一种新的旋转机械升降速阶段振动信号的瞬时频率估计算法

传统的短时傅里叶变换(Shorr-time Fouriertransform,STFT)峰值搜索法对高噪声、强干扰信号进行瞬时频率估计的结果往往偏离真实值误差较大,且对复杂的旋转机械设备产生的振动信号不能实现参考轴的瞬时频率估计.针对旋转机械升降速阶段振动信号的特点,提出一种新的旋转机械升降速阶段振动信号瞬时频率估计算法--STFT Viterbi拟合法(STF_TViterbi algofithm fit,STFT_VF).该方法采用STFT对振动信号进行时频分析,从而得到时间离散点和频率离散点组成的网格面,然后运用Viterbi算法实现对参考轴信号的瞬时频率估计.STFT VF方法极大地降低了噪声和干扰对瞬时频率估计结果的影响,实现了对复杂旋转机械振动信号的瞬时频率估计,且结果精度高.仿真和实际测试试验验证了本方法的正确性.     

基于CNCT的滚动轴承瞬时频率估计研究

针对滚动轴承升降速过程中非平稳信号的瞬时频率估计精度不足的问题,提出一种连续非线性调频小波变换的拟合方法来提高瞬时频率估计的精度。利用非线性调频小波变换产生良好能量聚集性的时-频分布,将仿真信号做时-频分布,然后再用峰值搜索在时频面上提取峰值,最后利用最小二乘法对瞬时频率进行拟合。MATLAB的实验结果表明,基于连续非线性调频小波变换的滚动轴承瞬时频率估计方法在低信噪比下可一定程度上提升信号瞬时频率估计的精度。     

基于LabVIEW的旋转机械计算阶次跟踪系统开发

转子系统的升降速过程包含了丰富的状态信息,一些在平稳运行时不易反映的故障征兆可能会充分地表现出来。因此,旋转机械的升降速过程中的振动信息对于旋转机械的状态监测及故障诊断具有独特的价值。转子系统的升降速过程是一种非平稳过程,其振动信号在时域和频域变化非常复杂,不满足傅里叶变换对信号的平稳性要求。基于傅里叶的传统谱分析方法无法有效地提取和分析该阶段的信息,而计算阶次跟踪技术的出现很好地解决了这一问题,基于LabVIEW开发了一套全新的旋转机械计算阶次跟踪系统。     

基于独立分量分析的消噪方法在旋转机械特征提取中的应用

为了有效提取信号特征，为后续的故障诊断分析提供足够的信息，针对旋转机械故障诊断的特点，探讨了在旋转机械振动信号采集过程中基于独立分量分析方法的降噪措施的可行性，并在此基础上，研究了基于独立分量分析的消噪方法在旋转机械升降速过程中信号特征提取中的应用。实验结果表明，利用该方法可有效消除振动信号采集过程中混入的噪声。     

小波尺度谱同步平均在弱信息识别中的应用

旋转机械的早期故障特征微弱,容易受到噪声的干扰,不容易准确识别.而旋转机械发生故障时其振动信号往往是非平稳信号,不同的非平稳性对应不同的故障状态.连续小波变换可以通过伸缩平移变换对信号进行多尺度细化分析,能够在不同的尺度上描述信号的局部特征,因此有利于故障信号的检测.时域同步平均可以削弱观测信号中的随机成分,降低噪声干扰,提取与平均周期相关的确定性信号,提高信噪比.结合小波变换和同步平均的优点,提出小波尺度谱同步平均的方法.对多周期的振动信号进行小波连续变换,并进行尺度谱重排,获得重排小波尺度谱;根据信号的周期性,对尺度谱进行同步平均,同步平均后的尺度谱可以有效地抑制干扰噪声,识别弱故障信息.通过仿真分析和实例分析验证了本方法的有效性,为旋转机械的早期故障诊断提供了新方法.     

阶次包络谱在轴承故障诊断中的应用

旋转机械的升降速过程包含丰富的状态信息,因而旋转机械的升降速过程对于旋转机械的故障诊断具有独特的价值.将常规的阶次分析技术与包络谱相结合,提出基于阶次包络谱的齿轮箱故障诊断方法.首先对齿轮箱升降速瞬态信号进行时域采样,再对时域信号实行等角度重采样,转化为角域平稳信号,最后对角域重采样信号进行包络谱分析,就可提取轴承的故障特征.通过对轴承内圈、外圈故障实验信号的分析,表明该方法能有效诊断轴承的故障.     

