Wavelet Packet Analysis Research Articles

An algorithm for improving the coefficient accuracy of wavelet packet analysis

On the basis of analyzing the cause of coefficients distortion inhered in the existing wavelet packet transform algorithms, a new method is presented to rectify the distortion and improve the accuracy. Its principle is to implement compensation calculation by using the complementary characteristic of wavelet filters. The simulation result of a typical signal shows the method is effective in improving the accuracy of wavelet packet coefficients. The influences of the compensation calculation times and vanishing moments of the wavelet are analyzed. Theoretically, the maximum error could be reduced to 25% with one time compensation calculation. The results show that with two and four times compensation calculations, the error is reduced to 10% and 2% respectively. The experiment result of the vibration signal of a roller bearing with outer ring fault shows that the proposed method can effectively magnify the amplitude of the fault frequencies in practice.

Research on detection of welding penetration state during robotic GTAW process based on audible arc sound

PurposePenetration state is one of the most important factors for judging the quality of a gas tungsten arc welding (GTAW) joint. The purpose of this paper is to identify and classify the penetration state and welding quality through the features of arc sound signal during robotic GTAW process.Design/methodology/approachThis paper tried to make a foundation work to achieve on‐line monitoring of penetration state to weld pool through arc sound signal. The statistic features of arc sound under different penetration states like partial penetration, full penetration and excessive penetration were extracted and analysed, and wavelet packet analysis was used to extract frequency energy at different frequency bands. The prediction models were established by artificial neural networks based on different features combination.FindingsThe experiment results demonstrated that each feature in time and frequency domain could react the penetration behaviour, arc sound in different frequency band had different performance at different penetration states and the prediction model established by 23 features in time domain and frequency domain got the best prediction effect to recognize different penetration states and welding quality through arc sound signal.Originality/valueThis paper tried to make a foundation work to achieve identifying penetration state and welding quality through the features of arc sound signal during robotic GTAW process. A total of 23 features in time domain and frequency domain were extracted at different penetration states. And energy at different frequency bands was proved to be an effective factor for identifying different penetration states. Finally, a prediction model built by 23 features was proved to have the best prediction effect of welding quality.

Wavelet Denoising Analysis of Rock’s Stress-Strain Curve under Uniaxial Compression

Rock stress-strain curve under uniaxial compression is the output of joint action of rock and test system, so it must have certain noise. In order to realize effective denoising processing, the concept of instantaneous deformation modulus has been put forward, and the difference quotient algorithm of instantaneous deformation modulus has been used to enhance noise. After wavelet analysis and wavelet packet analysis of rock stress consequence and instantaneous deformation modulus respectively, the results of different denoising schemes have been statistically analyzed, this analysis shows that the wavelet packet analysis of instantaneous deformation modulus can achieve optimal denoising effect.

Airborne Oxygen-Making System Drift Oxygen Sensor Characteristics for Fault Diagnosis Strategy

This paper focuses on airborne oxygen-making system shortcomings of oxygen sensor characteristic drift in, proposes a method of fault diagnosis. Oxygen sensor with a Wavelet packet analysis of feature extraction, based on wavelet neural network method to determine whether the sensor has failed, and sensor to detect hardware and software design are given.

Wavelet Packet Analysis and Gray Model for Feature Extraction of Hyperspectral Data

Wavelet packet analysis (WPA) and gray model (GM) are investigated for nonlinear unsupervised feature extraction of hyperspectral remote sensing data in this letter. Treated as derivative series, a hyperspectral response curve of each pixel is decomposed into an approximation and various detailed compositions by WPA, and then, GM is continuously applied to find the relationship among those detailed compositions. Cluster-space representation is used for determining the optimal wavelet. New extracted features can reveal the intrinsic identities of hyperspectral data. Experimental results show the feasibility and reliability of our proposed method in terms of classification accuracy.

Structure Fault Diagnosis of Tower Crane Based on Wavelet Packet Analysis and Support Vector Machines

First establish a dynamic model of tower crane in the load lifting process, the lifting load is solved under two work conditions.Then establish the FEM(finite element analysis) model of the tower crane under the normal and the damage condition. Get the dynamic displacement of the normal and the damage status under the lifting dynamic load. With wavelet packet decomposition and SVM(Support vector machines) multi-classification algorithm, a multi-fault classifier is constructed, and applied to the fault diagnosis of tower body. The results of the study show that the multi-fault classifier has such advantages as simple algorithm and excellent capability of fault classification, and it can not only diagnose the structural damage status, but also determine the positions of structural damage. This will be a new search on tower crane structural health diagnosis.

