaching 0.708%, the minimum root mean square error reaching 0.836% and Pearson correlation coefficient marking the closest to 1, these result all representing the optimum achievable values. The budget model selects the optimal parameter combination scheme for the system.

Life prediction of vehicle carbon fibre ceramic brake pads based on wavelet packet energy entropy

Life prediction of vehicle carbon fibre ceramic brake pads based on wavelet packet energy entropy

Life prediction of vehicle carbon fibre ceramic brake pads based on wavelet packet energy entropy

Life prediction of vehicle carbon fibre ceramic brake pads based on wavelet packet energy entropy

Automatic classification of weld defects from ultrasonic signals using WPEE-KPCA feature extraction and an ABC-SVM approach

The classification of weld defects is very important for the safety assessment of welded structures and feature extraction of ultrasonic defect signals is vital for defect classification. A novel approach based on wavelet packet energy entropy (WPEE) and kernel principal component analysis (KPCA) feature extraction and an artificial bee colony optimisation support vector machine (ABC-SVM) classifier is proposed in this paper. Firstly, the WPEE method is adopted to extract ultrasonic signal features of weld defects and KPCA is used for feature selection. Secondly, an ABC-SVM classifier is employed to perform defect classification. Finally, experiments involving defect feature extraction, selection and classification are carried out using four types of weld defect. The results demonstrate that the performance of the proposed feature extraction method based on WPEE is superior to that of wavelet packet energy (WPE). In addition, the WPEE-KPCA method achieved a higher accuracy rate of defect classification than WPEE.

Fault diagnosis of H-bridge cascaded five-level inverter based on improved support vector machine with gray wolf algorithm

The reliability of power electronics is very important to the safety and efficiency operation of the multilevel inverters, and it is very necessary to quickly detect and locate the faults. To solve the difficulty in locating open-circuit faults and improve the diagnosis accuracy for the H-bridge cascaded five-level inverters, an improved support vector machine (SVM) with gray wolf (GWO) optimization method has been elaborated and proposed in this paper especially for single switch faults and double switches faults in one phase. Firstly, the output voltage signals are selected as the sampling data, and whose eigenvectors have been taken as the input signals of the wavelet packet energy entropy method, and the dimensionality of these extracted eigenvectors is furtherly reduced with a probabilistic principle component analysis (PPCA) method to reduce the calculations and improve the diagnosis efficiency. Then, the SVM method is used for the fault classification, during which the GWO optimization has been introduced to find the optimal value of SVM, so as to improve the diagnostic efficiency. Experimental results show that this diagnosis method can not only achieve higher diagnostic accuracy, smaller identification error, and has greater efficiency compared with ordinary SVM or some other traditional diagnosis methods and other intelligent optimization algorithms, which promotes the fault diagnosis research for these multilevel inverters.

UWPEE: Using UAV and wavelet packet energy entropy to predict traffic-based attacks under limited communication, computing and caching for 6G wireless systems

The development of 6G has enhanced the communication capabilities of Internet of Things (IoT) devices. However, in wireless system, due to cost constraints, only a few devices have the communication capability of 6G, and the rest can only communicate with the Internet through self-organized networks. Due to simple hardware, these IoT devices have low capacities of communication, computing and caching. And they are vulnerable to all kinds of attacks. One of harmful attacks is traffic-based attack such as on–off attack, Denial of Service (DoS) attack which consumes the limited energy of IoT devices and wreaks havoc on data-based applications. However, there is no effective way to obtain the truth traffic of IoT devices, which makes it difficult for the cloud to secure the communication of IoT devices and manage the state of network. To ensure reliable communication, a novel approach to detect traffic-based attack by Unmanned Aerial Vehicle (UAV) and Wavelet Packet Energy Entropy (UWPEE) is proposed. In UWPEE scheme, UAV is sent to collect the truth traffic from IoT devices. Then wavelet packet energy entropy is innovatively adopted to detect attacks. Finally, the trust of IoT devices is determined according to their entropy. The experimental results show that UWPEE scheme can effectively identify traffic-based attacks with an accuracy rate of 84.47% and an average recognition efficiency of 4.89 for malicious nodes. Meanwhile, compared with the greedy algorithm, the flight path of the UAVs is reduced by 15.44%.

Study of the Dynamic Response of a Rigid Runway with Different Void States during Aircraft Taxiing

In this study, a set of numerical simulation analysis methods for studying the dynamic response of runway under the action of aircraft taxiing load are presented. An aircraft-runway coupled vibration model was established, and the runway pavement roughness was taken as the vibration excitation source; then, the aircraft taxiing dynamic load was obtained. A three-dimensional finite element model of the runway was established, and the vertical dynamic displacement (VDD) response and its variation law of the runway under different void conditions were studied under the action of the dynamic load of a taxiing aircraft. In addition, using wavelet packet transform, the acceleration signals at different positions of the pavement slab under the sliding load were decomposed into three layers. The relationship among the wavelet packet energy ratio (WPER), the wavelet packet energy entropy (WPEE) of each frequency band, and the void under the pavement slab was obtained. The results show that the established model could quickly and accurately solve the aircraft taxiing dynamic load. In the case of a slight void, the VDDs in the runway center and under the main landing gears had negative exponential and logarithmic relationships with the reduction coefficient of the dynamic elastic modulus of the base layer, respectively. When there was a severe void, the pavement slab was separated from the base layer. The VDDs in the runway center and under the main landing gears were exponentially and linearly related to the slab’s void area, respectively. The vibration signals were extracted at three measuring points, and the wavelet packet energy characteristics of the signals were compared and analyzed. It was found that the WPER and WPEE of the vibration signals in the void area of the slab corner could better reflect the void state of the slab bottom.

