基于微分局部均值分解的旋转机械故障诊断方法

提出一种基于微分局部均值分解(Differential local mean decomposition,DLMD)的旋转机械故障诊断方法。该方法在局部均值分解(Local mean decomposition,LMD)过程中融入微分和积分运算。对原始信号进行k阶微分,然后对微分后信号进行LMD分解,对分解得到的各乘积函数(Production function,PF)分量循环进行一次积分和一阶LMD分解,直至循环k次,得到m个PF分量和残余分量,将所有PF分量的瞬时幅值和瞬时频率组合,便可以得到原始信号完整时频分布。将该方法应用于旋转机械故障诊断研究中,通过仿真和试验进行分析研究,结果表明,基于微分局部均值分解的旋转机械故障诊断方法能够有效地抑制虚假干扰频率,提高旋转机械故障诊断准确性。     

基于局域均值分解的机械故障欠定盲源分离方法研究

结合局域均值分解(Local mean decomposition,LMD)和盲源分离各自的特点,提出一种基于局域均值分解的欠定盲源分离方法.该方法利用LMD对观测信号进行分解,得到一系列的生产函数分量,将所得到的生产函数(Production functions,PF)分量和原观测信号组成新的观测信号.对构成的新观测信号进行白化处理和联合近似对角化,得到源信号的估计.该方法能有效解决传统的盲源分离方法要求源信号满足非高斯、平稳和相互独立的假设,且要求观测信号数多于源数的不足等问题.仿真结果表明,所提出的方法是有效的,在处理非平稳信号混合的欠定盲分离方面,比传统时频域的盲源分离方法得到了更好的分离效果.将提出的方法应用到滚动轴承的混合故障分离中,试验结果进一步验证该方法的有效性.     

基于AIS的核动力旋转机械故障诊断方法

人工免疫系统(AIS)具有强大的学习能力和模式识别能力,因此,将其应用到故障诊断领域中来是非常有必要的.从现有故障诊断方法中存在的问题出发,提出了一种新的基于AIS的故障诊断方法,并简要介绍了AIS与其它优化算法(如HMM)相结合的故障诊断方法.     

基于CHMM的旋转机械故障诊断技术

隐马尔可夫模型(Hidden Markov model,HMM)是一种具有较强的时间序列建模能力的信号模式处理工具, 在语音处理中获得了广泛应用,特别适合于非线性、重复再现性不佳的信号的分析。基于振动信号与语音信号的相似性,将CHMM(Continuous Hidden Markov model)引入了旋转机械的故障诊断中。采用12阶LPC倒谱系数进行特征提取,建立CHMM,为防止数据下溢,引入前向一后向比例因子算法求其对数似然概率,并且采用K-means 算法对CHMM进行参数初始化。在给定的观测序列中每一种模型的优化路径通过Viterbi算法实现,用Baum-Welch 算法实现参数重估,并给出了重估公式。最后,在转子试验台上模拟了四种故障试验,建立了四种故障的CHMM 模型,通过求其最大似然概率值来决定机器的运行状态,试验结果证明了该方法的有效性。     

一种基于粗糙集和模式识别的旋转机械故障诊断方法

提出了一种基于粗糙集理论和模式识别的旋转机械故障诊断方法。该方法包括模式学习和模式匹配 2个过程 ,模式学习用粗糙集方法学习已知故障样本中的标准模式 ,即故障诊断规则 ,模式匹配把待诊断对象和标准模式进行匹配从而进行故障诊断。文中提出的学习方法考虑了样本中的重复对象和冲突对象 ,使获得的诊断规则能够覆盖所有的已知故障样本 ;在模式匹配时 ,根据条件匹配的程度、规则的置信度和诊断结论阈值获得诊断结论和结论置信度 ,从而使得到的结论更客观。最后通过实验验证了该方法的有效性。     

基于递归神经网络的旋转机械故障诊断方法

为实现对旋转机械的在线故障诊断,对10类故障情况下的振动信号进行频谱分析。发现旋转机械振动信号的频谱中含有丰富的故障信息,以此为故障特征向量建立了诊断模型。在现有神经网络故障诊断方法基础上,提出了一种基于带有偏差单元递归神经网络的在线故障诊断方法,设计了相应的故障样本和故障编码。仿真结果表明,该方法在收敛速度、非线性能力及精度方面明显优于一般方法。对故障模式的回想结果及实际运行结果证明,本方法切实可行,适合于旋转机械的在线故障诊断。     