Study on Energy Distribution of Single-Hole Blasting Vibration Signal Based on Wavelet Packet Transform

It is conducive to reducedamage of blasting vibration to realize energy distribution and attenuation lawof single-hole blasting vibration signal. With the measured single-holeblasting vibration velocity curves, used wavelet packet analysis technologywith high-resolution character, the law of energy distribution of single-holeblasting vibration signals in different frequency bands, and the effect ofblasting source and distance from the source on single-hole blasting vibrationsignal energy distribution were analysised. The results show that the energy ofsingle-hole blasting vibration signals attenuation very quickly in thefrequency domain concentration distribution in 0~100Hz; and distance from thesource has significant influence on energy distribution in the frequencydomain; The energy is mainly distributed in the low frequency band whendistance from the source is larger, which has guiding significance inmitigation of blast-induced vibrations.

Statistical damage classification method based on wavelet packet analysis

A novel damage classification method based on wavelet packet transform and statistical analysis is developed in this study for structural health monitoring. The response signal of a structure under an impact load is normalized and then decomposed into wavelet packet components. Energies of these wavelet packet components are then calculated to obtain the energy distribution. Statistical similarity comparison based on an F-test is used to classify the structure from changes in the wavelet packet energy distribution. A statistical indicator is developed to describe the damage extent of the structure. This approach is applied to the test results from simply supported reinforced concrete beams in the laboratory. Cases with single and two damages are created from static loading, and accelerations of the structure from under impact loads are analyzed. Results show that the method can be used with no reference baseline measurement and model for the damage monitoring and assessment of the structure with alarms at a specified significance level.

Fault Line Selection Method of Small Current Grounding System Based on Wavelet Packet and Multi Frequency Band Analysis

Fault Line Selection Method of Small Current Grounding System Based on Wavelet Packet and Multi Frequency Band Analysis

Audio sensing and modeling of arc dynamic characteristic during pulsed Al alloy GTAW process

PurposeThe purpose of this paper is to study the relationship between arc sound signal and arc height through arc sound features of GTAW welding, which is aimed at laying foundation work for monitoring the welding penetration and quality by using the arc sound signal in the future.Design/methodology/approachThe experiment system is based on GTAW welding with acoustic sensor and signal conditioner on it. The arc sound signal was first processed by wavelet analysis and wavelet packet analysis designed in this research. Then the features of arc sound signal were extracted in time domain, frequency domain, for example, short‐term energy, AMDF, mean strength, log energy, dynamic variation intensity, short‐term zero rate and the frequency features of DCT coefficient, also the wavelet packet coefficient. Finally, a ANN (artificial neural networks) prediction model was built up to recognize different arc height through arc sound signal.FindingsThe statistic features and DCT coefficient can be absolutely used in arc sound signal processing; and these features of arc sound signal can accurately react the modification of arc height during the GTAW welding process.Originality/valueThis paper tries to make a foundation work to achieve monitoring arc length through arc sound signal. A new way to remove high frequency noise of arc sound signal is produced. It proposes some effective statistic features and a new way of frequency analysis to build the prediction model.

Eddy Current Detecting of Leak Hole in Pipeline by Wavelet Packet Signal Analysis Method

In the process of detecting leak hole in pipeline by intelligent pig with eddy current measuring method, the cut off effect of eddy current field in the closed cross section of leak hole in steel plate is weak due to geometric deformation of eddy current on the edge of the hole produced by destroyed drilling. As a result, the measured signal is very unobvious. Meanwhile, owing to the rough surface of steel plate, the periodic interference generated by movement of the detecting probe cannot be eliminated or be inhibited easily by conventional signal processing methods for its large amplitude and approximate frequency band with the leak hole signals, the signal to noise ratio (SNR) of measured signal is very low and the accurate identification of leak hole cannot be guaranteed. The wavelet transform, with characteristics of time-frequency localization and multiple scales, is a useful and effective method for identifying singularity of the signals and adapts to detect the transient signal or extract non-stationary information in the signals with strong periodic interference and noise. The reconstructing signal SNR will be increased greatly in eddy current detecting of leak hole in the pipeline with wavelet packet analysis of the signal by constructing the self-defined cost function based on maximum SNR to obtain optimal wavelet packet basis function. This ensures good detection and location of leak hole in the pipeline.

