基于粗糙集理论的系统建模与故障诊断

应用粗糙集理论,可以从原始的数据中提取有用的知识或规则.依据这一思想,本文建立了一个实际非线性系统的粗糙集模型.在建模过程中,首先用系统输入输出的采样数据构成原始信息表,然后离散化,再利用粗糙集算法得到系统粗糙集模型的不完备规则集,通过实验和线性插补法实现规则集完备化,最后完成模型的设计和校验.校验结果表明所建的粗糙集模型是有效的,并利用该模型实现了系统的一种故障诊断.     

基于包络谱分析的滚动轴承故障诊断分析

介绍了包络谱分析方法的基本原理,它是一种基于滤波检波的振动信号处理方法,也是诊断设备零件损伤故障的一种有效的手段,尤其对初期故障和信噪比比较第的故障信号,识别能力很强。重点分析了包络谱分析方法在轴承故障诊断中的应用。通过对滚动轴承故障诊断的实例分析,验证了包络谱分析运用于诊断设备零件损伤故障所取得的效果。     

基于粗糙集和ANN的模拟电路故障诊断研究

针对传统故障字典法需要精确先验知识且不适合大规模故障诊断的缺点,提出了一种基于粗糙集和BP神经网络的模拟电路故障诊断新方法。首先进行样本数据采集和预处理,并使用定义的粗糙集差别矩阵算法矩阵法对数据进行属性简约,删除冗余信息,得到简约后的特征向量。然后,将简约后的特征向量作为BP神经网络的输入进行训练,最后,将训练好的BP神经网络模型用于故障诊断。仿真实验表明,文中的基于粗糙集属性简约和BP神经网络训练的故障诊断模型,具有较小的训练误差和较高的诊断精度。     

基于Teager-Huang变换的滚动轴承故障诊断方法

针对滚动轴承故障振动信号的非平稳特征,介绍了一种基于Teager-Huang时频谱和边际谱的滚动轴承故障诊断方法。详细阐述了Teager-Huang时频谱和边际谱的计算方法及物理意义。给出了该故障诊断方法的步骤,并对仿真和实际轴承的滚动体故障、内圈故障和外圈故障信号进行了分析和故障诊断。结果表明,基于Teager-Huang变换的故障诊断方法具有计算速度快,估计准确稳定的特点,是准确判断滚动轴承故障状态的一种有效新方法。     

基于GRU与迁移学习的滚动轴承故障诊断

现阶段基于深度学习的故障诊断需要大量的数据,而制作数据集是一项耗时耗力的工作。针对这一缺点,提出一种基于门控循环单元(Gate Recurrent Unit,GRU)与迁移学习的滚动轴承故障诊断方法。该方法利用与目标域特征相似且易获得源域数据的特点训练网络,确定网络结构和参数,冻结经过训练的卷积神经网络(Convolutional Neural Networks,CNN)和GRU,用小样本目标域数据训练该网络,微调全连接层和分类层,达到迁移的目的。实验对比分析表明,基于GRU与迁移学习的滚动轴承故障诊断方法明显优于基于BP神经网络和基于概率神经网络(Probabilistic Neural Network,PNN)方法的故障诊断,能够更加准确地进行故障分类,为小样本数据集下的故障诊断提出了新思路。     

Rolling bearing composite fault diagnosis method based on EEMD fusion feature

Aiming at the problem that the composite fault vibration signal of rolling bearing is complex and it is difficult to effectively extract the impact characteristics of the composite fault, a composite fault diagnosis method of rolling bearing based on multi-scale fuzzy entropy feature fusion is proposed. Compared with traditional fault feature extraction methods that can only extract single fault feature information, this method can increase the discrimination of composite fault features, effectively separate multiple composite fault features, and more comprehensively characterize composite fault feature information. First, the signal is processed by EEMD, getting a series of IMF components. Secondly, the energy and kurtosis index of the IMF component are calculated, the appropriate IMF component is selected through the correlation coefficient to obtain a new time series, the multi-scale fuzzy entropy is calculated, and feature fusion performed. Finally, the least square support vector machine is used to diagnose the fault of the fusion feature. The method is verified by a mechanical failure simulation test bench. The experimental results show that this method can quantitatively characterize the data information of fault signal, improve the anti-interference ability, have good feature extraction ability of composite fault of rolling bearings, and can effectively identify the type of composite fault. Compared with the method using multi-scale fuzzy entropy, energy and kurtosis index alone, the accuracy of fault diagnosis increases by 8.12 % and 11.65 %, respectively.