基于Wavelet-HMM的旋转机械故障诊断方法研究

隐Markov模型是一个双随机过程，适用于动态过程的时间序列的建模并具有强大的时序模式分类能力，特别适合非平稳、重复再现性不佳的信号分析；小波变换具有多分辨率分析的特点，在时频两域都具有表征信号局部特征的能力。文中将小波变换和隐Markov模型相结合，提出基于小波变换的HMM状态识别法，利用Daubechies小波进行8尺度的小波分解，然后从小波分解结构中提取一维信号的低频系数作为特征向量，将其输入到各个状态HMM来进行训练，其中输出概率最大的状态即是机组运行状态，从而实现状态的识别，实验结果表明该方法很有效。     

基于盲源分离与小波降噪的旋转机械故障分析

基于小波降噪和盲源分离相结合对机械信号进行分离与故障诊断。首先使用经分析选择的较好小波阈值对非平稳振动信号进行降噪,然后运用盲源分离技术分离出激振信号,结果表明利用小波阀值降噪后进行盲源分离时分离信号与源信号相似系数优于直接盲源分离;将小波降噪和盲源分离相结合应用于某燃气轮机的实测故障信号提取,诊断出转子发生了不平衡及碰摩等故障现象,与实测情况相符,有效说明了该方法在旋转机械故障诊断中的实用性。     

示波器在旋转机械故障简易诊断中的应用

介绍一种转轴振动的直接测量的方法，并利用在示波上显示的轴心轨迹图，时基图对振动的幅值、相位、频率等振动特性进行分析。还将根据轴心轨迹图的变化对机组的典型故障进行简易诊断。     

基于解析模态分解的机械故障诊断方法

针对旋转机械故障诊断问题,提出了一种基于解析模态分解(AMD)的旋转机械故障诊断方法。只要知道信号的频率成分,AMD方法就可以将含不同频率成分的信号分解为单频率信号,尤其能够分解有紧密间隔频率成分的信号。对于可预知故障特征频率的旋转机械的故障诊断,可利用AMD方法提取机械振动信号中故障特征频率所在频段的信号,并求该段信号的频谱,若频谱中含有故障特征频率,则说明机械振动信号中存在该故障。通过对滚动轴承故障信号和转子不对中故障信号的分析以及和经验模态分解(EMD)方法的对比,证明了AMD方法的有效性,且AMD方法比EMD方法更快速、准确。     

基于Elman神经网络的旋转机械故障诊断

提出了一种基于Elman神经网络的旋转机械故障诊断模型.该诊断模型综合了经验模态分解在故障特征提取和Elman网络在故障模式识别方面的优势,对故障信号进行经验模态分解,再对表征故障调制特征的本征模态函数计算瞬时幅值欧式范数构成特征矢量,将特征矢量输入到训练好的Elman神经网络中进行故障诊断.通过深沟球轴承故障诊断实例验证了所提故障诊断模型的有效性.     

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...     

旋转机械故障诊断专家系统

在旋转机械最佳诊断方法理论的指导下,依据基于黑灰白的推理机技术开发了旋转机械故障诊断专家系统。本文着重说明了故障诊断系统中推理机和知识库这两部分的设计及实现,最后根据工厂实际数据进行验证,列出了验证的结果。     

Fault diagnosis of rotating machinery based on multi-class support vector machines

Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering However, their applications in fault diagnosis of rotating machinery are rather limited Most of the published papers focus on some special fault diagnoses This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator     

Fault diagnosis of rotating machinery based on SVD, FCM and RST

Because there is no criterion available, discretization of numerous data about objects of information systems may be the biggest obstacle to performing inductive learning from instances. Based on a Singular value decomposition of matrix and Fuzzy C-means clustering, a discrete approach of continuous attribute values in a decision table, with continuous condition attribute values and discrete decision attribute values, has been proposed. Rough sets theory has been employed for diagnostic rule acquisition of rotating machinery with consideration of conflicting objects of decision table. A weak matching mode of objects with diagnostic rules has been proposed, with diagnostic conclusion and its belief degree obtained by comparing new objects with the standard diagnostic rules. An example at the end of this paper shows that the acquired rules have good merits of the ability of generalization and extension, and to some extent, improves classification level.     

Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm

A new hybrid clustering algorithm based on a three-layer feed forward neural network (FFNN), a distribution density function, and a cluster validity index, is presented in this paper. In this algorithm, both feature weighting and sample weighting are considered, and an optimal cluster number is automatically determined by the cluster validity index. Feature weights are learnt via FFNN based on the gradient descent technique, and sample weights are computed by using the distribution density function of a sample. Feature weighting and sample weighting highlight the importance of sensitive features and representative samples, and simultaneously weaken the interference of insensitive features and vague samples. The presented algorithm is described and applied to the incipient fault diagnosis of locomotive roller bearings. The diagnosis result demonstrates the superior effectiveness and practicability of the algorithm, and shows that it is a promising approach to the fault diagnosis of rotating machinery.     

Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs

This paper presents a novel method for fault diagnosis based on empirical mode decomposition (EMD), an improved distance evaluation technique and the combination of multiple adaptive neuro-fuzzy inference systems (ANFISs). The method consists of three stages. First, prior to feature extraction, some preprocessing techniques, like filtration, demodulation and EMD are performed on vibration signals to acquire more fault characteristic information. Then, six feature sets, including time- and frequency-domain statistical features of both the raw and preprocessed signals, are extracted. Second, an improved distance evaluation technique is proposed, and with it, six salient feature sets are selected from the six original feature sets, respectively. Finally, the six salient feature sets are input into the multiple ANFIS combination with genetic algorithms (GAs) to identify different abnormal cases. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the multiple ANFIS combination can reliably recognise different fault categories and severities, which has a better classification performance compared to the individual classifiers based on ANFIS. Moreover, the effectiveness of the proposed feature selection method based on the improved distance evaluation technique is also demonstrated by the testing results.     

Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble

This paper presents a novel method for fault diagnosis based on an improved wavelet package transform (IWPT), a distance evaluation technique and the support vector machines (SVMs) ensemble. The method consists of three stages. Firstly, with investigating the feature of impact fault in vibration signals, a biorthogonal wavelet with impact property is constructed via lifting scheme, and the IWPT is carried out to extract salient frequency-band features from raw vibration signals. Then, the faulty features can be detected by envelope spectrum analysis of wavelet package coefficients of the most salient frequency band. Secondly, with the distance evaluation technique, the optimal features are selected from the statistical characteristics of raw signals and wavelet package coefficients, and the energy characteristics of decomposition frequency band. Finally, the optimal features are input into the SVMs ensemble with AdaBoost algorithm to identify the different abnormal cases. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the SVMs ensemble can reliably separate different fault conditions and identify the severity of incipient faults, which has a better classification performance compared to the single SVMs.     

旋转机械基于信号的故障检测分析

旋转机械是各种类型机械设备中数量最多、应用最广泛的一类机械,介绍了基于信号处理的旋转机械故障诊断,并给出了支持向量机下模式识别与故障检测的方法,对压缩机进行了实验研究,证明了方法的有效性。该方法可以广泛应用到工程实际中,为有关人员起到一定的参考作用。     

Deep residual learning-based fault diagnosis method for rotating machinery

Effective fault diagnosis of rotating machinery has always been an important issue in real industries. In the recent years, data-driven fault diagnosis methods such as neural networks have been receiving increasing attention due to their great merits of high diagnosis accuracy and easy implementation. However, it is mostly difficult to fully train a deep neural network since gradients in optimization may vanish or explode during back-propagation, which results in deterioration and noticeable variance in model performance. In fault diagnosis researches, larger data sequence of machinery vibration signal containing sufficient information is usually preferred and consequently, deep models with large capacity are generally adopted. In order to improve network training, a residual learning algorithm is proposed in this paper. The proposed architecture significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length. Little prior expertise on fault diagnosis and signal processing is required, that facilitates industrial applications of the proposed method. Experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach.