Wavelet Packet Energy Spectrum Research Articles

A Fault Identification Method of Hybrid HVDC System Based on Wavelet Packet Energy Spectrum and CNN

Aiming at the shortcomings of traditional fault identification methods in fault information acquisition, In the scenario of hybrid HVDC transmission system, a new fault identification method is proposed by using wavelet packet energy spectrum and convolutional neural network (CNN), which effectively solves the problem of complex fault feature extraction of hybrid HVDC transmission system. This method effectively improves the accuracy of fault identification. Firstly, tThe frequency-domain characteristics of the fault transient signal are extracted by wavelet packet transform, and the feature differences are reflected in the form of energy spectrum. Secondly, according to the extracted energy feature information, the order of fault line and fault type is identified by CNN. Finally, through example verification and algorithm comparison, it is concluded that, the mentioned model has a strong ability to identify faults, and has strong anti-noise interference and tolerance to transition resistance.

Fault Distance Measurement Method Based on Wavelet Energy Spectrum and BWO Algorithm Optimized CNN-GRU Hybrid Neural Network

Aiming at the problems such as noise interference in the current fault ranging methods for flexible DC transmission lines, a single-ended intelligent fault ranging method is proposed as a hybrid neural network based on wavelet energy spectrum and BWO algorithm to optimize the multi-head attention mechanism of CNN combined with gated recurrent unit GRU. First, the correlation between wavelet spectrum energy and fault distance is analyzed, and wavelet packet decomposition is used to extract the wavelet packet energy spectrum feature vector as the model input of the neural network. Second, the hybrid neural network model is constructed and trained to mine the deep fault information in the time series, and the parameters of the hybrid neural network model with added multi-head attention are optimized using the iterative optimization search of the beluga algorithm to achieve fast and accurate positioning of the fault distance. Finally, the network is trained using the constructed four-terminal Zhangbei flexible DC transmission system model to collect data, and the experimental results prove that the method has high distance measurement accuracy, strong anti-interference ability and generalization ability, and a certain degree of resistance to transition resistance.

Characterization and Prediction of Wind Turbine Blade Damage Based on Fiber Grating Sensor

INTRODUCTION: As a renewable and clean use of energy, wind power generation has a very important role in the new energy generation industry. For the many parts of various wind turbines, the safety and reliability of wind turbine blades are very important. OBJECTIVES: The energy spectrum simulation algorithm included in the wavelet analysis method is used to simulate and analyzewind turbine blade damage, to verify the correctness and validity of wind turbine blade damage analysis. METHODS: Matlab simulation is used to introduce the experiments related to the static and dynamic detection of fiber grating sensors, analyze the signal characteristics of the wind turbine blade when it is damaged by the impact, and provide a basis for the analysis of the external damage of large wind turbine blade. RESULTS: The main results obtained in this paper are the following. By analyzing the decomposition of wavelet packets, the gradient change of wavelet impact energy spectrum before and after the wavelet damage was obtained and compared with the histogram, and the impact energy spectrum of each three-dimensional wavelet energy packet in the image was compared and analyzed, which can well realize the recognition of wavelet damage gradient for solid composite materials. CONCLUSION: With the help of Matlab simulation to collect the impact response signal, using the wavelet packet energy spectrum method to analyze the signal, can derive the characteristics of wind turbine blade damage.

Automatic Diagnosis Method for Short Circuit Faults in Power Measurement Instruments Based on CNN

The accuracy of short-circuit fault diagnosis methods for power measuring instruments is still low, which can directly lead to abnormal power data statistics. To address this issue, a convolutional neural network is used to construct an automatic diagnosis model for short circuit faults in power measuring instruments. The model utilizes wavelet packet energy spectrum to extract crucial features from the signal. The model identifies and determines the fault type based on the obtained operation characteristic data and the set short-circuit fault diagnosis criteria. The Sparrow optimization algorithm is used to optimize the model's parameters and enhance its performance. Experimental analysis revealed that after the signal features were extracted by using the wavelet energy spectrum distribution, the error value fluctuated in the range of 0.00 ~ 0.04 with regard to the measured value and was mainly around 0.02. The extracted features achieved high accuracy levels. The designed model exhibited an average diagnostic accuracy of 97.03%, surpassing the other three models by 9.24%, 7.10%, and 4.25%. The presented model can improve the precision and productivity of fault detection, support the safety and reliability of power system operations, and facilitate the collection and analysis of power usage data