Fault Diagnosis Method of Rolling Bearing Based on Ensemble Local Mean Decomposition and Neural Network

For a problem of mode mixing occurs in implementation process of local mean decomposition (LMD) method, an analytical method based on ensemble local mean decomposition (ELMD) and neural network is proposed to apply to fault diagnosis of rolling bearing, the vibrational signal of rolling bearing is decomposed into a series of product functions(PF) by ELMD method. The PF components which contain main fault information are selected to perform a further analysis. The kurtosis coefficient and energy characteristic parameters extracted from these PF components can be used as the input parameters of the neural network to identify the working status and fault types of rolling bearing. Through the analysis of rolling bearing with fault-free, inner-race fault and outer-race fault, the results indicate that the method based on ELMD and neural network has a higher failure recognition rate than the method based on wavelet packet analysis and neural network, and the working status and fault types of rolling bearing can be identified accurately and effectively.

Hybrid Signal Processing Approach for Enhanced Detection of Defects in EC-NDE

In eddy current (EC) testing, presence of lift–off and material property variation makes the defect detection as a challenging task. In view of this, a signal processing approach comprising of independent component analysis (ICA) and wavelet packet analysis (WPA) is attempted. In this paper, performance of hybrid signal processing approach and the resultant improvement in signal-to-ratio (SNR) is discussed.

Fault identification of gearbox degradation with optimized wavelet neural network

A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degra- dation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back- propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experi- mental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.

Fault Diagnosis of Cascaded Converter Based on Wavelet Packet RBF Neural Network

To study the power component open circuit faults diagnosis method of the cascaded converter. Aiming at the insufficiency of the BP learning algorithm in the machinery fault diagnosis, such as the low learning convergence speed, the easily appearing local minimum, the instability learning performance caused by the initial value, to proposed a new method applied to the cascaded converter based on radial basis function (RBF) neural network. Experiments show that the method based on wavelet packet analysis and RBF neural network has better learning and fault identification capability, and it can meet the online real-time fault diagnosis of the cascaded converter.

Quantitative Acoustic Emission Fatigue Crack Characterization in Structural Steel and Weld

The fatigue crack growth characteristics of structural steel and weld connections are analyzed using quantitative acoustic emission (AE) technique. This was experimentally investigated by three-point bending testing of specimens under low cycle constant amplitude loading using the wavelet packet analysis. The crack growth sequence, that is, initiation, crack propagation, and fracture, is extracted from their corresponding frequency feature bands, respectively. The results obtained proved to be superior to qualitative AE analysis and the traditional linear elastic fracture mechanics for fatigue crack characterization in structural steel and welds.

Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network

A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.

A Fault Line Detection Method Based on Wavelet Packet Analysis in Distribution Network

With the rapid development of power network automation, the detection of the default single-phase grounding fault line in small current neutral grounding system has became a hot spot in research of power network system. Generally, power network neutral point is not grounded or grounded through arc-suppression coil grounding. When single phase grounding fault occurs, the power network system is named as small current grounding system since the grounding short-circuit is small due to a low-impedance short circuit.

Fault Diagnosis of Induction Motor Based on Wavelet Packet Analysis and Negative Selection Algorithm

For the spectrum characteristics of the motor vibration, the vibration fault diagnosis method of motor rotor rubbing based on wavelet packet transform and real-valued negative selection algorithm is put forward. Using wavelet packet analysis to energy analysis of rotor rubbing and extracting the fault feature vectors, then by real-valued negative selection algorithm to identify the normal and failure mode eigenvectors. The experimental results show that with this method all the rotor rubbing faults can be detected comprehensive and rapidly. This method has high feasibility of the wavelet packet analysis and real-valued negative selection algorithm in the rotor rubbing fault diagnosis.

Application of Wavelet Analysis to Fault Diagnosis of Mechanical Equipment

Aiming at the characteristics of mechanical gear transmission, taking automobile main reducer as research object, this paper analyzes the gear transmission of failure mechanism and failure characteristics. According to the good time frequency character and adaptive ability of wavelet analysis, it proposes gear fault information extraction method based on wavelet packet analysis and have carried out simulation analysis. The results show that the method of wavelet packet analysis can effectively detect mutations in the signal part and noise to achieve the diagnosis of mechanical system failures.