A study of polynomial fit-based methods for qualitative trend analysis

Qualitative trend analysis (QTA) of sensor data is a useful tool for process monitoring, fault diagnosis and data mining. However, because of the varying background noise characteristics and different scales of sensor trends, automated and reliable trend extraction remains a challenge for trend-based analysis systems. In this paper, several new polynomial fit-based trend extraction algorithms are first developed, which determine the parameters automatically in the hypothesis testing framework. An existing trend analysis method developed by Dash et al. (2004) is then modified and added to the abovementioned trend extraction algorithms, which form a complete solution for QTA. The performance comparison of these algorithms is made on a set of simulated data and Tennessee Eastman process data based on several metrics.     

隐Markov模型的多算法交叉耦合自适应算法

以离子通道信号重构为例，扩展HMM为矢量隐Markov模型，利用随机逼近原理对期望最大算法进行自适应改造，估计离子通道的动力学特征参数；递归辅助变量算法估计背景噪声的统计特征；卡尔曼滤波预测背景噪声；三种算法交叉耦合重构离子通道信号。该算法能够克服滤波器和背景噪声的影响，在低信噪比情况下得到了较高精度的估计参数和重构信号，具有鲁棒性和一致收敛特性。     

On-line identification scheme using adaptive filters

An on-line scheme for identifying a linear process is proposed which consists of a linear time-varying filter and a parameter update algorithm. The disturbances affecting the process, its input and its output, belong to a general class of signals which are a mixture of stochastic and deterministic signal processes generated by some linear time-invariant system excited by white noise and the Dirac delta function, respectively. The process and the disturbance signal models are not restricted to be asymptotically stable. Either a probing input signal or a normal operating input signal can be employed. The probing signal consists of a finite number of sinusoidal signals (exponentially increasing sinusoidal signals for unstable processes) of distinct frequencies. When a normal operating signal is used, an adaptive scheme is employed to tune the parameters of the filters to the distinct frequency components of the signal. The convergence of the parameter estimates to their true value is established.     

自适应滤波器在超宽带穿墙雷达中的应用研究

作为一种新型探测和定位墙后人体目标的方法,超宽带穿墙雷达在军事和民用多个领域得到了广泛的应用。由于目标信号经常被强背景噪声所淹没,为实现动目标检测,需寻求一种有效的背景相消方法以凸显动目标信号,提高动目标检测和定位的精度。在研究回波信号特点的基础上,考虑到无限冲击响应（IIR）滤波器和自适应滤波器的优点,提出采用基于IIR的自适应滤波器背景噪声相消的方法,抑制回波信号中的噪声。为验证方法的有效性,采用该方法对利用SIR20雷达收集的实测数据进行了相关处理。从结果看,该方法能有效抑制背景噪声,凸显目标信号,为目标的检测和定位奠定了良好的基础。     

Stable on-line parameter identification with general knowledge on level of information noise discrete-time case

An on-line parameter identification problem is posed and solved for discrete-time systems with general knowledge on the level of the inherent information noise. The knowledge can be the bound on either the magnitude or the finite-index ^p norm, pε[1, ∞), of the noise. Based on the knowledge, a switching type gradient algorithm (or called gradient algorithm with dead zone) is proposed to estimate the parameters of the system from the available input-output data. In spite of the existence of the noise, this on-line algorithm guarantees that the estimation error is monotonically decreasing, and the parameter estimate is convergent to a steady-state value under a mild condition. Furthermore, the algorithm is stable in the sense that the estimation error will converge to zero as the bound on the noise gradually diminishes.     

大型雷电定位系统中实时自适应联合检测算法

雷电电磁辐射的持续时间具有随机性。采用能量法检测雷电数据块,当信号长度远短于数据块长度时,将会产生噪声淹没信号现象而引起检测概率降低的问题。发现可利用峰度来描述含有短雷电信号的数据块的波形特征,而且能量块检测与特征检测具有互补特性。为了提高检测概率,将能量块检测和特征检测相结合,利用自动筛选思想和删余检测技术实时估计背景噪声,提出了实时自适应联合雷电检测算法。通过对实采的雷电数据进行实验,结果表明,所提出的检测算法能够明显提高检测概率,表明了其有效性和实用性。     

基于Transformer的汽轮机滚动轴承早期微弱故障检测方法研究

为了应对滚动轴承早期微弱故障的挑战本文提出了一种新的方法。该方法首先采用PCA（主成分分析）对振动信号进行特征筛选，以降低数据维度，有效地简化了振动数据的结构，增强了特征的表达力。接着，使用CEEMDAN（完全自适应噪声集合经验模态分解）算法来分解被背景噪声干扰的微弱故障振动信号，它通过在经验模态分解（EMD）的基础上引入自适应噪声，增强了对微弱特征的识别能力，有够效地分离出趋势和噪声数据，显著提高了故障诊断的准确性。最后，引入Transformer模型，进一步优化了特征的提取和表征，实现对长序列数据的高效处理，用于微弱故障特征的提取和表征。这一综合方法具有降维、噪声抑制和长序列处理等多重优势，有望在滚动轴承故障检测中取得显著成果。     

