螺杆泵驱动装置磨损状态监测系统

螺杆泵驱动装置受力复杂,振源众多,轴承和齿轮的磨损类故障较为频繁,而常规的单项指标测试只能进行定性分析,诊断精确度不高。为此,开发了磨损状态监测系统。该系统由传感器、诊断仪主机和便携式计算机组成。通过建立设备信息库、故障模式库实现了故障特征自动提取;通过建立磨损状态分级模型,实现了部件的故障诊断及整机磨损状态的评判。实际应用中,诊断出了电机轴承磨损和驱动装置大、小齿轮磨损的故障,表明系统具有较高的应用价值。     

故障树分析及其在制粉系统故障诊断中的应用

利用故障树分析法对火力发电厂制粉系统的常见故障进行了分析研究，建立了制粉系统的故障树，涵盖了球磨机、给煤机、排粉机、粗细粉分离器的常见故障，求出了其最小割集，给出了制粉系统发生故障的原因及导致故障各种原因组合的可能性。并且以球磨机为例给出了故障树分析的具体应用，通过球磨机振动信号的采集与分析，对球磨机的机械部件故障进行诊断。故障树分析法给制粉系统的故障智能诊断提供了一种有效的方法，为故障维修及故障预防提供了指导。     

基于振动可视化分析的机械故障诊断方法

提出了一种基于振动可视化技术的机械系统故障诊断方法。通过振动可视化技术补充和完善常规监测手段所得到的振动信息,同步反映设备关键测点在时间和频率上的变化,以此作为系统故障诊断的依据。首先,以功能简单的运动部件为对象研究可视化技术应用的可行性;其次,以多通道振动信号分析结果作为输入量,将其应用于机械系统故障诊断。应用结果表明,基于振动可视化技术的机械系统级故障诊断方法能够有效地简化复杂机械系统故障源的确定过程,同时完成故障模式的准确匹配。     

基于能量算子梯度邻域特征提取的核电应急柴油发电机组故障诊断方法

气门间隙故障作为应急柴油发电机组（EDG）的典型故障,容易导致性能下降、机械故障等,传统人工拆盖检查气门间隙的方式费时费力。利用Teager能量算子的瞬态振动识别能力,结合振动冲击能量变化规律,提出了一种基于能量算子梯度邻域的振动冲击始点自适应精确提取的EDG气门间隙故障在线诊断方法,该方法的故障识别参考阈值为EDG设计参数。在一台12缸V型柴油机上进行实验验证,结果表明该方法能够有效诊断气门故障,同时具备追踪气门间隙的能力。研究成果为EDG在线监测诊断提供了新途径。     

Diagnostics and prognostics based on adaptive time-frequency feature discrimination

This paper presents a novel diagnostic technique for monitoring the system conditions and detecting failure modes and precursors based on wavelet-packet analysis of external noise/ vibration measurements. The capability is based on extracting relevant features of noise/ vibration data that best discriminate systems with different noise/vibration signatures by analyzing external measurements of noise/vibration in the time-frequency domain. By virtue of their localized nature both in time and frequency, the identified features help to reveal faults at the level of components in a mechanical system in addition to the existence of certain faults. A prima-facie case is made via application of the proposed approach to fault detection in scroll and rotary compressors, although the methods and algorithms are very general in nature. The proposed technique has successfully identified the existence of specific faults in the scroll and rotary compressors. In addition, its capability of tracking the severity of specific faults in the rotary compressors indicates that the technique has a potential to be used as a prognostic tool.     

基于K-L散度的机械或传感器故障判别方法

机械故障诊断系统中,对同一监测部位通常采用双传感器配置（如水平和垂直方位）。文中首先运用核密度估计方法得到两传感器输出信号的概率密度函数估计,然后计算两输出信号间K-L（Kullbaek—Leiber）散度,并提出一种基于K-L散度值的机械或传感器故障判别准则。通过对一个齿轮减速箱实测振动信号和模拟的传感器故障信号的计算,可以发现,与无故障状态时K-L散度相比,监测部位出现机械故障时两传感器输出信号间K-L散度显著减小;而两传感器之一出现故障时其K-L散度显著增大。因此,两信号间K-L散度的变化可用于区别机械和传感器故障。     

