基于OSF的SET信号重构方法及故障诊断应用

同步提取变换(synchroextracting transform, 简称SET)通过提取短时傅里叶变换(short-time Fourier transform, 简称STFT)在瞬时频率位置的时频系数可获得较理想的时频谱，该方法提高了时频分辨率，减少了交叉项的影响，一定程度上抑制了噪声对STFT时频谱的干扰。针对在SET时频谱的基础上进行信号分量的重构与故障诊断拓展方面的应用，提出了一种基于顺序统计滤波器(order statistics filter, 简称OSF)的SET信号分量重构方法。首先，利用边际谱表征SET时频谱中信号的幅值在整个频率范围内随频率变化的情况；其次，采用顺序统计滤波器分割边际谱，将分割所得边界映射至SET时频谱后，利用SET逆变换重构信号分量；最后，利用峭度指标筛选包含丰富故障信息的分量并进行包络分析，提取故障特征。仿真信号及滚动轴承内圈故障信号的处理结果证明了该方法的有效性。     

基于听觉分流机制的瞬态信号提取方法

以听觉分流机制为基础,提出一种瞬态信号自动提取方法。首先,对信号进行带通滤波和相位调整;其次,获得各滤波信号二次包络的极大、极小值及其对应时间,基于两种极值幅值和时间,计算得到同步性和瞬态性线索;最后,综合这两类线索信息,在时频平面中筛选出与瞬态成分相对应的时频段,并最终完成瞬态成分的波形生成与修整。通过数值仿真和实测信号检验,所提方法能够在较强的背景信号下有效提取出瞬态信号,对瞬态信号的初始时间具有较高的识别精度,具有一定的实际应用潜力。     

IDENTIFICATION OF TIME-VARYING MODAL PARAMETERS USING LINEAR TIME-FREQUENCY REPRESENTATION

A new method of parameter identification based on linear time-frequency representation andHilbert transform is proposed to identify modal parameters of linear time-varying systems frommeasured vibration responses. Using Gabor expansion and synthesis theory measured responses arerepresented in the time-frequency domain and modal components are reconstructed by time-frequencyfiltering. The Hilbert transform is applied to obtain time histories of the amplitude and phase angle ofeach modal component, from which time-varying frequencies and damping ratios are identified. The     

无转速计旋转机械升降速振动信号零相位阶比跟踪滤波

提出了一种用零相位滤波法和瞬时频率估计技术实现旋转机械升降速过程振动信号的阶比跟踪滤波的方法,和传统的以硬件实现的阶比跟踪滤波方法相比,该方法具有无需转速计和跟踪滤波器等硬件、用纯软件的方法实现和对原始采样信号不产生相位移动等优点。讨论了该方法的基本原理和算法,并在算法实现上提出了数据分段重叠滤波技术,可有效抑制一般分段数字滤波会产生的边缘效应。测试试验验证了该方法的正确性和有效性。     

跟踪滤波器的设计及其应用

在实际测量中,一般被测信号频率变化范围较大,采用常用的带通滤波器处理信号难以胜任,为了有效地检测信号,应采用一种能跟踪信号频率的窄带通滤波技术,为此本文首先阐述了开关电容滤波器的基本原理,提出了使用开关电容波滤器组成跟踪滤波器的原理与实现方法,作为应用例,具体设计了一个能压缩带宽的窄带通跟踪波滤器,实际应用于新型仪器非接触式车速仪中,有效地解决了车速变化快及变化范围大的车速信号检测问题。     

基于L-T的联合算法在光纤法珀纳米压力传感器脉压检测信号中的应用

研究了一种改进的去噪方法及其在脉冲拍频信号去噪中的应用。该算法结合了局部均值分解(Local mean decomposition,LMD)和时频峰值滤波(Time-frequency peak filtering,TFPF)的优点,称为L-T算法。TFPF作为一种经典的时频滤波方法,较长的窗长可以在保留信号幅值的前提下有效抑制随机噪声,而较短的窗长则导致信号幅值严重衰减。因此,为了保持有效信号幅度、抑制随机噪声,对LMD和TFPF进行了改进。首先利用LMD将原始信号分解为无级生存(Progression-free survival,PFS),然后计算各乘积函数均值的标准误差,将许多PFSs分为有用分量、混合分量和噪声分量。其次,将短窗TFPF用于有用分量去噪,长窗TFPF用于混合分量去噪,得到重构后的信号。最后,将该算法用于F-P压力传感器的降噪。实验结果表明,与传统小波去噪算法相比,L-T算法去噪效果更优。     