强脉冲噪声环境下的多径衰信道估计

提出一种在强干扰脉冲噪声存在下对无线多径信道进行估计的算法.在无线通信系统中,衰落信道可以采用自回归(AR)模型建模,通过RLS算法和自适应Kalman滤波器分别对AR模型的参数进行估计,但是,这两种算法对噪声干扰非常敏感.为了加快RLS算法的收敛性,并有效抑制大脉冲干扰的影响,在算法的改进中引入了抑制因子,用于对脉冲干扰幅度的抑制.仿真结果显示:相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估参数的收敛速度.     

Adaptive picking of microseismic event arrival using a power spectrum envelope

An automatic, adaptive, spectrogram-based algorithm for picking the arrival time of microseismic data is proposed. The algorithm provides a significant improvement in the ratio of detected events to false triggers and in the resolution of the microseismic structure. It mainly addresses the problem of automatic picking when the signal-to-noise ratio (S/N) is low and so false and missed triggers frequently occur. It combines the short time average/long time average (STA/LTA) algorithm with an envelope algorithm. It also constructs an envelope from a time-frequency representation of the signal. The threshold is set dynamically, according to the existing noise level and the S/N ratio. The algorithm also uses the fixed LTA value to represent the noise level for a seismic record. It is applied to pick the arrival times of P-waves of local events recorded at eight stations.     

基于无源振动传感器标签的轴承故障在线诊断技术

提出了一种基于无源振动传感器标签的穿梭车轴承故障在线诊断技术.设计了一种无源射频识别(RFID)振动传感器标签结构,因其工作在无源模式下,减少了在线故障诊断的成本,同时可以实现对轴承故障的长期在线诊断.介绍了振动信号的处理方式,提出了基于奇异熵的奇异值分解信号降噪算法,依据信号的奇异熵自行定阶降噪,避免了人为预设参数所导致的误差,并提出了基于最小二乘支持向量回归(LS-SVR)的故障诊断算法.测试结果表明:设计的标签能够可靠地完成信号采集和传输,采用的算法能够快速而准确地定位故障,较传统故障诊断方法提高了实时性并降低了成本.     

Normalized estimation of fundamental frequency and measurement of harmonics/interharmonics

An adaptive notch filter is presented to estimate the fundamental frequency and measure both harmonics and interharmonics of an almost periodic signal with unknown time-variant fundamental frequency, which has the robustness that the convergence speed is determined by neither amplitude nor frequency of fundamental component. The algorithm forms a one-dimensional slow adaptive integral manifold whose existence and stability are proved by averaging method and Lyapunov stability theorem. The local exponential stability and the ultimate boundedness of fundamental frequency estimation are proved. The local exponential stability makes sure that the fundamental frequency, the harmonic and interharmonic components can be all fast tracked. The principle for adjusting the parameters with their influences on transient and steady-state performance is investigated and decreasing parameters can improve noise characteristic. The validity is verified by simulation results.     

基于EMD-SVD差分谱的DWT域LMMSE自适应信道估计算法

针对当前基于奇异值分解的线性最小均方误差（SVD-LMMSE）法信道估计误差相对较大的问题,提出了一种基于经验模态分解和奇异值分解（EMD-SVD）差分谱的离散小波变换（DWT）域线性最小均方误差（LMMSE）自适应信道估计算法。在对信号进行最小二乘（LS）信道估计及预滤波处理后,运用DWT对信号的高频系数进行阈值量化去噪处理;然后结合基于EMD-SVD差分谱的自适应算法,将强噪声小波系数中微弱的有效信号提取出来,并进行信号的重构;最后根据循环前缀（CP）内、外噪声方差的均值设置相应门限,对循环前缀以内的噪声进行再次处理,从而进一步降低噪声的影响。对算法的误码率（BER）和均方误差（MSE）性能进行实验仿真,实验结果表明：所提算法的整体性能明显优于经典的LS算法、传统的LMMSE算法和目前较为流行的SVD-LMMSE算法,能够较好地降低噪声的影响,并可有效提升信道估计的精确度。     

