Research on fiber optic gyro signal de-noising based on wavelet packet soft-threshold

Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro （FOG）. The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6°/h.

Implementation of GPR Signals De-Noising Based on DSP

An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor （DSP） based on wavelet shrinkage algorithm. In order to realize real-time GPP, signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimenial results show that the DSP based application not only basically meets the real-time requirement of GPP, signals analysis, but also assures the quality of the GPR signals analysis.

Moving horizon based wavelet de-noising method of dual-observed geomagnetic signal for nonlinear high spin projectile roll positioning

Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal deforms in the exposed and magnetically contaminated environment.In order to preciously recognize the roll information and effectively separate the noise component from the original geomagnetic sequence,based on the error source analysis,we propose a moving horizon based wavelet de-noising method for the dual-observed geomagnetic signal filtering where the captured rough roll frequency value provides reasonable wavelet decomposition and reconstruction level selection basis for sampled sequence;a moving horizon window guarantees real-time performance and non-cumulative calculation amount.The complete geomagnetic data in full ballistic range and three intercepted paragraphs are used for performance assessment.The positioning performance of the moving horizon wavelet de-noising method is compared with the band-pass filter.The results show that both noise reduction techniques improve the positioning accuracy while the wavelet de-noising method is always better than the band-pass filter.These results suggest that the proposed moving horizon based wavelet de-noising method of the dual-observed geomagnetic signal is more applicable for various launch conditions with better positioning performance.

Automatic de-noising and recognition algorithm for drilling fluid pulse signal

Wavelet forced de-noising algorithm is suitable for denoising of unsteady drilling fluid pulse signal, including baseline drift rectification and two-stage de-noising processing of frame synchronization signal and instruction signal. Two-stage de-noising processing can reduce the impact of baseline drift and determine automatic peak detection threshold range for signal recognition by distinguishing different features of frame synchronization pulse and instruction pulse. Rising and falling edge relative protruding threshold is defined for peak detection in signal recognition, which can make full use of the degree of the signal peak change and detect peaks flexibly with rising and falling edge relative protruding threshold combination. A synchronous decoding method was designed to reduce position uncertainty of the frame synchronization pulse and eliminate the accumulative error of time base drift, which determines the first instruction pulse position according to position of the frame synchronization pulse and decodes subsequent instruction pulse by taking current instruction pulse as new bit synchronization pulse. Special tool software was developed to tune algorithm parameters, which has a decoding success rate of about 95% for the universal coded signals. For the special coded signals with check byte, decoding success rate using the automatic threshold adjustment algorithm is as high as 99%.

A De-Noising Method for Track State Detection Signal Based on EMD

In the track irregularity detection, the acceleration signals of the inertial measurement unit (IMU) output which with low frequency components and noise, this paper studied a de-noising algorithm. Based on the criterion of consecutive mean square error, a de-noising method for IMU acceleration signals based on empirical mode decomposition (EMD) was proposed. This method can divide the intrinsic mode functions (IMFs) derived from EMD into signal dominant modes and noise dominant modes, then the modes reflecting the important structures of a signal were combined together to form partially reconstructed de-noised signal. Simulations were conducted for simulated signals and a real IMU acceleration signals using this method. Experimental results indicate that this method can efficiently and adaptively remove noise, and this method can better meet the precision requirement.

STABLE RECOVERY OF SPARSELY CORRUPTED SIGNALS THROUGH JUSTICE PURSUIT DE-NOISING

This paper considers a corrupted compressed sensing problem and is devoted to recover signals that are approximately sparse in some general dictionary but corrupted by a combination of interference having a sparse representation in a second general dictionary and measurement noise.We provide new restricted isometry property(RIP)analysis to achieve stable recovery of sparsely corrupted signals through Justice Pursuit De-Noising(JPDN)with an additional parameter.Our main tool is to adapt a crucial sparse decomposition technique to the analysis of the Justice Pursuit method.The proposed RIP condition improves the existing representative results.Numerical simulations are provided to verify the reliability of the JPDN model.

A NEW DE-NOISING METHOD BASED ON 3-BAND WAVELET AND NONPARAMETRIC ADAPTIVE ESTIMATION

Wavelet de-noising has been well known as an important method of signal de-noising. Recently,most of the research efforts about wavelet de-noising focus on how to select the threshold,where Donoho method is applied widely. Compared with traditional 2-band wavelet,3-band wavelet has advantages in many aspects. According to this theory,an adaptive signal de-noising method in 3-band wavelet domain based on nonparametric adaptive estimation is proposed. The experimental results show that in 3-band wavelet domain,the proposed method represents better characteristics than Donoho method in protecting detail and improving the signal-to-noise ratio of reconstruction signal.

