柔性形态滤波和遗传规划在电机轴承故障诊断的应用

针对电机轴承故障振动信号的强噪声背景.以及电机轴承故障是一个内圈故障、外圈故障和滚动体故障多级分类问题的情况,提出了一种基于柔性形态滤波和遗传规划的电机轴承故障诊断方法.该方法首先对电机轴承故障原始信号进行柔性形态滤波,然后提取滤波后信号的故障特征频率的归一化能量以及时域统计特征量作为遗传规划中的终止符,通过复制、交叉、突变以及适应度计算等操作,使个体逐渐逼近问题的最优解,得到电机轴承故障模式分类的最优模型,试验结果表明了该方法的有效性.     

Fault features extraction for bearing prognostics

Over the past years, investigation on condition-based maintenance (CBM) technique on bearing has been conducted. Bearing diagnostics and prognostics are the important aspects in CBM. A key to the success of using vibration data for bearing fault diagnostics and bearing lifecycle prognostics is a quantified relationship between bearing damage and bearing fault features. To establish such a quantitative relationship, effective signal processing techniques to extract bearing fault features from vibration signals are needed. This paper describes a newly developed fault feature extraction method for bearing prognostics. The effectiveness of the method is demonstrated with two real bearing run-to-failure test datasets: one collected under normal operating conditions and another one under abnormal operating conditions. Experimental results show that the bearing fault features extracted using both traditional vibration analysis methods and the proposed method give clear bearing heath degradation trend for the dataset collected under normal operating conditions. However, for the data collected under abnormal operating conditions, bearing fault features obtained using traditional vibration analysis methods fail to show the bearing health degradation trend while the fault features extracted using the proposed method give consistent bearing degradation trends.     

基于压缩采集与CNN的风电机轴承故障诊断

针对传统风电机轴承故障检测存在的采样数据量大、故障特征依赖主观选取的问题,提出了风电机轴承故障的信号压缩采集、自动提取特征及故障诊断的方法,解决了风电机轴承振动信号特征提取计算复杂、受先验知识影响较大的问题.首先基于梯度加速法(NAG)和QR分解理论对随机高斯观测矩阵进行优化,实现风电机轴承振动信号压缩采集;然后将压缩...     

一种城市轨道车辆轴箱轴承故障特征提取方法

针对城市轨道车辆轴箱轴承结构特点及轴承故障时产生振动信号的特征,提出了一种新的轴箱轴承故障特征提取方法。引入卷积滤波,对采集的轴箱轴承振动信号进行分频、滤波、重构,提取轴承故障特征。在故障特征提取的基础上,进行轴承故障识别及定位。结果表明：该方法对振动信号进行分析,具有不压缩、任意分频和减少截断误差的性质;可以更好地获得故障特征;能够有效地应用于轴箱轴承的故障诊断。     

A novel RSG-based intelligent bearing fault diagnosis method for motors in high-noise industrial environment

Bearing fault diagnosis is a critical and challenging task for prognostics and health management of motors. The ability to efficiently and accurately classify the fault categories based on sensor signals is the key to successful bearing fault diagnosis. Although various data-driven methods have been developed for fault diagnosis in recent years, automatic and effective extraction of discriminative fault features from high-noise vibration signals generated in the real-world industrial environment remains a challenging task. To tackle this challenge, this paper proposes a novel deep learning method based on the combination of residual building Unit, soft thresholding and global context, called RSG, to solve the complex mapping relationship between vibration signals and different types of bearing faults. The proposed RSG integrates the working mechanisms of soft threshold and global context to achieve effective noise reduction and feature extraction. A comparative analysis is performed to demonstrate the advantages of the proposed method. Furthermore, the proposed method is tested on a faulty motor dataset collected by our developed intelligent motor test platform based on Industrial Internet of Things. Experimental results show that our method can achieve an average fault diagnosis accuracy of 98%. Thus, the proposed method proves to be an efficient solution for intelligent bearing fault diagnosis for motors in a high-noise industrial environment.     