     

Rolling bearing fault convolutional neural network diagnosis method based on casing signal

Affected by the transmission path, it is very difficult to diagnose the vibration signal of the rolling bearing on the aircraft engine casing. A fault diag     

基于小波包与粗集的往复压缩机故障诊断方法

提出了基于小波包与粗集的往复压缩机故障诊断的新方法。该方法使故障特征提取及规则的提取都由计算机自动完成，经诊断实例表明，使用该方法可对往复压缩机进行有效的故障诊断。     

Intelligent fault diagnosis of rolling bearing based on kernel neighborhood rough sets and statistical features

Intelligent fault diagnosis benefits from efficient feature selection. Neighborhood rough sets are effective in feature selection. However, determining the neighborhood value accurately remains a challenge. The wrapper feature selection algorithm is designed by combining the kernel method and neighborhood rough sets to self-adaptively select sensitive features. The combination effectively solves the shortcomings in selecting the neighborhood value in the previous application process. The statistical features of time and frequency domains are used to describe the characteristic of the rolling bearing to make the intelligent fault diagnosis approach work. Three classification algorithms, namely, classification and regression tree (CART), commercial version 4.5 (C4.5), and radial basis function support vector machines (RBFSVM), are used to test UCI datasets and 10 fault datasets of rolling bearing. The results indicate that the diagnostic approach presented could effectively select the sensitive fault features and simultaneously identify the type and degree of the fault.     

基于角域级联最大相关峭度反褶积的滚动轴承早期故障诊断

变转速工况是某些启制动工作制设备常用的工作方式,针对启制动工作制下滚动轴承故障的振动信号呈现非平稳特性,加之现场环境噪声的干扰,难以从原始故障信号中提取特征频率。提出基于角域级联最大相关峭度(CMCKD)的滚动轴承故障诊断方法。首先将时域非平稳故障信号进行角域重采样转换为角域内的平稳信号;然后用级联最大相关峭度反褶积对故障信号进行处理,抑制信号中的噪声,提取信号中的周期冲击成分。通过对仿真和实验数据的分析,验证了角域级联最大相关峭度反褶积方法的有效性。     

Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram

The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is very important in the field of predictive maintenance. Till date, the resonant demodulation technique (envelope analysis) has been widely exploited in practice. However, much practical diagnostic equipment for carrying out the analysis gives little flexibility to change the analysis parameters for different working conditions, such as variation in rotating speed and different fault types. Because the signals from a flawed bearing have features of non-stationarity, wide frequency range and weak strength, it can be very difficult to choose the best analysis parameters for diagnosis. However, the kurtosis of the vibration signals of a bearing is different from normal to bad condition, and is robust in varying conditions. The fast kurtogram gives rough analysis parameters very efficiently, but filter centre frequency and bandwidth cannot be chosen entirely independently. Genetic algorithms have a strong ability for optimization, but are slow unless initial parameters are close to optimal. Therefore, the authors present a model and algorithm to design the parameters for optimal resonance demodulation using the combination of fast kurtogram for initial estimates, and a genetic algorithm for final optimization. The feasibility and the effectiveness of the proposed method are demonstrated by experiment and give better results than the classical method of arbitrarily choosing a resonance to demodulate. The method gives more flexibility in choosing optimal parameters than the fast kurtogram alone.     

非理想数据下基于仿真数据辅助迁移学习的滚动轴承故障诊断

旋转机械在实际工程应用中常处于正常状态,因而呈现故障样本稀少甚至部分缺失等非理想数据情况。针对直接采用非理想数据建立深度学习诊断模型时的低准确率问题,提出基于有限元仿真数据辅助迁移学习的故障诊断方法。首先,通过数值仿真计算不同运行工况和故障类型的轴承信号;进而,利用大量低成本高保真的仿真样本对模型预训练,利用真实小样本或者仿真样本增补后的混合样本进行模型微调,以完成高准确率故障诊断,并降低迁移学习对故障轴承实测数据的依赖;最后,利用两个轴承实验台数据进行验证。结果表明在单类故障样本数为1时,采用所提方法建立的模型准确率超过95%;在故障样本稀少且多类缺失时,准确率比仿真数据直接增补方式提升超10%。     

粗糙集合理论在旋转机械故障诊断技术中的应用

粗糙集合理论基于严格的集合分析方法,通过对数据集合进行等价关系、近似空间、分类等运算,发现隐含在数据中的规律与性质,从而完成知识发现。在人工智能机器故障诊断系统中,如何获得故障诊断等知识成为关键技术。论文提出将粗糙集合理论应用于加工中心故障诊断技术,使得故障诊断技术中的知识获取瓶颈问题得以有效解决。     

基于粗糙集的石油开采故障诊断方法

故障诊断是保证石油开采正常运作的关键.利用计算机准确的监控与故障诊断,能够让生产管理者可以及时发现油井或泵的故障,并且立即采取正确的措施.本文以有杆泵抽油过程为例,给出了基于粗糙集理论的抽油故障诊断方案,将反映系统状态的数据建构决策表,对五种典型的故障类别进行识别,结果显示了方法的有效性.     