正弦加载液压泵故障振动信号的阶比多尺度形态滤波方法研究

 液压泵在变载荷作用下会引起转速波动且发生幅值调制现象,振动信号呈现出明显的非平稳性,传统的滤波方法难于进行有效滤波。针对正弦加载液压泵故障振动信号提出一种阶比多尺度形态滤波方法,利用基于EEMD理论的时频阶比分析方法将正弦加载液压泵故障振动非平稳信号转化成角域平稳信号,再用多尺度形态滤波方法对角域平稳信号进行多尺度寻优和解调处理获得多尺度解调信号,并进行阶次谱分析得到振动信号的特征阶次。通过实验证实,该方法能对正弦加载液压泵故障振动信号进行有效滤波,且能提取出更多的有用故障信息。     

旋转机械虚拟式振动信号特征分析仪

介绍了一种用于分析旋转机械振动信号的仪器, 该仪器可以有效提取出振动信号的特征信息.该仪器具有先进的时频分析功能,另外,运用时频分析技术中的瞬时频率理论,提出并实现了基于峰值搜索的瞬时频率估计的阶比谱、阶比谱阵、阶比跟踪、阶比跟踪滤波、阶比分量提取等阶比分析功能.该仪器弥补了传统旋转机械分析仪器在旋转机械升/降速信号分析上的不足,弥补了现有仪器在旋转机械非平稳信号分析上对硬件需求的缺陷.实验结果表明该仪器具有很好的工程应用价值.     

Demodulation Based on Harmonic Wavelet and Its Application into Rotary Machinery Fault Diagnosis

The harmonic wavelet transform(HWT) and its fast realization based on fast Fourier transform(FFT) are introduced. Its ability to maintain the same amplitude-frequency feature is revealed. A new method to construct the time-frequency(TF) spectrum of HWT is proposed, which makes the HWT TF spectrum able to correctly reflect the time-frequency-amplitude distribution of the signal. A new way to calculate the HWT coefficients is proposed. By zero padding the data taken out, the non-decimated coefficients of HWT are obtained. Theoretical analysis shows that the modulus of the coefficients obtained by the new calculation way and living at a certain scale are the envelope of the component in the corresponding frequency band. By taking the cross section of the new TF spectrum, the demodulation for the component at a certain frequency band can be realized. A comparison with the Hilbert demodulation combined with band-pass filtering is done, which indicates for multi-components, the method proposed here is more suitable since it realizes ideal band-pass filtering and avoids pass band selecting. In the end, it is applied to bearing and gearbox fault diagnosis, and the results reflect that it can effectively extract the fault features in the signal.     

传感器阵列预测空域多信号分类目标定位跟踪

目标定位跟踪技术广泛应用于军事民用领域,是当前研究的热点与难点.提出了一种空域多信号分类-自回归粒子滤波(multiple signal classification autoregressive particle filter,MUSIC-ARPF)方法,定位跟踪地面目标.该方法使用多信号分类(multiple signal classification,MUSIC)算法估计目标波达方向(direction of arrival,DOA)并计算目标信号源位置,利用自回归(autoregressive model,AR)模型和粒子滤波(particle filter,PF)算法预测信号源下一时刻位置,进而自适应选择通带与阻带扇面进行空域滤波,同时调整MUSIC算法中谱峰搜索区域,提高DOA估计的分辨率,减少目标定位的扫描域.实验结果表明,空域MUSIC-ARPF方法能够减少目标定位时间,提高目标跟踪精度.     

