基于AWM和OSVD的局部放电白噪声抑制方法

局部放电(partial discharge,PD)检测是评估电力电缆绝缘状态的主要手段之一,而白噪声是局部放电检测中不可避免的一种干扰。为了有效抑制局部放电白噪声,提出一种基于自适应波动(adaptive wave motions,AMW)与最优奇异值分解(optimal singular value decomposition,OSVD)的局部放电白噪声联合抑制方法。该方法首先利用AWM对含噪局放信号进行初次白噪声抑制,然后采用自适应阈值时间窗提取PD脉冲,最后采用OSVD去噪法进一步去除PD脉冲的冗余白噪声,从而实现白噪声的有效抑制。使用离散小波变换(discrete wavelet transform,DWT)、最优奇异值分解、短时奇异值分解法(short-time singular value decomposition,STSVD)及该文所提方法对含噪的局放仿真信号和实验室实测信号进行白噪声抑制,并对比三者去噪效果。结果表明:该方法不仅具有更好的白噪声抑制性能,当数据量较大时,还具有更高的执行效率,其工程应用价值较好。

PRINCIPLE AND EXPERIMENTAL DEMONSTRATION OF ADAPTIVE CANCELLATION OF STRUCTURAL VIBRATION IN TIME DOMAIN

This paper presents the principle and critical factors of adaptive cancellation of structural vibration in time domain(ACSV-TD).Digital-analog simulations and model tests are conducted on cancelling forced vibration of a cantilever beam.Filtered-X RLS algorithm is used to get faster convergence speed and stronger adaptability (in comparison with LMS algorithm). The results demonstrate the efficiency and adaptability of the ACSV-TD.

Robust adaptive dynamic surface control for nonlinear uncertain systems

We propose a new method for robust adaptive backstepping control of nonlinear systems with parametric uncertainties and disturbances in the strict feedback form. The method is called dynamic surface control. Traditional backstepping algorithms require repeated differentiations of the modelled nonlinearities. The addition of n first order low pass filters allows the algorithm to be implemented without differentiating any model nonlinearities, thus ending the complexity arising due to the 'explosion of terms' that makes other methods difficult to implement in practice. The combined robust adaptive backstepping/first order filter system is proved to be semiglobally asymptotically stable for sufficiently fast filters by a singular perturbation approach. The simulation results demonstrate the feasibility and effectiveness of the controller designed by the method.

FREQUENCY-DOMAIN IMPLEMENTATION OF FILTERED-X ALGORITHMS WITH ON-LINE SYSTEM IDENTIFICATION FOR VIBRATION CONTROL

This paper describes the implementation of frequency-domain least mean squares (LMS) and Filtered-X algorithms and compares the performance of the frequencydomain adaptive control algorithm to a comparable timedomain controller. When the frequency-domain LMS step size is allowed to vary as a function of frequency,the frequency-domain algorithm exhibits a better vibration reduction than the time-domain algorithm for the weaker frequencies in the energy spectrum.

Theoretical analysis of adaptive harmonic window and its application in frequency extraction of vibration signal

The goal of this paper is to find an excellent adaptive window function for extracting the weak vibration signal and high frequency vibration signal under strong noise.The relationship between windowing transform andfiltering is analyzed first in the paper.The advantage of adjustable time-frequency window of wavelet transform is introduced.Secondly the relationship between harmonic wavelet and multiple analytic band-pass filter is analyzed.The coherence of the multiple analytic band-pass filter and harmonic wavelet base function is discussed,and the characteristic that multiple analytic band-pass filter included in the harmonic wavelet transform is founded.Thirdly,by extending the harmonic wavelet transform,the concept of the adaptive harmonic window and its theoretical equation without decomposition are put forward in this paper.Then comparing with the Hanning window,the good performance of restraining side-lobe leakage possessed by adaptive harmonic window is shown,and the adaptive characteristics of window width changing and analytical center moving of the adaptive harmonic window are presented.Finally,the proposed adaptive harmonic window is applied to weak signal extraction and high frequency orbit extraction of high speed rotor under strong noise,and the satisfactory results are achieved.The application results show that the adaptive harmonic window function can be successfully applied to the actual engineering signal processing.

Adaptive WNN aerodynamic modeling based on subset KPCA feature extraction

In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.