短丝纤维卷绕牵伸齿轮箱故障信号的图像特征提取方法

针对短丝纤维卷绕牵伸齿轮箱故障信号不易提取的问题,提出了基于图像纹理信息的特征提取方法。通过对齿轮箱振动信号进行小波包双谱分析,获得具有稳定纹理信息的振动信号双谱图,采用基于小波变换对双谱图进行图像融合,提高图像的综合纹理特征。采用灰度共生矩阵的四个特征参数对振动信号的双谱图进行加权融合特征提取。在短丝生产线上对齿轮箱常见的齿轮破损和裂纹进行了实验分析,结果表明本文方法的故障识别率达到85%以上。     

基于SVD-AR模型与VPMCD的轴承故障诊断方法

针对强噪声背景下振动信号故障特征难以提取的问题,提出了基于奇异值分解的自回归(SVD-AR)模型,用于提取振动信号的特征,并与变量预测模型模式识别(VPMCD)方法相结合应用于轴承故障诊断.对轴承振动信号进行SVD;然后,利用奇异值差分谱对分量信号进行筛选,对能够反映故障信息的分量信号建立AR模型,提取轴承振动信号的特征信息;采用VPMCD对滚动轴承运行状态进行识别.实验证明了方法的合理性和有效性.     

S能量谱特征提取在轴承故障诊断中的应用

准确提取振动信号的特征,是滚动轴承故障检测的关键问题,为此提出一种基于S能量谱特征提取的故障诊断方法。该方法对振动信号进行S变换,得到时频矩阵,并构建S能量谱,对S能量谱进行奇异值分解（Singular Value Decomposition,SVD）分析,得到能够反映S能量谱特征的奇异值,利用变量预测模型（Variable Predictive Model based Class Discriminate,VPMCD）方法,通过建立特征值之间的内在关系,构建故障识别模型。将所提方法应用于滚动轴承故障检测,实验结果表明,S能量谱特征提取轴承故障诊断方法具有较高的正判率。     

基于Gibbs抽样的轴承故障诊断方法

针对现有轴承故障诊断方法的不足,即诊断片面性问题,提出了一种基于Gibbs抽样的轴承故障诊断方法。首先对轴承振动信号进行局部特征尺度分解（LCD）得到内禀尺度分量（ISC）;然后对轴承振动信号和ISC分别提取时域特征,按照特征敏感度高低对时域特征排名,选择排名靠前的特征组成特征集;其次使用特征集训练产生基于Gibbs抽样的多维高斯分布模型;最后通过后验分析得到概率,实现轴承故障诊断。实验结果表明诊断正确率达到100%,与基于SVM的轴承诊断方法相比,在特征数为43个时诊断正确率提升了11.1个百分点。所提方法能够有效地对滚动轴承故障状态进行诊断,对高维复杂的轴承故障数据也有很好的诊断效果。     

Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning

Roller bearing is one of the most widely used elements in rotary machines. Condition monitoring of such elements is conceived as pattern recognition problem. Pattern recognition has three main phases: feature extraction, feature selection and feature classification. Histogram features can be used for fault diagnosis of roller bearing. This paper presents the use of decision tree for selecting best few histogram features (bin ranges) that will discriminate the fault conditions of the bearing from given train samples. These features are extracted from vibration signals. A rule set is formed from the extracted features and fed to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper also presents the usage of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezoelectric transducer is captured for the following conditions – good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race fault. The histogram features were extracted and good features that discriminate the different fault conditions of the bearing were selected using decision tree. The rule set for fuzzy classifier is obtained by once using the decision tree again. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging.     

基于SVD和TKEO的轴承振动信号特征提取

为了解决滚动轴承振动信号中微弱故障信息难以提取的问题,提出了一种基于奇异值分解（Singular Value Decomposition,SVD）和Teager-Kaiser能量算子（Teager-Kaiser Energy Operator,TKEO）的轴承振动信号特征提取方法。采用SVD将突变信息从背景噪声和光滑信号中分离,提取信号的突变信息;利用TKEO计算突变信息的瞬时能量,对该能量信号进行频谱分析,从而提取出轴承振动信号的能量频谱特征,用于故障检测。将该方法应用于轴承外圈、内圈局部故障状态下的振动信号特征提取,利用特征信息能够准确检测并识别出故障类型,表明了该方法的可行性和有效性。     