Experimental Study on Bridge Structural Damage Identification Based on Quasi-Static Displacement Effects and Wavelet Packet Decomposition

Based on the displacement-induced linearity error curve and the theory of wavelet packet analysis, a bridge structural damage identification method is proposed, integrating two damage indicators: Quasi-Static Displacement-Induced Linearity Error Curve (QSDIL) and Relative Energy Rate of Wavelet Packet Energy Spectrum (RES). This method first constructs the QSDIL damage indicator based on a quasi-static displacement-induced linearity error, which is used for the preliminary localization of the bridge structural damage. Subsequently, relying on the principles of wavelet packet analysis, the method constructs the RES damage indicator for accurate positioning of the damage location in the bridge structure. The proposed method is experimentally validated, and the impact of factors such as single-point damage, multi-point damage, signal noise, lane position, and vehicle weight on the experimental results is investigated. The results indicate that the proposed method exhibits excellent identification performance for the location of structural damage in both single-point and multi-point damage scenarios, with good agreement between experimental and theoretical values. As the signal-to-noise ratio decreases, the accuracy and precision of the RES curve in locating the bridge structural damage position exhibit a nonlinear decreasing trend, with relatively small identification errors observed at noise levels of 90 dB to 100 dB. Different lane positions have a minimal impact on the damage identification effectiveness. With increasing vehicle weight, both QSDIL and RES curves show an increasing trend in peak values, facilitating the localization of bridge structural damage positions.

Fault Diagnosis of Medium Voltage Circuit Breakers Based on Vibration Signal Envelope Analysis.

In modern power systems or new energy power stations, the medium voltage circuit breakers (MVCBs) are becoming more crucial and the operation reliability of the MVCBs could be greatly improved by online monitoring technology. The purpose of this research is to put forward a fault diagnosis approach based on vibration signal envelope analysis, including offline fault feature training and online fault diagnosis. During offline fault feature training, the envelope of the vibration signal is extracted from the historic operation data of the MVCB, and then the typical fault feature vector M is built by using the wavelet packet-energy spectrum. In the online fault diagnosis process, the fault feature vector T is built based on the extracted envelope of the real-time vibration signal, and the MVCB states are assessed by using the distance between the feature vectors T and M. The proposed method only needs to handle the envelope of the vibration signal, which dramatically reduces the signal bandwidth, and then the cost of the processing hardware and software could be cut down.

Precision local anomaly positioning technology for large complex electromechanical systems

In recent years, Prognostics Health Management (PHM) technology has become an important reference technology in fields such as avionics and electromechanical systems due to its ability to reduce costs and achieve state based maintenance and autonomous support. However, with the operation of large and complex electromechanical systems (ES), the data generated gradually ages the status of components, and traditional PHM technology is difficult to solve the problem of electromechanical system components becoming more complex. Based on this, this study takes the hydraulic actuator cylinder as an example to construct a local component fault detection model. Firstly, fault data features are extracted using wavelet packet energy spectrum, and then a fault detection model is constructed based on support vector machine (SVM). In response to the shortcomings of SVM, a smooth support vector machine (SSVM) is proposed to replace SVM, and an improved crow search algorithm (ICSA) is used to improve SVM. Finally, an intelligent detection model for hydraulic actuator cylinder faults based on ICSA-SSVM was constructed based on the above algorithms. The experimental results show that the ICSA-SSVM model has the fastest Rate of convergence, among which, the positioning accuracy is 0.96, the fitting degree is 0.984, the fault detection accuracy is 99.16 %, the recall value is 94.52 %, and the AUC value is 0.986, all of which are better than the existing fault detection models. From this, it can be seen that the precise local anomaly localization technology for large-scale complex electromechanical systems based on the ICSA-SSVM algorithm proposed in this study can improve the efficiency and accuracy of fault detection, achieve accurate and intelligent detection of ES local anomalies, and have certain positive significance for the development of China’s industry.