基于残差分析的挠度传感器故障时间定位算法

及时诊断出桥梁健康监测系统中传感器故障及确定传感器发生故障的精确时刻,可以有效地降低系统虚假报警率、保障系统正常运行。通过分析桥梁传感器故障演变过程,建立了数据标准化残差模型,在该模型基础上提出了一种基于数据标准化残差分析的故障时间定位算法,该算法以传感器采集数据的残差偏离量化值对故障进行判定,从而对传感器故障时间进行定位。以系统中常见的挠度传感器作为研究对象,针对工程中常见的常值故障、固定偏差、精度下降、漂移故障4种故障类型进行了仿真实验。实验结果表明,该方法对挠度传感器4种故障的时间定位精度有较大幅度的提高。     

超声波远距离振动信号检测系统的设计

在机械设备状态监测与故障诊断中,振动信号是一个重要的状态参量。本文针对压电式、电涡流传感器等常规的振动测量方法和仪器在特殊环境中的局限性,提出了基于超声波的非接触式检测方法,研究并设计了系统检测装置。该检测装置主要由超声波发生模块、超声波接收模块和基于LabVIEW的数据采集/处理3个部分组成,利用该装置对振动对象进行远距离测量,并同时用传统测量仪器(电涡流位移传感器)作对照实验,得到较理想的检测结果,验证了该超声波振动检测装置的有效性和可行性。     

A machine learning approach for the condition monitoring of rotating machinery

Rotating machinery breakdowns are most commonly caused by failures in bearing subsystems. Consequently, condition monitoring of such subsystems could increase reliability of machines that are carrying out field operations. Recently, research has focused on the implementation of vibration signals analysis for health status diagnosis in bearings systems considering the use of acceleration measurements. Informative features sensitive to specific bearing faults and fault locations were constructed by using advanced signal processing techniques which enable the accurate discrimination of faults based on their location. In this paper, the architecture of a diagnostic system for extended faults in bearings based on neural networks is presented. The multilayer perceptron (MLP) with Bayesian automatic relevance determination has been applied in the classification of accelerometer data. New features like the line integral and feature based sensor fusion are introduced which enhance the fault identification performance. Vibration feature selection based on Bayesian automatic relevance determination is introduced for finding better feature combinations.     

航空发动机振动监测研究

随着航空发动机的维护方法从定期维护向视情维护过渡,军用发动机状态监控与故障诊断技术在航空发达国家已得到广泛应用,而在我国,这项技术的应用研究还处于起步阶段.振动分析是诊断发动机结构强度故障的一种有效方法,绝大多数结构强度方面的故障都与发动机振动信号有密切的关系.发动机振动监测是状态监控与故障诊断的一项重要内容.首先研究了航空发动机的典型故障,随后分析了产生这些故障的振动机理并构建了分析模型.最后确定了发动机振动监测系统的组成和主要功能.     

大型电动机的状态监测及诊断

电动机故障包括绝缘故障、定子故障、转子故障、轴承故障等。各种故障都会以一定的故障信号方式表现出来，而通过对信号中故障特征信号的提取分析可以对电动机故障进行判断。本文对电动机的多种基于信号监测的故障分析方法进行了原理分析，包括对定子电流信号的多种分析、轴承振动的频谱分析、电动机转速的波动分析等，对其他的多种故障监测方法也进行了介绍，并对每种分析方法所适用的故障诊断类型及优缺点给予了说明，最后指出了今后的发展趋势，为电动机故障诊断方法的应用提供了参考依据。     