改进的HHT方法在旋转机械不对中故障特征提取中的应用

HHT(希尔伯特-黄变换)能够将振动信号分解为有限的具有实际物理意义的模态分量,并由此可对机械故障信号进行特征提取,但噪声的干扰对分解过程和分解结果影响却很大。针对这一不足,本文提出了先利用小波变换技术对含噪故障信号进行消噪处理,再作HHT分析的方法;利用此方法对实测的不对中振动信号进行了故障特征提取和分析。结果表明,该方法克服了直接运用HHT分解方法由噪声带来的不必要的干扰,提高了参数提取的准确性,并由此提高了机械故障诊断率。     

阶比双谱及其在旋转机械故障诊断中的应用

双谱分析是处理非线性、非高斯信号的有力工具,然而,它是以分析恒频振动的稳态信号作为前提条件的,对分析旋转机械中广泛存在的变频振动信号（如旋转机械升降速信号）是无能为力的。而阶比双谱是一种分析变频振动信号的新方法,它将非稳态信号按等转角间隔进行采样,得到阶域中的稳定信号,再进行双谱分析;仿真显示该方法优于阶比谱和传统双谱。最后,将该方法成功地应用到旋转机械升降速过程的故障诊断中,实验结果表明该方法是有效的,阶比双谱可很好地分析机械振动的非线性非平稳信号。     

基于非线性时序模型盲辨识的因子隐Markov模型识别方法

基于模型辨识的机械有效故障特征提取方法中输入信号难以确定,以及机械设备运行过程中具有信息量大、非平稳、特征重复再现性差的特点,结合非线性时序模型盲辨识和因子隐Markov模型,提出一种基于非线性时序模型盲辨识的特征提取的因子隐Markov模型识别方法,并应用到旋转机械升降速过程故障诊断中.同时还与基于Fourier变换、小波变换的特征提取的因子隐Markov模型识别方法进行比较,试验结果表明该方法是有效的.     

基于VMD-SVD和SVM的旋转机械故障诊断研究

旋转机械的振动信号具有非线性、非平稳特点,同时其早期的微弱故障信号易受噪声的干扰,因此在故障诊断中难以提取其故障特征,识别其故障类型,针对这一问题,提出了一种基于变分模态分解(VMD)-奇异值分解(SVD)和支持向量机(SVM)的旋转机械故障诊断方法.首先,对原始振动信号进行了VMD分解,并得到了其若干个分量信号;然后...     

Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform

The vibration signal of the run-up or run-down process is more complex than that of the stationary process. A novel approach to fault diagnosis of roller bearing under run-up condition based on order tracking and Teager-Huang transform (THT) is presented. This method is based on order tracking, empirical mode decomposition (EMD) and Teager Kaiser energy operator (TKEO) technique. The nonstationary vibration signals are transformed from the time domain transient signal to angle domain stationary one using order tracking. EMD can adaptively decompose the vibration signal into a series of zero mean amplitude modulation-frequency modulation (AM-FM) intrinsic mode functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. Experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager-Huang transform approach is better to that of the Hilbert-Huang transform approach for bearing fault detection and diagnosis. The Teager-Huang transform has better resolution than that of Hilbert-Huang transform. Teager-Huang transform can effectively diagnose the faults of the bearing, thus providing a viable processing tool for gearbox defect monitoring.     

升降速过程中转子系统油膜失稳规律

以某悬臂转子-轴承系统为研究对象,基于有限元方法建立了转子-轴承系统动力学模型。通过时域图、三维谱图和幅频响应图得到了转子系统升降速过程中出现的油膜失稳特征,分析了不同角加速度值在升降速过程中对1阶和2阶油膜失稳规律的影响。研究发现:升降速过程中产生的切向惯性力会改变油膜失稳转速,与稳态情况(角加速度为零)相比,升降速情况下2阶油膜失稳转速有所延迟;升速过程中随着角加速度值的增大2阶油膜失稳转速略有增加、降速过程略有减小;降速过程与升速过程相比,出现明显的迟滞效应。     

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.     