Inter-Turn Short Circuit Fault Detection of PMSM Based on Wavelet Packet Energy Spectrum and CEEMDAN-HT

Inter-Turn Short Circuit Fault Detection of PMSM Based on Wavelet Packet Energy Spectrum and CEEMDAN-HT

Experimental design and data analysis and optimization of mechanical condition diagnosis for transformer sets

Abstract The typical power transformer diagnosis approach is imprecise and unstable. A support vector machine classification algorithm is proposed, by designing an algorithm program that can improve the accuracy and speed of energy transformer diagnosis, the vibration signals of the surface twisting in different states are extracted by wavelet packet energy spectrum signal processing method, it is verified that the curve similarity between the vibration simulation model and the measured data is greater than 0.98, proving the simulation model’s validity. The calculation technique of online short circuit inductance is developed from the equivalent transformer model, and the variation error of simulation results is less than 0.05% when compared to the real transformer characteristics. The suggested state diagnostic technique successfully compensates for the drawbacks of the reactance method, which is incapable of detecting and judging the slightly loose or faulty winding. The method’s accuracy and superiority, as well as the practicability of the state diagnosis system, are demonstrated.

Fault Diagnosis of Bearings Using Wavelet Packet Energy Spectrum and SSA-DBN

To enhance fault characteristics and improve fault detection accuracy in bearing vibration signals, this paper proposes a fault diagnosis method using a wavelet packet energy spectrum and an improved deep confidence network. Firstly, a wavelet packet transform decomposes the original vibration signal into different frequency bands, fully preserving the original signal’s frequency information, and constructs feature vectors by extracting the energy of sub-frequency bands via the energy spectrum to extract and enhance fault feature information. Secondly, to minimize the time-consuming manual parameter adjustment procedure and increase the diagnostic accuracy, the sparrow search algorithm–deep belief network method is proposed, which utilizes the sparrow search algorithm to optimize the hyperparameters of the deep belief networks and reduce the classification error rate. Finally, to verify the effectiveness of the method, the rolling bearing data from Casey Reserve University were selected for verification, and compared to other commonly used algorithms, the proposed method achieved 100% and 99.34% accuracy in two sets of comparative experiments. The experimental results demonstrate that this method has a high diagnostic rate and stability.

Fault diagnosis of transformer winding short circuit based on WKPCA-WM and IPOA-CNN

When the winding of the power transformer is short-circuited, the winding will experience constant vibration, which will cause axial instability of the winding, and then lead to winding looseness, deformation, bulge, etc., therefore, a diagnosis method based on the Improved Pelican Optimization Algorithm and Convolutional Neural Network (IPOA-CNN) for short-circuit voiceprint signal of transformer windings is proposed. At the same time, considering the input parameter dimension of deep learning cannot be too high, a new feature parameter selection method is constructed for this model. Firstly, the frequency characteristics of winding acoustic vibration signals are analyzed, and then the characteristic parameters of transformer acoustic signals are extracted by Wavelet Packet Energy Spectrum (WPES) and Mel Frequency Cepstrum Coefficient (MFCC), respectively. Then, the two methods are combined to construct the WM feature extraction algorithm, and the Weighted Kernel Principal Component Analysis (WKPCA) is used to reduce the dimension of the feature to obtain the feature parameters with accurate feature information and low redundancy; Finally, combined with Sobol sequence to optimize the initial population of Pelican Optimization Algorithm (POA), the convolution kernel of Convolutional neural network (CNN) was optimized by IPOA, and the optimal convolution kernel was obtained. The transformer winding short-circuits voiceprint diagnosis models of WKPCA-WM and IPOA-CNN were constructed, which realized the accurate diagnosis of winding short-circuit voiceprint. The validity and feasibility of the method are verified by the acoustic signal data collected in the laboratory.

Fault Recognition for Mechanical Arm by Using Relative Margin SVM

Monitoring and detecting faults during the operation of the manipulator is the prerequisite for fault recognition and safe operation. Accurate classification of mechanical arm faults can support to effectively eliminate mechanical arm faults. In this paper, the authors utilize a relative margin support vector machine (RMSVM) to classify and monitor the faults for mechanical arm. First, the status of mechanical arm are represented a high dimensional vector which consists of the mean, variance, correlation coefficient of the residual momentum signal in time domain, and the wavelet packet energy spectrum in frequency domain. The collected feature vectors for mechanical arm status are used to train RMSVM. A virtual prototype of mechanical arm is used to analyze the changes in the features of the residual momentum caused by fault and evaluate the RMSVM model for future mechanical arm status. The simulation results show that RMSVM can effectively detect the faults during the operation of manipulator.