Computational noise effects on adaptive filter algorithms

Finite word length arithmetic roundoff noise in adaptive filter algorithms results in statistical variations in the filter weight vector about the infinite precision arithmetic weight vector. These roundoff errors may be modeled as a statistically non stationary driving noise affecting weight mean and covariance convergence. Mean and covariance expressions and bounds are desired for word lengths in fixed-point arithmetic by making use of multiplication roundoff error models. The adaptive filter algorithms consist of the LMS algorithm, the Widrow-Hoff LMS algorithm, pilot-vector algorithm and clipped vector algorithm. All of these algorithms can be implemented on-line and real-time. However, only the behavior of the LMS algorithm is reported here. The implementation of the adaptive filter algorithms in finite word length arithmetic is most evident in minicomputer, microprocessor, and dedicated digital signal processors for on-line real-time signal identification and parameter estimation in many disciplines. Radar signal processing, adaptive beam forming, acoustic signal identification, communication channel enhancement have a definite need for advanced filtering concepts. Our adaptive algorithms are typically employed in these filter configurations. These filters can also be employed in phase distortion equalizers. A particular advantage of these filters is that they can be trained to equalize a variety of distortions. Should a particular distortion scenario change in time, the filters can be made to easily adapt to the new problem.     

On-line system identification in real time using a minicomputer

The parameters of the dynamic model of a linear system are estimated from the measurements of the input and output data by using the recursive algorithm for matrix pseudoinverse. For measurements contaminated with noise, the stochastic approximation algorithm is employed in conjunction with the matrix pseudoinverse algorithm for obtaining unbiased least-squares estimates. Both the methods are tested on-line in real time using the PDP 11/45 minicomputer while the system is simulated on a TR-20 analogue computer. The results confirm the feasibility of using the algorithms to identify the parameters of a class of industrial processes on-line using a minicomputer. This has special application to the adaptive control of such processes, based on rapid identification of slowly-varying parameters.     

强噪声背景下钢丝绳损伤信号降噪方法

钢丝绳损伤信号是一种非平稳无周期性的冲击信号,其特征信号的降噪处理和特征提取成为亟待解决的难题。小波变换方法若小波基或者分解层数不适合,会在信号降噪的同时引入其他噪声干扰,影响信号处理与特征提取的效果。相较于小波变换方法,移位平均法只需要选择一定的移位窗宽即可实现对信号的有效降噪,但移位窗宽需要人为选择,盲目性大。针对上述问题,提出一种强噪声背景下钢丝绳损伤信号降噪方法。利用钢丝绳漏磁检测传感器采集不同类型的断丝数据,向信号中加入强高斯白噪声,以模拟强噪声背景;采用自适应移位平均法对钢丝绳损伤信号进行降噪,利用量子粒子群优化(QPSO)算法优化移位平均法的窗宽;将损伤信号的信噪比(SNR)作为适应度函数,通过QPSO算法使得损伤特征信号SNR最大化,从而实现最优信号降噪效果。实验结果表明,对于强噪声背景下的钢丝绳平稳和波动信号,相较于小波变换,自适应移位平均法的降噪效果更明显,信噪比更高,信号更为平滑。实测结果表明,对于现场采集的噪声相对弱一些的钢丝绳损伤信号,自适应移位平均法的降噪效果也比小波变换好,验证了自适应移位平均法具有较好的通用性。     

Adaptive and learning algorithms for seismic detection of personnel

This correspondence is concerned with adaptive digital processing to extract impulse-like signal features from the correlated background noise for detection of intruders with the seismic sensor data. Both the adaptive digital filtering and the adaptive Kalman filtering methods are developed and shown to perform nearly the same for a short data segment. For continued processing of a long duration seismic record, the adaptive Kalman filtering considered has better capability to learn the nonstationary data characteristics than the considered adaptive filtering and to adaptively remove the background noise. Detailed experimental results are presented. Other considerations such as the hardware implementation and the relationships among the parameters are also examined.     

基于误差通道并行建模的主动控制系统

提出了基于误差通道并行在线建模算法的主动控制系统,该系统同时采用3个自适应滤波器,并且通过引入一个延迟单元,以保证滤波器解的唯一性。数学分析和仿真实验结果表明,该控制算法能获取误差通道的无偏估计,并可降低主动降噪系统总体代价。     

机械故障诊断中振动参量转换的波形基线修正算法

原始信号的获取及预处理的精度决定着机械状态监测与诊断系统能否正常工作。在分析了振动信号固有特性的基础上,提出了一种基于最小二乘法的积分后波形基线修正算法。方法简单实用,具有很好的精度和通用性,为各种精密分析诊断软件提供了足够精度的数据。该算法已应用于汽轮发电机组在线监测与故障诊断系统(MDS-3)中,并取得了很好的效果,为判断机组运行状态、进行故障诊断提供了准确可靠的数据据和信息。     

微弱正弦信号参数估计算法研究

该文提出了一种强噪声背景下微弱正弦信号频率、幅值与相位三个参数的估计算法.根据离散小波变换,对强噪声背景下采样序列进行预处理,构造N+2个新的序列.对该序列实施离散傅里叶变换,结合序列的构造方式得到了正弦信号频率,相位和幅值的估计算法.仿真实验表明,在不同类型的噪声背景下,该算法具有较高的估计精度与较强的鲁棒性.