Contactless Fault Diagnosis for Railway Point Machines Based on Multi-Scale Fractional Wavelet Packet Energy Entropy and Synchronous Optimization Strategy

Railway point machines (RPMs) is one of the most vital devices closely related to the efficiency and safety of train operation. Considering the advantages of contactlessness and easy-to-collect of sound signals, a novel sound-based fault diagnosis method for RPMs is proposed. First, fractional calculus is introduced to wavelet packet decomposition energy entropy (WPDE). Fractional WPDE (FWPDE) is then proposed, which is verified to be a more effective tool for fault feature representation. Second, coarse-grain process is firstly introduced to FWPDE. Novel feature named multi-scale FWPDE is developed, which can significantly improve fault diagnosis accuracy. Third, to select optimal feature set and optimize the hyperparameters of support vector machine (SVM) at the same time, a synchronous optimization strategy based on binary particle swarm optimization (BPSO) is presented, which can further improve the diagnosis accuracy. The superiority and effectiveness of the proposed method are verified by comparing to some existing fault diagnosis methods. The diagnosis accuracies of reverse-normal and normal-reverse switching processes reach 99.33% and 99.67%, respectively. Especially, the proposed method is suitable for diagnosis of similar faults, which can also provide reference for similar research fields.

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

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%.

Detection of modulated chatter using moving average difference spectrum analysis

Chatter monitoring is an essential requirement for high-precision machining systems. Chatter vibration is characterized by amplitude modulation when the beat-frequency effect or geometric eccentricities exist, which hinders methods based on the transient characteristic index. Here, moving average difference spectrum analysis (MADSA) is proposed for detection of modulated chatter; the method can detect chatter frequency and track its amplitude from low signal-to-noise vibration signals early on. MADSA utilizes a moving average to smooth the amplitude trend of chatter frequency candidates obtained by the difference spectrum analysis, and the chatter frequency is detected using monotonicity analysis. MADSA was validated in both simulations and gear-milling machine experiments, and comparisons between wavelet packet-energy entropy method and MADSA are described, to illustrate the performance of the latter.

Monitoring the Health Status of Rolling Bearings Based on Adaptive mean-shift

In order to monitor the structural health condition of rolling bearings in real time, an adaptive mean-shift algorithm was proposed to replace the fixed step size in the Mean Shift algorithm with adaptive variable step size. Wavelet packet energy entropy was used to extract the characteristics of rolling bearing vibration signals, and a series of centroids were clustered by adaptive mean-shift after dimensionality reduction. The centroid in normal state was used as the reference centroid to calculate the offsets of all centroids (including reference centroid) and reference centroid. Experiments show that the adaptive mean-shift algorithm converges quickly and has high accuracy. The centroid offset can be used to monitor the structural health effectively.

Quantitative inspection for identifying broken coal mine wire rope based on wavelet packet sparse representation

The magnetic flux leakage (MFL) signal of steel wire rope is easily affected by background noise, rope strands and so on. A preprocessing method for the damage signal based on wavelet packet sparse representation is proposed. This method is suitable for the damage signal of the wire rope. The original signal is decomposed into three layers of wavelet packets and the wavelet packet coefficients are sparsely represented by the matching pursuit (MP) and orthogonal matching pursuit (OMP) algorithms. The signal-to-noise ratio (SNR) of the reconstructed signal is much higher than that obtained through the wavelet threshold shrinkage method, the median filter method and the singular value difference spectrum method. The proposed method can significantly improve the noise reduction effect of the damage signal. A principal component analysis (PCA)-based particle swarm optimisation support vector machine (PSO-SVM) model for quantitative recognition is proposed. Seven global eigenvalues and wavelet packet energy entropy details of damage signals are extracted as effective eigenvalues. The eight eigenvalues are used as the input for the SVM that is designed and trained. A PSO-SVM classification model based on PCA is proposed. The results show that the recognition rate of the SVM is 94.73%. The quantitative recognition accuracy is improved.

Fault Diagnosis of Analog Circuits Based on Wavelet Packet Energy Entropy and DBN

To solve the problem of low classification and recognition rate caused by small number of fault samples and inaccurate feature extraction in analog circuit fault diagnosis, a fault diagnosis algorithm for analog circuit system based on the combination of wavelet packet energy entropy and Deep Belief Network (DBN) is proposed. Firstly, the original output voltage signal of the circuit is decomposed by a multi-layer wavelet packet, then the feature vector is constructed in the form of energy entropy, and then the principal component analysis (PCA) is used for feature selection. The reduced dimension feature vector is taken as the input vector of DBN, and the fault diagnosis is completed after training and learning of the DBN network model. The experimental results show that compared with other algorithms, the proposed method can be more accurate and effective in diagnosing fault types in analog circuits, especially for Sallen-Key band-pass filter circuits, the fault recognition rate reaches 100%.