Analysis of Blasting Vibration Effect of Railway Tunnel and Determination of Reasonable Burial Depth

Based on specific examples of underpass tunnel blasting, field measurements, and numerical simulation studies are carried out. According to the results of the blasting vibration data measured on-site, a regression model of the blasting vibration velocity is established. Based on the wavelet packet energy spectrum analysis method, the effect of frequency on the vibration response intensity is studied. In addition, the maximum charge per delay allowed for tunnel blasting is obtained by formula inversion. Relying on ANSYS/LS-DYNA to establish a three-dimensional numerical model, the accuracy of numerical simulation can be checked by the measured vibration data. The results show that the numerical simulation has high precision and can meet the subsequent analysis needs. Using numerical simulation, the variation law of the vibration response characteristics of ground blasting under different tunnel burial depths is studied. The analysis results show that with increasing tunnel burial depth, the ground blasting vibration velocity decays exponentially. According to the corresponding specification of blasting vibration, a reasonable value range of the buried depth of the underpass tunnel can be obtained. The research ideas and methods introduced can be used for reference for similar railway tunnel blasting control and railway tunnel route selection.

Detection of rupture inside rubber bearings using active sensing method

Rubber bearings are key components of base-isolated structures; therefore, it is important to monitor their damage states. This study investigates the effectiveness of the active sensing method for detecting rubber rupture inside rubber bearings. The mechanism of wave propagation demonstrates the potential of the active sensing method for rubber rupture damage detection. Detection tests were performed on a full-scale rubber bearing, and a swept signal from 1 kHz to 100 kHz was used as the detection signal. A linear discriminant analysis (LDA) method based on the wavelet-packet energy spectrum is proposed to detect the rubber rupture damage. The LDA method can comprehensively consider the influences of axial pressure and shear deformation, displaying a 98% accuracy for rubber rupture damage detection. In addition, detection tests were performed on a scaled rubber specimen under different damage states. The results show that the LDA method has excellent performance when detecting the degree of rupture damage, with an accuracy of 95.8%.

Research on wear detection mechanism of cylinder liner-piston ring based on energy dissipation and AE

In the field of cylinder liner-piston ring (CL-PR) wear calculations, the traditional calculation models are difficult to use as a guide for wear detection. Therefore, a new analysis model of CL-PR wear based on energy dissipation theory is established in this paper. By using a mixed lubrication equation and simulation modification method, a new characterization formula of CL-PR wear is theoretically established. The maximum error between its calculated results and the simulation results is only 1.167%. The qualitative relationship between the wear volume and the frequency band energy of the acoustic emission (AE) signal is established based on the energy conversion relationship. The AE signal acquisition platform of CL-PR wear is built on the EV80 combustion engine, and the Bayes Shrink method is used to set the threshold of large-amplitude disturbance AE events for noise reduction. The wear signal is processed by wavelet packet energy spectrum decomposition, and the resulting wear energy spectrum can better characterize the change of wear characteristics. The BP (Back Propagation) neural network is trained with the characteristic parameters of the energy spectrum, and the classification accuracy of training and finally experimental results reach 98.6% and 100%, respectively, proving that AE technology can be applied to CL-PR wear monitoring combined with the neural network.

Magnetic-Acoustic Feature Extraction and Damage Fusion Evaluation of 45 Steel Specimens during Fatigue Process for Remanufacturing

To clarify the changes of the magnetic-acoustic features of 45 steel specimens during fatigue damage, an experimental platform was built to carry out magnetic memory and acoustic emission detection. The magnetic memory and acoustic emission signals of specimens in different damage states were collected, and the multi-scale entropy characteristics of magnetic memory signals, as well as the wavelet packet energy spectrum and singularity index characteristics of acoustic emission signals, were further extracted. A magnetic-acoustic feature fusion and damage assessment model was constructed by using Naive Bayes method. Results show that the average value of multi-scale entropy of normal magnetic field intensity Hp (y) increases gradually with the increase of fatigue cycles, and the average value of multi-scale entropy of magnetic field intensity gradient K gradually decreases. The cumulative ringing count and energy spectrum (proportion of frequency band 1) of acoustic emission signals decrease with the increase of fatigue cycles, while the amplitude singularity index gradually increases. The established model has high evaluation accuracy, and the conclusions of this paper can provide basic methods and data support for fatigue damage evaluation of remanufactured components.