Electric load management in spindle run-up and run-down for multi-spindle machine tools via optimal power-torque trajectories and peak load synchronization

Electrical energy consumed during machine tool spindle run-up and run-down has a non-negligible impact on overall spindle energy performance. A model-based efficiency analysis of a common spindle unit architecture, with a separate induction motor, is conducted to investigate different loss sources. The identified model is exploited to define optimized motor torque trajectories for spindle run-up/run-down, exploiting the idle time typically available on the fastest machining modules in a transfer machine tool. Study reveals that savings can be achieved, both in terms of peak power and energy losses, by adopting optimal values for the acceleration rate and power limit. To reduce power peaks at the machine level, a unit synchronization algorithm is proposed which avoids simultaneous acceleration/deceleration of multiple spindles. The overall cycle time is preserved by prioritizing the execution of the slowest processing unit. The proposed methodologies have been applied to a three spindles flexible transfer machine tool, estimating the corresponding gains in terms of energy losses and power peak reduction.     

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     

Eulerian laser Doppler vibrometry: Online blade damage identification on a multi-blade test rotor

Laser Doppler vibrometry enables the telemetry-free measurement of online turbomachinery blade vibration. Specifically, the Eulerian or fixed reference frame implementation of laser vibrometry provides a practical solution to the condition monitoring of rotating blades. The short data samples that are characteristic of this measurement approach do however negate the use of traditional frequency domain signal processing techniques. It is therefore necessary to employ techniques such as time domain analysis and non-harmonic Fourier analysis to obtain useful information from the blade vibration signatures. The latter analysis technique allows the calculation of phase angle trends which can be used as indicators of blade health deterioration, as has been shown in previous work for a single-blade rotor.This article presents the results from tests conducted on a five-blade axial-flow test rotor at different rotor speeds and measurement positions. With the aid of artificial neural networks, it is demonstrated that the parameters obtained from non-harmonic Fourier analysis and time domain signal processing on Eulerian laser Doppler vibrometry signals can successfully be used to identify and quantify blade damage from among healthy blades. It is also shown that the natural frequencies of individual blades can be approximated from the Eulerian signatures recorded during rotor run-up and run-down.     

Detection of a static eccentricity fault in a closed loop driven induction motor by using the angular domain order tracking analysis method

In this study, a new method was presented for the detection of a static eccentricity fault in a closed loop operating induction motor driven by inverter. Contrary to the motors supplied by the line, if the speed and load, and therefore the amplitude and frequency, of the current constantly change then this also causes a continuous change in the location of fault harmonics in the frequency spectrum. Angular Domain Order Tracking analysis (AD-OT) is one of the most frequently used fault diagnosis methods in the monitoring of rotating machines and the analysis of dynamic vibration signals. In the presented experimental study, motor phase current and rotor speed were monitored at various speeds and load levels with a healthy and static eccentricity fault in the closed loop driven induction motor with vector control. The AD-OT method was applied to the motor current and the results were compared with the traditional FFT and Fourier Transform based Order Tracking (FT-OT) methods. The experimental results demonstrate that AD-OT method is more efficient than the FFT and FT-OT methods for fault diagnosis, especially while the motor is operating run-up and run-down. Also the AD-OT does not incur any additional cost for the user because in inverter driven systems, current and speed sensor coexist in the system. The main innovative parts of this study are that AD-OT method was implemented on the motor current signal for the first time.     

THE IDENTIFICATION OF THE UNBALANCE AND THE FOUNDATION MODEL OF A FLEXIBLE ROTATING MACHINE FROM A SINGLE RUN-DOWN

The reliable estimation of a flexible foundation model and the state of unbalance (both amplitude and phase) of a turbogenerator from machine run-down measured vibration data is an active research area. Earlier studies on the estimation of both these quantities used the whole frequency range of the run-down as a single band. However, such an identification may be inaccurate for large flexible foundations having many modes in the run-down frequency range. For reliable identification, the whole frequency range has to be divided into a number of frequency bands and the frequency-dependent foundation models have to be estimated together with the unbalance. This paper combines the unbalance estimation with the split frequency range for the foundation model, and highlights the limitations observed during the estimation of foundation models and the state of unbalance. A simulated example is used to validate and assess the advantages of the proposed method. This method is compared to the previous method using the whole frequency range as a single band. Having established the method in simulation, experimental data from a 3m long test rig, with four journal bearings, is used to test the method. The approach gives reliable estimates of the machine unbalance, even in the presence of modelling errors and measurement noise.