Modal parameters identification for short data sequences based on stratified sampling and optimism complex Morlet wavelet

A novel modal parameter identification method based on stratified sampling and optimism complex Morlet wavelet is proposed for short data sequences. Stratified sampling is applied to divide the structure response signal into different layers which called sub samples with different thresholds, and then free decrement response signal of each layer is extracted by random decrement technique. The optimism complex Morlet wavelet transform is applied to identify modal parameter of each layer, and the weight of the layer is also determined based on the sample standard deviation. The modal parameter of the structure can be obtained by weighted calculation.The engineering application shows that the proposed method has the ability to identify modal parameter accurately, decouple low frequency intensive modal composition and restrain high frequency fake modal effectively.     

Improved algorithms of modal parameter identification and its application to shaking table frame model test

Based on the data-driven stochastic subspace identification (SSI), an improved ITD method, an improved STD method and an improved complex index method are put forward. SSI has high identification accuracy, and Hankel matrix orthogonal projection calculation of data can retain all the useful information in the original data and simultaneously remove noise. The data obtained in P-matrix is taken as input for the ITD , STD and complex index methods, and then the three methods would no longer need random decrement technique or NExt method for pre-processing, thereby eliminating the error caused by the two pretreatment methods.The improved methods are applied to the modal parameter identification for structure shaking table test model by using the simulated earthquake response data from Tongji University. The recognition results further prove the correctness and feasibility of the improved methods.     

Structual Dynamic Characteristics Analysis and Improvement for Two-way Branch Exhaust Pipe Shield

The two-way branch exhaust pipe shield is the main source of surface radiate noise of a vehicle's engine.(Using) the finite element analysis software ANSYS,a finite element model of the shield is established,and then the(modal) analysis is done theoretically with this model,the result is consistent with the experiment.The result shows that this finite element model can be used for the next structual dynamic characteristics analysis.With this model,the relationship of structural parameter,structural style of the shield and the corresponding vibrative modal is analyzed.The(optimal) improved measures are adopted which is based on the practical structure of the shield.The analysis of the(improved) structure shows that the increase of natural vibration frequency is visible,the vibration noise of the shield is suppressed distinctly.     

QRS complexes detection using quadratic b-spline wavelet transform algorithm

Aiming at the low accuracy and low adaptability of wave detection, a QRS complexes detection algorithm is proposed based on quadratic b-spline wavelet, while combined with binary search algorithm and arc approximating curve algorithm. The signal is decomposed with quadratic b-spline wavelet through Mallat algorithm and the R wave is detected by adjusting the threshold with binary search and modulus maximumizing. The T wave and P wave are detected by using arc approximating curve algorithm based on the least square. This algorithm is certified with the ECG signals from MIT-BIH database and is demonstrated that the algorithm enhanced the adaptability of R wave detection and improved the accuracy of T wave and P wave detection. The simulation experiment shows that the improved algorithm can effectively improve the automatic detection capabilities of ECG signals.     

Real time monitoring for multivariate statistical process with on line multiscale filtering

By analyzing shortages of current MSPCA model, an on line multi variable statistical process monitoring method is proposed, which uses some concepts from online multi scale filtering and can be applied to sensor fault diagnosis. In the method, wavelet decomposition is employed to the signals using edge correction filter in a fixed length data window, and then wavelet denoising is conducted with wavelet threshold filtering. Next, an on line multi scale model is constructed for data combining wavelet transformation and adaptive PCA in the previous data window. This model avoids time waste in direct signal denoising and reduces time cost in multi scale data with conventional PCA, which eventually increases accuracy in fault diagnosis. Experiments on eight vibration signals of 6135D diesel engine under severe leak condition prove the practicability and feasibility of the proposed method.     

Least-square-based Optimization of Frequency Response Function Estimation in Modal Parameters Identification

The accuracy of modal parameters identification has the influence on the quality of dynamic characteristics analysis of mechanical structure directly. And the precision of the frequency response function estimation has the great influence on the accuracy of modal parameters identification. In the engineering the frequency response function can be estimated by the average power spectrum and cross-power spectrum with the hel Pof FFT. As the result of limited frequency resolution in the course of FFT and the noise, the accuracy of modal parameters identification is influenced by the frequency response function estimation. This paper has put forward a least-square-based optimization of frequency response function estimation in modal parameters identification. According to the optimized frequency response function, the peak value can be searched to identify the damped natural frequencies and damping ratio directly. An experiment has been made to validate the proposed method. The result indicates that the damped natural frequencies and damping ratio identified by the optimized frequency response function are closer to the true results than which without the optimized frequency response function. So the accuracy of modal parameters identification can be improved by adopting the proposed method.     