A fault characteristics extraction method for rolling bearing with variable rotational speed using adaptive time-varying comb filtering and order tracking

The fault characteristics of rolling bearing with variable rotational speed are usually related to shafting speed and vary with time. Moreover, the rolling bearing fault characteristics are easily submerged by the noises. To address these issues, an adaptive time-varying comb filtering (ATVCF) method that combines the merits of comb filter and adaptive time-varying filtering (ATVF) is proposed and applied to extract the fault-related component from the envelope signal of rolling bearing. And on this basis, via the joint application of ATVCF and order tracking (OT), a fault characteristics extraction methodology for rolling bearing with variable rotational speed, namely ATVCF-OT, is developed. In the ATVCF-OT, the ATVCF method can adaptively extract time-varying harmonic components containing rolling bearing fault information from the rolling bearing fault vibration signal, and the OT analysis can effectively stabilize the time-varying rolling bearing fault features. Therefore, the ATVCF-OT methodology is particularly suitable for fault feature extraction of rolling bearing with variable rotational speed. Simulation and experimental results indicate that the ATVCF-OT method can effectively remove the fault-unrelated components and highlight the fault features of rolling bearing. The comparisons with the direct envelope order method and the ensemble empirical mode decomposition (EEMD)-based envelope order method demonstrate the advantages of the proposed ATVCF-OT method.

     

DEVELOPMENT OF VIRTUAL INSTRUMENT IN CHARACTERISTIC ANALYSIS OF ROTATING MACHINERY BASED ON INSTANTANEOUS FREQUENCY ESTIMATION

Based on the recently quick-developing time-frequency analysis (TFA) technique and virtual instrument (VI) technique, a virtual instrument in characteristic analysis of rotating machinery is researched and developed successfully. By utilizing instantaneous frequency estimation (IFE) theoretics of TFA technique, and based on IFE of peak searching on the time-frequency spectrum, order analysis (OA) functions is put forward and implemented, such as order spectrum, order spectrum matrix, order tracking, order tracking filtering, and order component extraction, etc. Unlike the home and abroad existing popular characteristic analyzers, which need key phasing devices such as shaft encoder, phase-locked loop (PLL), phase-locked multiple frequency, tachometer, etc, to implement constant angle sampling directly or indirectly, whereas this instrument only uses the vibration signal of rotating machinery to carry out OA. This instrument makes up the shortage of these traditional instruments in analyzing the non-stati     

回转式MRF阻尼器性能测试实验系统

针对回转式磁流变液（MRF）阻尼器研究的需要,以通用工控机为硬件平台,以LabVIEW为开发工具,研究开发了阻尼器性能测试实验系统。该系统综合应用计算机、虚拟仪器、传感检测、变频调速、自动控制等技术,实现了回转式MRF阻尼器测试实验过程的自动控制,包含电机转速自动调节,励磁电流自动扫描,阻尼器转速、转矩、温度信号的自动采集、显示、存储、分析等功能,具有人机界面友好、系统维护与功能扩充容易等特点。实际应用结果表明,该测试实验系统可以显著缩短测试实验时间,提高了测试实验精度和效率,为回转式MRF阻尼器的研究提供了有效的手段。     

Optimal estimation of the relevant information coming from a variable reluctance proximity sensor placed in a car undergoing performance tests

Today's automotive industry has a soaring interest in efficient, reliable and robust sensors able to make intelligent driving decisions that can save millions of lives every year. To that end, the sensors used in today's cars are being provided with microprocessors and application-specific integrated circuit technologies that incorporate a certain amount of intelligence into the sensors themselves. In this paper, an inverse square-root adaptive filtering algorithm for recursive least-squares estimation (QR-RLS) is used to improve the response of a wheel speed sensor placed in a car undergoing performance tests. Such an algorithm is used to carry out an optimal estimation of the relevant signal coming from the sensor, which is buried in a broad-band noise background where we have little knowledge of the noise characteristics. The results of the experiment are satisfactory, a significant improvement of 32.5 dB in the signal-to-noise ratio at the QR-RLS adaptive filter output was achieved. Also, in order to compare classical filtering techniques with optimal adaptive filtering techniques, the signal coming from the wheel speed sensor was also filtered by using a second-order lowpass digital Butterworth filter. The results of comparing the aforementioned filters show that the optimal adaptive filter is superior to the classical filter.     

Improvement on Gabor order tracking and objective comparison with Vold–Kalman filtering order tracking

The waveform-reconstruction schemes of order tracking (OT) such as the Gabor and the Vold–Kalman filtering (VKF) techniques can extract specific order and/or spectral components in addition to characterizing the processed signal in rpm-frequency domain. The study first improves the Gabor OT (GOT) technique to handle the order-crossing problem, and then objectively compares the features of the improved GOT scheme and the angular-displacement VKF OT technique. It is numerically observed the improved method performs less accurately than the VKF_OT scheme at the crossing occurrences, but without end effect in the reconstructed waveform. As OT is not exact science, it may well be that the decrease in computation time can justify the reduced accuracy. The characterisation and discrimination of riding noise with crossing orders emitted by an electrical scooter are conducted as an example of the application.     