Local wavelet packet decomposition of soil hyperspectral for SOM estimation

It is a critical work to accurate extraction of spectral characteristics of soil organic matter (SOM), which will help to improve the model performance for SOM estimation. In this paper, we propose a new SOM prediction model using local wavelet characteristics of soil spectrum. Specifically, six level local wavelet packets decomposition are considered with different moving window sizes and sliding lengths, and thus formulate two new spectral indicators, namely, local wavelet packet energy spectrum (LWE) and local logarithmic wavelet packet energy spectrum (LLWE). The LWE and LLWE are then viewed as model inputs, which are respectively used to forecast SOM content. We test the prediction performance of the LWE and LLWE based on the two prediction models, that is, multiple linear regression (MLR) and ridge regression with cross-validation (RCV). The result shows that the two new spectral indicators help to enhance the spectral response information of SOM. Among the four prediction models, the LLWE-MLR is the most outstanding. Compared to the original hyperspectral and energy features vectors, the LWE and LLWE bring the better model performance. Since both the high and low-frequency components of local information of soil hyperspectral are fully extracted, the two new spectral indicators significantly improve the prediction accuracy and reliability of SOM.

Tensile damage mechanisms of carbon fiber composites at high temperature by acoustic emission and fully connected neural network

Abstract In this paper, the tensile damage mechanism of carbon fiber composites at high temperatures is analyzed. The acoustic emission technique was employed to monitor the tensile process of specimens. The acoustic emission signals at high and room temperatures were classified based on k-means and the wavelet packet energy spectrum. The results show that the damage mechanisms at high temperatures and room temperature differ. At high temperatures, there is more stress release, the material instability appears earlier, and redistribution occurs in the specimen. The damage mechanisms include matrix cracking, fiber/matrix debonding, and fiber breakage. For damage mechanism identification, the acoustic emission characteristics were used under room temperature and high-temperature conditions in the fully connected neural network, with an accuracy rate of 97.5%. The results indicate that the network is suited for both high temperatures and room temperature and can better identify various damage mechanisms.

Short-circuit fault evaluation of SF6 circuit breaker energy storage motor coil based on high-frequency equivalent model

Abstract The capacitive inductance parameters of the energy storage motor windings were calculated by finite element method, and the high-frequency equivalent model of the winding was established based on ATP-EMTP. Square wave pulse voltage signal was applied to the input end of the model, and the response voltage curve at the end of the circuit model was measured. The wavelet packet-energy spectrum was calculated as the characteristic parameter of the response curve in different short-circuit positions, and the short-circuit position was evaluated by the random forest algorithm. The accuracy rate of short-circuit fault location in the test set was 75%, indicating that the method has certain feasibility.

Research on the mechanism of equal-thickness screening process formation of banana-type vibrating screens based on the signal analysis of the wavelet packet energy spectrum

ABSTRACT Banana-type vibrating screen has been widely used in classification operations in coal cleaning and mining fields due to good screening performance and large processing capacity. In this work, a new analytical method was introduced to clarify the mechanism of equal-thickness formation of banana-type vibrating screens. The vibration characteristics of the collision between the screen surface and the particles were analyzed using the wavelet packet frequency and the energy spectra. The main frequency band of the collision at the infeed and outfeed ends was the 2nd frequency band (low-frequency band). The corresponding amplitude and energy percentage value of the screen surface at the infeed end were 0.12 mm and 4.3%, respectively, which were higher than those at the outfeed end. The spectral amplitude and the energy percentage value had “high-in and low-out” characteristics which verified the mechanisms of thin-layer penetration and equal-thickness formation of banana-type vibrating screens. This work contributes to the theory of equal-thickness screenings and provides new research ideas and technical support for designing vibrating screens.