Fault Diagnosis of Commutation Failure Using Wavelet Transform and Wavelet Neural Network in HVDC Transmission System

Commutation failure is the most common fault on the inverter side of the high voltage direct current (HVDC) transmission system. In the actual engineering operation, there are many reasons for the commutation failure. In order to diagnose the causes in real-time and effectively, this article proposes a new method for the fault diagnosis of the commutation failure based on the combination of the wavelet transform and wavelet neural network (WNN). First, by analyzing the changes in electrical characteristics caused by the failure of commutation, the voltage signal of the inverter side commutation bus is finally selected as the feature signal for diagnosis. Then, the wavelet transform is used as a tool for extracting fault feature quantities, and the root mean square (rms) value of the wavelet detail coefficients, wavelet energy skewness, and wavelet energy spectrum information entropy of the inverter side commutation bus voltage signal after wavelet decomposition are used as three feature parameters. At the same time, wavelet packet decomposition is performed on the inverter side commutation bus voltage signal, and the wavelet packet subband energy, energy entropy, and energy skewness are extracted as the other three feature vectors. Finally, the simulation test data shows that the proposed fault diagnosis method can accurately identify the specific fault causes and has high reference value and specific engineering guiding significance for improving the reliability of the HVDC transmission system. Compared with the traditional BP neural network method for fault diagnosis of commutation failure, the proposed diagnosis method considers the fault type more comprehensively, the diagnosis speed is faster, and the recognition accuracy rate is higher in the case of a small number of samples.

A Novel Rotating Machinery Fault Diagnosis Method Based on Adaptive Deep Belief Network Structure and Dynamic Learning Rate Under Variable Working Conditions

With the development of modern industries, the working environment of rotating machinery has become increasingly complicated. Therefore, it is very meaningful to accurately identify the type of equipment failure under variable operating conditions. This paper presents a rotating machinery fault diagnosis method based on dynamic learning rate deep belief network (DBN) with adaptive structure (PSO-DDBN). Firstly, the wavelet packet energy entropy principle was used to obtain the characteristic matrix of the original data, and then the characteristics of the data under variable conditions were distinguished. Secondly, in order to adjust the structure of DBN, the loss function of DBN was used to construct the convergence function in particle swarm optimization (PSO) adaptive process. The dynamic learning rate strategy was applied to the training process of the network. The network gradient value in each iteration was recorded and the dynamic learning rate function was constructed to achieve the purpose of dynamically adjusting the network learning rate and making the network convergence faster and more stable. Then, the performance of PSO-DDBN was verified by the data of bearing and gearbox under variable conditions. Finally, other intelligent diagnosis algorithms were compared with this method, and the results showed that this method had better universality and fault classification ability.

Estimation of Continuous Joint Angles of Upper Limb Based on sEMG by Using GA-Elman Neural Network

The estimation of continuous and simultaneous multijoint angle based on surface electromyography (sEMG) signal is of considerable significance in rehabilitation practice. However, there are few studies on the continuous joint angle of multiple joints at present. In this paper, the wavelet packet energy entropy (WPEE) of the special subspace was investigated as a feature of the sEMG signal. An Elman neural network optimized by genetic algorithm (GA) was established to estimate the joint angle of shoulder and elbow. First, the accuracy of the method is verified by estimating the angle of the shoulder joint. Then, this method was used to simultaneously and continuously estimate the shoulder and elbow joint angle. Six subjects flexed and extended the upper limbs according to the intended movements of the experiment. The results show that this method can obtain a decent performance with a RMSE of 3.4717 and R2 of 0.8283 in shoulder movement and with a RMSE of 4.1582 and R2 of 0.8114 in continuous synchronous movement of the shoulder and elbow.

A Quantum Mechanics-Based Framework for EEG Signal Feature Extraction and Classification

Quantum machine learning (QML) is an emerging research field, which is devoted to devising and implementing quantum algorithms that could enable machine learning faster than that of classical computers. In this article, a hierarchic quantum mechanics-based framework is investigated to implement both the feature extraction and classification in the electroencephalogram (EEG) signal. First, the classical EEG signal dataset is prepared as a quantum state while the sign of the data point is preserved. The prepared quantum state is then evolved with the quantum wavelet packet transformation (QWPT) and the wavelet packet energy entropy (WPEE) feature is extracted as the input of the subsequent quantum classifier. We finally propose the improved quantum support vector machine with the arbitrary nonlinear kernel, which is employed to predict the label of the EEG signal. The complexity analysis indicates that the proposed framework provides exponential speedup over the same structured classical counterpart. Besides, the quantitative experimental results verify the feasibility and validity.