基于小波脊提取的扭转振动测试方法研究

针对工程中转子的扭转振动提取对硬件要求严格的情况,提出了一种基于小波脊提取的扭转振动测试方法。首先,通过小波变换获得齿信号的时频信息;其次,对时频信息进行脊提取得到齿信号的瞬时频率,获得转子的瞬时转速;然后,对瞬时转速进行参数估计获得最终的扭转振动参数;最后,通过仿真与试验分别对所提出的方法进行了验证。结果表明,与传统的扭转振动测试方法相比,所提出的方法不但具有较高的精度,且对硬件的要求相对较低,所需采样频率和储存空间远远小于传统的测齿法。     

基于角域AR模型滤波的滚动轴承故障诊断

在齿轮噪源存在的变转速滚动轴承故障诊断过程中,因混合信号中转频分量相对较小,使得基于时频表达的阶比跟踪技术受到限制。虽然基于故障特征频率的角域重采样能提取轴承的故障特征,但这种算法不能确定故障位置,而且可能会出现误判。针对这一问题,提出了基于角域自回归(auto regressive,简称AR)模型滤波的处理方法。该方法利用线调频小波路径追踪算法从降采样处理的混合信号中提取齿轮瞬时啮合频率趋势线并估计转速,根据估计转速信息对原混合信号进行等角度重采样,获得了角域信号。利用角域信号中齿轮啮合振动成分具有周期性的特点,使用AR模型对其滤波,并且对滤波后信号进行包络阶比分析,完成故障判断。通过处理仿真信号和实验信号,验证了该方法不仅能有效地去除齿轮噪声,并且可以判断轴承故障位置。     

Gabor 滤波器在指纹图像处理中的应用

在实时指纹识别系统中，图像增强的效果好坏将影响特征提取及指纹鉴别的识别率。现把Gabor滤波器应用在指纹图像增强处理中，利用了Gabor滤波器的方向选择和频率选择特性，把指纹图像的局部方向和局部频率作为Gabor滤波函数的参数，然后把Gabor函数与纹理图像进行卷积，从而去除图像噪声，达到了增强图像的目的。在算法设计上采用了查表的方法，提高了算法的速度，节省了运算的时耗。实验证明，算法工作稳定，效果好，鲁棒性强。     

Automated Surface Inspection Using Gabor Filters

In this paper we present a machine vision system for the automatic inspection of defects in textured surfaces found in industry. The defects to be inspected are those that appear as local anomalies embedded in a homogeneous texture. The proposed method is based on a Gabor filtering scheme that computes the output response of energy from the convolution of a textured image with a specific Gabor filter. The best parameters of a Gabor filter are selected so that the energy of the homogeneous texture is zero, and any unpredictable defeats will generate significantly large energy values. A simple thresholding scheme then follows to discriminate between homogeneous regions and defective regions in the filtered image. This transforms texture differences into detectable filter output. The experiments on structural textures such as leather and sandpaper have shown the effectiveness of the proposed method.     

改进小波去噪Teager算子的齿轮微弱故障提取方法

针对齿轮箱在强噪声背景下齿轮微弱故障振动信号的特征不易被提取的问题,提出将改进小波去噪和Teager能量算子相结合的微弱故障特征提取方法。采用改进小波阈值函数对振动信号进行去噪处理,与形态学滤波和传统小波阈值函数相比能够有效地提高信号的信噪比。对去噪后的信号进行集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)得到若干本征模式函数(intrinsic mode function,简称IMF),计算各IMF分量与原信号的相关系数并结合各IMF分量的频谱剔除虚假分量。对有效的IMF分量计算其Teager能量算子,并重构得到Teager能量谱,对重构信号进行时频分析并将其结果与原信号的希尔伯特黄变换(HilbertHuang transform,简称HHT)得到的边际谱进行对比。实验研究结果表明,本研究方法相比HHT能够对齿轮微弱故障特征进行更为有效地提取,验证了本研究方法在齿轮箱微弱故障诊断中的可行性。