基于双维度EKNN的滚动轴承早期故障分类算法

为提高对滚动轴承早期故障识别的精确度,提出一种基于混合特征提取的故障分类模型.该模型利用类内紧致性和类间重叠性识别出次优特征组,作为增强K近邻分类器(EK N N)的输入,并以距离和密度双维度计算,得到最大平均分类精确度,进而输出最优早期故障特征组,对未知数据进行分类来检测故障.实验采集滚动轴承在低速运行下的早期故障声...     

基于粗糙集和遗传算法的变压器故障诊断方法

本文针对基于变压器油中溶解气体的故障诊断方法不能有效地把溶解气体的绝对值和比值进行有机结合、断点划分过于绝对且缺少针对性、应用粗糙集进行约简时采用的方法不适合进行大规模数据的全局最优解求取等问题,提出了一种粗糙集和遗传算法相结合的约简算法,将溶解气体的绝对值和比值作为原始规则进行有针对件的处理,从而获得最终的结果.算法首先将连续属性的离散化、属性约简和值约简郁统一转化为可辨识矩阵的化简问题,利用遗传算法的全局并行寻优能力对这一类问题进行统一处理,有效地提高了.算法的执行效率和连贯性,从而保证得到全局最优结果.将建立的专家系统应用于某特定型号220 KV主变压器(隔膜式),获得了较为满意的诊断结果,与当前应用较多的另外7类方法进行比较,验证了本文提出方法的有效性和实用性.     

基于贝叶斯网络和粗糙集的信息融合方法研究及应用

利用贝叶斯网络处理不确定性问题能力强和粗糙集约简能够去除冗余性特征的优势,提出了一种基于贝叶斯网络和粗糙集的信息融合方法。该方法提取齿轮泵振动信号的幅域量纲参数作为来自不同传感器的多源信息,改进了特征属性约简方法,设计了贝叶斯网络分类器,构建了多故障贝叶斯网络对特征进行融合,通过最大后验概率准则识别故障类型。两次融合结果对比分析表明,特征属性约简后诊断正确率明显提高,验证了该方法的有效性和实用性。     

A hybrid approach of rough set theory and genetic algorithm for fault diagnosis

This paper proposes an integrated intelligent system that builds a fault diagnosis inference model based on the advantage of rough set theory and genetic algorithms (GAs). Rough set theory is a novel data mining approach that deals with vagueness and can be used to find hidden patterns in data sets. Based on this approach, minimal condition variable subsets and induction rules are established and illustrated using an application for motherboard electromagnetic interference (EMI) test fault diagnosis. This integrated system successfully integrated the rough set theory for handling uncertainty with a robust search engine, GA. The result shows that the proposed method can reduce the number of conditional attributes used in motherboard EMI fault diagnosis and maintain acceptable classification accuracy. The average diagnostic accuracy of 80% shows that this hybrid model is a promising approach to EMI diagnostic support systems .     

基于自适应免疫检测器的变速器轴承故障诊断研究

针对变速器轴承振动信号不平稳、特征难以提取等问题,以对振动信号的幅值和频率的变化不敏感的5种无量纲参数为特征参数,将这5种特征参数分为自我模式和非我模式,并基于人工免疫原理设计了一种自适应免疫检测器米克服传统变速器轴承故障诊断方法的局限,重新定义了自适应变异和抑制阈。在变速器轴承实验台上,首先对变速器轴承6种状态下的振动加速度信息进行了测量;然后计算了这些振动信息的无量纲指标;再用所得到的5种无量纲特征参数训练免疫检测器,使之成为成熟检测器;最后采用训练好的成熟检测器对变速器轴承的故障状态进行了诊断。试验结果表明：利用自适应免疫检测器进行变速器轴承故障诊断是可行的,而且该检测器具有较强的故障识别能力。     

一种基于聚类的滚动轴承故障诊断方法

近年来,机器学习技术在故障智能诊断领域得到了广泛的应用,聚类作为最主要的无监督学习技术在基于机器学习的故障智能诊断中占有重要的地位。滚动轴承故障诊断中,传统的频谱分析法通常采用共振解调技术,但当内圈、滚动体或多点故障时,产生复合调制,从解调谱线很难分辨故障类型。针对此,提出了一种新的基于模糊聚类的滚动轴承故障诊断方法,该方法以模糊Fisher准则为聚类目标,通过对待测样本与已知状态样本数据聚类,求得待测样本隶属度,进而判断滚动轴承的故障类型。实验结果表明该方法是有效的。