Parameter optimized Morlet wavelet de noising method based on cross validation method

A new de noising method based on parameter optimized Morlet wavelet is put forward. The Morlet wavelet is chosen as the mother wavelet because its shape is similar to the mechanical shock signals. The mother Morlet wavelet is improved by adding two parameters which decide the shape of the mother wavelet in time domain. The added parameters and the appropriate scale parameter for the wavelet transformation are designed by the cross validation method. Finally, the useful components of the signal can be obtained by the improved Morlet wavelet de noising method. The gear fault diagnosis experimental result shows that the proposed method has a good de nosing performance and it is effective in fault feature extraction.     

Filtering Construct of Adaptive Filter Grou Pwith Multi-scale Decomposition

The principle and method of the adaptive filter and the filtering with wavelet transform were analyzed, and the model and method of adaptive filtering with wavelet transforms for the transient signal was established. The separated noise of signal by the multi-scale decomposition of wavelet transforms, was the input signal of adaptive filter, and accordingly the optimal filtering method of signal-noise decomposition was realized. By the adaptive filter grou Pbased on the wavelet transform, the optimal filtering to the multi-noise of signal is achieved at the same time, and the method presented in this paper has the excellent filtering capability. Examples of application demonstrate that this method presented is excellent to realize the optimal estimate to the valuable signal and noise of the transient signal in the same frequency segment.     

Effective Coefficient Threshold for Complex Wavelet Transform

This paper constructs complex wavelet which used for suppressing white noise in PD,and then analyses some disadvantages of several existing wavelet threshold and presents a method of Effective Wavelet Coefficient(EWC) according to the characteristic of modulus maximum of PD signals.Then it is compared with maximin theory threshold selection and Stein unbiased risk estimate theory threshold selection.The findings suggest that the method of EWC is adaptive,and it can suppress noise completely with small distortion of PD signal.     

Application of Wavelet Denoising in Partial Discharge Online Monitoring of Transformer

Because of the strong interferences, such as discrete spectrum interferences (DSI), white noise and pulse-shaped interferences, it is still a difficult work to extract the partial discharge (PD) signal for transformer online monitoring techniques. Through deeply study on the automatic selection of threshold value and mother wavelet, the wavelet-based de-noising method for partial discharge signals is proposed. The analyzing results on simulation signal and field-measured signal indicate that the proposed method is fit for de-noising white noise but for DSI the comparably worse de-noising results is acquired. Hence it is predicted that good de-noising results will be achieved with the wavelet-based de-noising method combined with digital filtering method fitting for de-noising DSI.     

基于超静定方程的橡胶材料本构模型参数识别

由于橡胶材料具有非线性和大变形特性,使得描述橡胶力学特性的本构模型参数的确定比较烦琐和困难。为了提高橡胶本构模型参数识别的准确性,基于超静定方程求解原理推导出一种新的识别方法。以某橡胶衬套为例,识别的参数应用于有限元分析,对比试验数据,结果表明该识别方法可以准确识别橡胶材料参数,并且精度相对于最小二乘法有了明显改善,显示了超静定识别法的有效性和可靠性。     

基于近红外光谱的小麦品质分类研究

为了快速、简便、准确地鉴别小麦品质的类别,本研究提出了应用近红外光谱分析技术结合BP神经网络的鉴别方法对小麦进行品质分类。研究过程中对小麦样品的光谱数据进行了详细分析,采用马氏距离剔除了光谱数据中异常数据,并通过主成分分析说明利用近红外光谱鉴别小麦品质分类的可行性。为了提高所建模型的性能,采用SPXY算法对小麦样品进行合理的划分。并选取了一阶微分加归一化的预处理方法来处理光谱数据,消除无关信息和噪声对小麦光谱数据的影响。运用偏最小二乘法压缩光谱数据,减少了数据量,节省建模时间。最后采用BP神经网络方法建立了小麦品质分类模型。实验结果显示：模型的鉴别效果较好,对强筋样品识别的准确率高达94.4%,弱筋样品识别的准确率高达100%。实现了快速、准确地对小麦品质强筋和弱筋两类的鉴别,对小麦生产、市场交易及食品加工有着非常重要的意义。     

Improvement and Application of Immune Genetic Algorithm in Structural Damage Identification

In order to solve structural multi-damage identification problem, a two-stage method based on Bayesian theory and immune genetic algorithm (IGA) is presented. Firstly, structural modal strain energy and frequency are considered as two kinds of information sources, and Bayesian theory is utilized to integrate the two information sources and preliminarily detect structural damage locations. Then, immune genetic algorithm is used to identify structural damage extents. Considering the convergence rate of basic IGA is still not very good, some improved strategies, such as culture vaccine, two termination conditions, are presented. It is shown that the two-stage method can precisely identify structural damage locations and extent, and the calculated results of the proposed improved IGA are obviously better than those of both the basic IGA and simple genetic algorithm.     