Coefficients de-noising with wavelet transform for magnetic flux leakage data obtained from oil pipeline

This paper considers the problem of noise cancellation for the magnetic flux leakage (MFL) data obtained from the inspection of oil pipelines. MFL data is contaminated by various sources of noise, and the noise can considerably reduce the detectability of flaw signals in MFL data. This paper presents a new de-noising approach for removing the system noise contained in the MFL data by using the coefficients de-noising with wavelet transform. Experimental results are presented to demonstrate the advantages of this de-noising approach over the conventional wavelet de-noising method.

Improved signal de-noising in underwater acoustic noise using S-transform: A performance evaluation and comparison with the wavelet transform

Sound waves propagate well underwater making it useful for target locating and communication.Underwater acoustic noise(UWAN)affects the reliability in applications where the noise comes from multiple sources.In this paper,a novel signal de-noising technique is proposed using S-transform.From the time-frequency representation,de-noising is performed using soft thresholding with universal threshold estimation which is then reconstructed.The UWAN used for the validation is sea truth data collected at Desaru beach on the eastern shore of Johor in Malaysia with the use of broadband hydrophones.The comparison is made with the more conventionally used wavelet transform de-noising method.Two types of signals are evaluated:fixed frequency signals and time-varying signals.The results demonstrate that the proposed method shows better signal to noise ratio(SNR)by 4 dB and lower root mean square error(RMSE)by 3 dB achieved at the Nyquist sampling frequency compared to the previously proposed de-noising method like wavelet transform.

De-Noising of ECG Signals by Design of an Optimized Wavelet

In this paper, a different method for de-noising of ECG signals using wavelets is presented. In this strategy, we will try to design the best wavelet for de-nosing. Genetic algorithm tests wide range of quadrature filter banks and the best of them will be chosen that minimize the Signal-to-Noise Ratio (SNR). Furthermore, the wavelet function and scaling function related to these filters are reported as the best wavelet for de-noising. Simulation results for de-noising of a noisy ECG signal show that using obtained wavelet by proposed method improves the SNR of about 2.5 dB.

不同粗糙度煤岩界面超低摩擦效应与声发射特征试验研究

为了揭示动载扰动作用下煤岩界面粗糙度对超低摩擦型冲击地压影响机制,采用自行研制的煤岩界面超低摩擦试验装置,以沈阳红阳三矿1082 m采深煤岩体为研究对象,通过改变煤块与砂岩块体表面粗糙度来模拟煤岩界面不同粗糙特性,以粗糙度系数表征煤岩界面粗糙程度,工作块体水平位移表征冲击过程中超低摩擦效应强度,声发射能量为工作块体摩擦滑动过程中的信号参数,进行应力波扰动下不同粗糙度煤岩界面超低摩擦试验.研究结果表明:(1)超低摩擦滑动过程中,工作块体水平位移、声发射能量计数以及累计能量曲线呈现出孕育阶段、激发阶段、稳定阶段变化特征;(2)煤岩界面粗糙度越小,工作块体水平位移和声发射能量峰值越大,煤岩界面越易发生超低摩擦效应;(3)不同煤岩界面粗糙度下,相比于其他扰动频率, 2 Hz时更易发生超低摩擦效应;(4)给出了声发射能量峰值与煤岩界面粗糙度系数对应关系.声发射能量峰值可以有效表征超低摩擦效应强度,可以用声发射能量峰值预测超低摩擦效应强度.

基于HHT的混凝土损伤AE信号分析新方法

介绍了HHT这一非线性、非平稳信号的处理方法。针对混凝土材料损伤所产生的声发射信号复杂的特点,提出了基于HHT的分析混凝土材料中产生的声发射信号的新方法,借此分析了从预应力混凝土梁在三点弯曲载荷下的破坏实验中拾取的实际AE信号,获得了该损伤信号的Hilbert谱。通过与小波分析结果的比较,显示出该方法具有处理精度高、自适应性强的特点,能有效地提取声发射信号中损伤成分的主要特征,为AE信号处理提供了一种新的途径。

基于AE信号与优化SVM的刀具磨损状态识别

根据小波包变换理论,对刀具磨损声发射(AE)信号进行滤波和能量特征值提取。采用分步式扫描的方法对传统的盒计数法进行改进,并利用改进的盒计数法计算滤波后信号的广义分形维数特征值。以上述提取的特征值为备选特征,采用支持向量机(SVM)作为分类器,利用量子遗传算法(QGA)首先对分类器的输入特征进行筛选,之后对分类器的模型参数进行优化。利用优化后的分类器对测试样本进行分类,测试结果表明,该方法可以对刀具磨损状态进行有效识别。

基于ICEEMDAN-SSA-Wavelet的声发射信号降噪研究

针对粘接件声发射(AE)信号含有噪声分量难以滤除的问题,提出一种改进ICEEMDAN的方法。该方法首先使用ICEEMDAN分解原始AE信号,并通过相关系数和能量差值的方法筛选出低频分量和高频分量;运用麻雀优化算法(SSA)优化后的改进小波阈值去噪算法对其进行去噪;最后将保留的低频分量和去噪后的高频分量重构成一个新的信号,通过实验数据对比和分析评估降噪效果。实验结果表明,相较于改进小波阈值去噪和ICEEMDAN去噪,文中提出的方法对金属与非金属粘接件AE信号的降噪效果更好,能够保护原始信号的频域信息,进而提高脱粘检测精度。

AE信号归原处理法在砂轮磨钝监测中的应用

利用声发射(AE)信号的归原处理法,在线监测小批量、多品种磨削过程砂轮钝化程度,实验结果表明,该方法获得的结果与砂轮钝化有很好的一致性。

基于AE信号的新型砂轮点磨削状态监测方法

提出了一种基于声发射(AE)信号对新型点磨削砂轮磨削状态进行实时监测方法.建立了表面粗糙度与AE信号的对应关系,为监测磨削加工表面粗糙度提供了条件.采用单因素实验研究了各参数对AE信号RMS值的影响规律,结果进一步证明了AE信号与表面粗糙度的对应关系.对比分析了砂轮不同磨损状况下的AE信号,依据此信号可对磨削状态进行实时监测.为了区分声发射源性质的异同,对磨削过程中的AE信号进行了频谱分析,砂轮发生磨损时,AE信号在45~65 k Hz,80~90 k Hz,100~110 k Hz频段的能量升高显著,并且在15 k Hz附近出现了很高的尖峰,为监测磨削状态提供了一种可行且有效的方法.

声发射(AE)信号在砂轮钝化程度检测中的应用

本文利用声发射 (AE)信号的归原处理法 ,在线监测小批量、多品种磨削过程砂轮钝化程度 ,利用该方法可以克服仅靠监测AE信号幅值变化不能监测工件材料、加工要求和磨削参数经常变化环境下砂轮钝化程度的缺陷 ;实验结果表明 ,声发射(AE)

多道ICP-AES多元素瞬时信号检测、处理集成软件的开发

介绍了多道ICP AES多元素瞬时信号检测处理集成软件的研制过程 ,应用该软件实现了多道ICP AES和多种瞬时进样技术在线联用的信号采集。

基于数值模拟的栓塞修复过程中AE信号分析

为探讨木质部栓塞修复过程监测到大量AE信号现象的原因,基于Rayleigh-Plesset方程建立描述栓塞修复过程中空化泡的运动方程,并用数值模拟方法得到空化泡运动方程的解,对AE信号进行动力学分析。结果表明:当木质部液水势升高时,空化泡会塌缩为半径较小的气泡。但经过一次塌缩并不会使栓塞修复。在压强升高的情况下,气泡会经多次塌缩直到形成稳定的微小气泡,栓塞才会完全修复。在气泡多次塌缩的过程中,气泡半径减小,但是并不是单调减小而是以减幅振动的方式减小,从而形成振源,成为AE信号的来源之一。

基于声发射信息熵均距的滑动轴承润滑状态故障诊断研究

在声发射信号的信息熵距的基础上,提出了声发射信号的信息熵均距诊断方法,该方法能有效提升汽轮机上滑动轴承润滑状态的诊断精度,通过突出润滑状态的信息熵特征和改变信息熵点之间的距离算法,使不同润滑状态之间的差异更明显,以增强对润滑状态的准确识别能力。该效果在半干摩擦状态的诊断上表现最显著,并在实际机组上验证了该方法的有效性,为滑动轴承润滑状态的诊断和故障预测提供了更可靠的方法。