Auditory-model-based Feature Extraction Method for Mechanical Faults Diagnosis

It is well known that the human auditory system possesses remarkable capabilities to analyze and identify signals. Therefore, it would be significant to build an auditory model based on the mechanism of human auditory systems, which may improve the effects of mechanical signal analysis and enrich the methods of mechanical faults features extraction. However the existing methods are all based on explicit senses of mathematics or physics, and have some shortages on distinguishing different faults, stability, and suppressing the disturbance noise, etc. For the purpose of improving the performances of the work of feature extraction, an auditory model, early auditory(EA) model, is introduced for the first time. This auditory model transforms time domain signal into auditory spectrum via bandpass filtering, nonlinear compressing, and lateral inhibiting by simulating the principle of the human auditory system. The EA model is developed with the Gammatone filterbank as the basilar membrane. According to the characteristics of vibration signals, a method is proposed for determining the parameter of inner hair cells model of EA model. The performance of EA model is evaluated through experiments on four rotor faults, including misalignment, rotor-to-stator rubbing, oil film whirl, and pedestal looseness. The results show that the auditory spectrum, output of EA model, can effectively distinguish different faults with satisfactory stability and has the ability to suppress the disturbance noise. Then, it is feasible to apply auditory model, as a new method, to the feature extraction for mechanical faults diagnosis with effect.     

Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement

The fault diagnosis of rolling element bearing is important for improving mechanical system reliability and performance. When localized fault occurs in a bearing, the periodic impulsive feature of the vibration signal appears in time domain, and the corresponding bearing characteristic frequencies (BCFs) emerge in frequency domain. However, in the early stage of bearing failures, the BCFs contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations, an effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized by genetic algorithm. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. In the enhanced autocorrelation envelope power spectrum, only several single spectrum lines would be left, which is very simple for operator to identify the bearing fault type. Moreover, the proposed method can be conducted in an almost automatic way. The results obtained from simulated and practical experiments prove that the proposed method is very effective for bearing faults diagnosis.     

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures.     

电动汽车的开关磁阻电动机驱动及其系统激振仿真

将新型开关磁阻电动机（switched reluctance motor,SRM）作为驱动源,应用于电动汽车。同时,根据开关磁阻电机的振动特性,以电机为激振源,导出了电机径向力和切向力随时间变化的解析表达式,用解析方法分析了它们对车辆振动系统的激振程度。仿真计算结果显示,开关磁阻电机的径向力是振源的主要激振力,是系统振动的主要因素。研究结果为电动汽车驱动系统的开发及其运行稳定性研究提供了理论依据。     

一种新的机械系统故障分类器

提出了一种利用支持矢量机对机械系统故障进行分类的新方法;以二值分类为基础,开发了基于支持矢量机的多值分类器。并以齿轮的多种故障分类为例,进行了实际应用验证。结果表明,该方法具有很好的分类能力和较高的计算效率,不需要对原始数据进行预处理就可达到满意的效果,适合于机械故障诊断中的多故障分类。该方法的应用,为故障诊断技术向智能化方向发展提供了新的途径。     

基于Matlab的柴油机故障诊断

利用小波分析、BP神经网络技术处理柴油机工作时产生的振动信号。在输入层对振动信号进行小波变换,提取其在多尺度下的特征作为故障特征向量。根据这些特征向量进行BP网络的分析,以对柴油机进行故障诊断。在实验台上模拟了多个故障,并对柴油机工作时在汽缸盖上方振动信号采集和处理,对几种故障模式进行了成功的判别。结果表明此方法是可行和有效的。     

汽轮发电机组振动故障的综合自动诊断方法研究

利用振动信号频率成分结构和频率幅值研究了汽轮发电机组振动故障的综合自动诊断方法。根据振动故障的频谱特点 ,将振动故障分为 4个模式类 ,并确定故障类模式中心 ,在故障类的层次上做模式识别 ;利用频谱征兆和模糊关系矩阵 ,计算故障隶属度值以确定故障的严重程度 ;通过实例阐明了本方法对故障模式有较强的识别能力     

振动监测技术在工艺气螺杆压缩机中的应用

介绍了工艺螺杆压缩机常见振动故障的表现及其成因,并根据振动故障信号的检测原理,提出了监测系统的搭建方法和测振仪器的选择要求。在此基础上,重点分析了不同振动故障的频谱特征,归纳出相应的诊断要点,为现场人员快速准确地解决实际振动问题提供了有效途径。     

滚动轴承的振动监测与故障诊断系统研究

论述了滚动轴承的振动监测与故障诊断的原理,方法和系统。为了简单 判别轴承有无故障,采用了振动监测系统对滚动轴承进行巡回在线监测,发现故障后发出声光警告,然后则自动转入诊断模块,用共振解调法进一步判断故障发生部位及其趋势等。