Identification of combustion noise and mechanical noise in internal combustion engine

To identify internal combustion engine (IC engine) noise, a mathematical model is established, and the corresponding program is developed. By introducing global thresholds to decrease noise in the wavelet transforms program, the anti-interference of the program can be guaranteed, the spectrums of the combustion noise and mechanical noise are separated, and their A-weighted sound power levels are figured out. Taking a CG125 style motorcycle engine for an example, theoretical examples that include different interferential noises are constructed to simulate the identification and to validate the accuracy and practicability of the program. Results of the identification show that the spectrums of the combustion noise and mechanical noise before and after separation are agreed well; errors of A-weighted sound power level are all less than 0.6 dB.     

Quantitative analysis of damage extent of transmission tower by using strain energy method

In order to solve the structural damage identification problem of transmission tower, a two-step identification method based on change rate and dissipation rate of modal strain energy is proposed. The damage location is identified by the change rate of modal strain energy method. Then, the modal strain energy dissipation rate theory is analyzed further by using the element stiffness matrix after injury. As a result, a more accurate damage quantification equation method is introduced. This method requires only several modals of vibration. The numerical simulation results show that this two-step method can find the damage location and damage degree of transmission towers effectively.     

A Kind of Robust Identification Algorithm Research

A new robust identification algorithm which is based on the estimation of parameter model,i.e.,Optimal Volume Ellipsoid parameter set identification algorithm is presented in this paper.It identifies not only a parameter point(the center of the ellipsoid) but also the error bounds of each parameter.It employs noise bounds in identification algorithm and dynamically modifies coefficients of identification convergent speed and direction in order to attain satisfactory perfomance.Digital simulation results show that is possesses high accuracy,dramatic convergent speed,excellent performance against noise and strong robustness as well as uniform convergent,which is obviously superior to traditional identification schemes.     

Testing and modal analysis on multi stage planetarygearbox housing of shield tunnelling machine

The finite element model is developed for the multi stage planetary gearbox housing in shield tunnelling machine (STM), and the the eigenvalue problem is solved by using block Lanczos method, and then the low order natural frequencies and corresponding modes are extracted. Based on the theory of experimental modal analysis, the modal testing is made for the gearbox housing by pulse excitation method. The least squares complex frequency domain (LSCF) estimation method is used to analyze the modal data, and the natural characteristics of housing is obtained. According to the modal assurance criterion (MAC), the experimental modal parameters are validated. Modal analysis shows that the experimental data is coincided with analytical results, which indicates the accuracy of the theoretical model and testing method. The coupling sympathetic vibration is disappearing between the gears train and housing, the local vibration is large at the high speed side of gearbox housing, and the torsional mode is outstanding under operating condition. The research results provide theoretical basis and experimental support for the structural optimization of the gearbox of STM.     

Wavelet Packet Decomposition Based on Entropy Threshold for White-noise Rejection in PD Signal

This paper analyzes the different characteristics of white-noise interference in the signals of partial discharge (PD) after wavelet transform. There is high value in lots of scales for PD and white-noise interference is to zero with increasing scale. The threshold is set for wavelet coefficient in all scales. If the coefficient of signal is higher than the threshold, it is PD signal. Otherwise it is noise interference. A threshold-based wavelet packet transform (WPT) algorithm is put forward to suppress white noise interference in PD signals. The results testifies that it has a favorable adaptability to extract PD signals using WPT.     

EEG Signal Denoising Based on Wavelet Transform

The article focuses on the method of noise cancellation for EEG signal. The method of notch filter is discussed. According to the frequency of noise and the principle of notch filter, the design result of the notch filter and the denoised signal are presented. Then, the analysis of EEG signal are proposed based on wavelet transform (WT) and noise cancellation using WT. Wavelet transform is a multi-resolution time-frequency analysis method. It can decompose mixed signal into signals at different frequency bands. The EEG signal is analyzed and denoised using WT, then the results are presented respectively. Comparing the experiment results shows that WT can detect and process noise in the EEG signal effectively.     

Dynamic Characteristics Analysis and Structure Design of Improvement for Exhaust Manifold Cover

The exhaust manifold cover is the main source of surface radiate noise of a 6 360 vehicle's engine.By using the finite element analysis technology,a finite element model of the cover is established,and then the modal analysis is done theoretically with this model,the result is consistent with the experiment.On this basis,the transient response of the cover under the excitation of engine block's vibration is analyzed.The result show that the free edge is the main source of radiate noise,because its normal velocity is maximal.The improved measures are adopted which is based on the practical structure of the cover.The analysis of the improved structure shows that the measures are effective,the radiate noise of the cover is decreased distinctly.