Minimum variance adaptive beamforming combined with coherence factor weighting applied to ultrafast active cavitation imaging

The ultrafast active cavitation imaging(UACI)based on plane wave transmission and delay-and-sum(DAS)beamforming has been developed to monitor cavitation events with a high frame rate.However,DAS beamforming leads to images with limited resolution and contrast.In this paper,minimum variance(M V)adaptive beamforming and coherence factor(CF)weighting are combined to achieve an MVCF-based UACI,which can improve the cavitation imaging quality.The detailed algorithm evaluation has been investigated from both simulation and experimental data The simulation data include10point targets and a cyst,while the experimental data are obtained by detecting the dissipation of cavitation bubbles in water excited by a single element transducer with frequency of1.2MHz.The advantages of the proposed methodology as well as the comparison with conventional B-mode,DAS?M V,DAS-CF and MV on the basis of compressive sensing(CS)(called MVCS)beamformers are discussed.The results show that MVCF beamformer has a significant improvement in terms of both resolutions and signal-to-noise ratio(SN R).The MVCF-based UACI has a SNR at21.82dB higher,lateral and axial resolution at2.69times and1.93times?respectively,which were compared with those of B-mode active cavitation mapping.The MVCF-based UACI can be used to image the residual cavitation bubbles with a higher SNR and better spatial resolution

A new improvement of TCP based on adaptive filtering

On the basis of studying the standard TCP retransmission mechanism, this paper proposes a method to adopt the theory of adaptive filtering in the field for the estimation of round trip time （RTT）. Then this method is embedded in TCP for estimation of RTT, and defined as RTT-AF TCP. In the schemes with Only-TCP flow and with TCP and UDP flow fighting for the link, the experiment results were made and analyzed by using the standard TCP and the RTT-AF TCP. The experimental results show higher throughput and lower loss rate of packets by using RTT-AF TCP for data transmission, and achieve excellent effect.

海上油气设施数据整合系统循环冗余校验方法

由于传输数据的规模不断增加,导致校验的时间开销逐渐增大,为此,提出海上油气设施数据整合系统循环冗余校验方法研究,提取系统数据循环的特征,将其作为个性化循环冗余校验编码设计的基础,采用SDN技术计算在最小循环步长下,数据传输冗余与校验编码的相似性是否在自适应波动阈值范围内,实现对数据的校验。测试结果表明,设计方法对系统数据校验的耗时始终保持在5 ms以内。

Maximal-overlap adaptive multiwavelet for detecting transient vibration responses from dynastic signal of rotating machineries

Vibration signal is an important prerequisite for mechanical fault detection. However, early stage defect of rotating machiner- ies is difficult to identify because their incipient energy is interfered with background noises. Multiwavelet is a powerful tool used to conduct non-stationary fault feature extraction. However, the existing predetermined multiwavelet bases are independ- ent of the dynamic response signals. In this paper, a constructing technique of vibration data-driven maximal-overlap adaptive multiwavelet （MOAMW） is proposed for enhancing the extracting performance of fault symptom. It is able to derive an opti- mal multiwavelet basis that best matches the critical non-stationary and transient fault signatures via genetic algorithm. In this technique, two-scale similarity transform （TST） and symmetric lifting （SymLift） scheme are combined to gain high designing freedom for matching the critical faulty vibration contents in vibration signals based on the maximal fitness objective. TST and SymLift can add modifications to the initial multiwavelet by changing the approximation order and vanishing moment of mul- tiwavelet, respectively. Moreover, the beneficial feature of the MOAWM lies in that the maximal-overlap filterbank structure can enhance the periodic and transient characteristics of the sensor signals and preserve the time and frequency analyzing res- olution during the decomposition process. The effectiveness of the proposed technique is validated via a numerical simulation as well as a rolling element beating with an outer race scrape and a gearbox with rub fault.

Simple Adaptive Filtering Scheme to Improve Measurement Accuracy of Gyroscope on Angular Motion Base

The objective of this work is to improve the measurement accuracy of a gyroscope on a angular motion base with a simple adaptive filter scheme.Two main topics are highlighted in this work.The first topic is to show building a dual-process model employed for the conventional Kalman filter.The second topic is to show developing a modified noise adaptive algorithm when measurement noise and process noise are unknown.The experimental results are presented to show that the simple adaptive filtering scheme outperforms the other conventional scheme in this paper in terms of noise reduction.