基于改进一维卷积和双向长短期记忆神经网络的故障诊断方法

针对工业领域中故障诊断数据存在时序性和夹杂强噪声的特点导致的收敛速度慢以及诊断精度低的问题,提出了一种基于改进一维卷积和双向长短期记忆（1DCNN-BiLSTM）神经网络融合的故障诊断方法。该方法包括故障振动信号的预处理、特征的自动提取以及振动信号的分类。首先,采用自适应白噪声的完整经验模态分解（CEEMDAN）技术对原始振动信号进行预处理;其次,构建1DCNN-BiLSTM双通道模型,将处理后信号输入双向长短期记忆（BiLSTM）神经网络模型和一维卷积神经网络（1DCNN）模型两个通道,从而对信号的时序相关性特征、局部空间的非相关性特征和弱周期性规律进行充分提取;然后,针对信号夹杂强噪声的问题,对压缩与激励网络（SENet）模块进行改进并将其作用于两个不同的通道;最后,输入全连接层将双通道提取的特征进行融合并借助Softmax分类器实现对设备故障的精确识别。使用凯斯西储大学轴承数据集进行实验,结果表明改进后的SENet模块同时作用于1DCNN通道和stacked BiLSTM通道,1DCNN-BiLSTM双通道模型在保证快速收敛的情况下有最高诊断精度96.87%,优于传统单通道模型,有效提高了机械设备故障诊断效率。     

基于MED和边际谱的自动倾斜器轴承故障诊断

针对直升机自动倾斜器轴承早期微弱故障特征易被强烈背景噪声淹没的问题，提出了一种基于最小熵反褶积（Minimum Entropy Deconvolution，MED）和边际谱的自动倾斜器轴承故障诊断方法。采用MED对采集的振动信号进行滤波降噪，提高了信号的信噪比，突出了轴承早期微弱故障特征；通过Hilbert变换和经验模态分解（Empirical Mode Decomposition，EMD）获取去噪包络信号的本征模态函数（Intrinsic Mode Functions，IMF）集，并引入峭度筛选准则选取合理IMF集计算局部Hilbert边际谱，有效地提取了故障特征频率，能够通过故障特征频率进行故障类型判别。通过某型直升机自动倾斜器故障诊断试验系统验证了该诊断方法的合理性和可行性。     

基于SVD-形态降噪的TKEO故障诊断方法研究

针对强噪声干扰背景下微弱故障特征信息难以提取的问题,提出了一种基于奇异值分解(SVD)-形态降噪的Teager能量算子(TKEO)故障诊断方法.首先对轴承振动信号进行SVD,对得到的分量信号进行形态滤波,以滤除噪声干扰;然后利用峭度准则对分量信号进行筛选,并对其进行重构;最后利用TKEO计算重构信号的瞬时能量,得到信号的能量谱,提取振动信号的特征.将提出的方法应用于滚动轴承故障分析,结果表明该方法能清晰地提取故障特征信息.     

基于EMD和奇异值分解技术的滚动轴承故障诊断方法

提出了基于EMD(Empirical mode decomposition)和奇异值分解技术的滚动轴承故障诊断方法。采用EMD方法将滚动轴承振动信号分解成若干个基本模式分量(Intrinsic mode function，IMF)之和，并形成初始特征向量矩阵。然后对初始特征向量矩阵进行奇异值分解得到矩阵的奇异值，将其作为滚动轴承振动信号的状态特征向量，通过建立Mahalanobis距离判剐函数判断滚动轴承的工作状态和故障类型。实验数据的分析结果表明，本文方法能有效地应用于滚动轴承故障诊断。     

AKSNet: A novel convolutional neural network with adaptive kernel width and sparse regularization for machinery fault diagnosis

Convolutional kernels have significant affections on feature learning of convolutional neural network (CNN). However, it is still a challenging problem to determine appropriate kernel width. Moreover, some features learned by convolutional layers are still redundant and noisy. Thus, adaptive selection of kernel width and feature selection of feature maps are key techniques to improve feature learning performance of CNNs. In this paper, a new deep neural network (DNN) model, adaptive kernel sparse network (AKSNet) is proposed to extract multi-scale fault features from one-dimensional (1-D) vibration signals. Firstly, an adaptive kernel selection method is developed, where multiple branches with different kernels are used to extract multi-scale features from vibration signals. Channel-wise attention is developed to fuse features generated by these kernels to obtain different informative scales. Secondly, a spatial attention is used for dynamic receptive field to focus on salient region of feature maps. Thirdly, a sparse regularization layer is embedded in the deep network to further filter noise and highlight impaction of the feature maps. Finally, two cases are adopted to verify effectiveness of AKSNet-based feature learning for bearing fault diagnosis. Experimental results show that AKSNet can effectively extract features from multi-channel vibration signals and then improves fault diagnosis performance of the classifier significantly. AKSNet shows better recognition performance in comparison with that of shallow neural networks and other typical DNNs.     

基于自注意力机制辅助分类生成对抗网络的轴承故障诊断

针对传统判别式轴承故障诊断算法在复杂工况下依赖人工特征提取、诊断效果不佳的问题,提出将生成式模型辅助分类生成对抗网络(auxiliary classifier generative adversarial network,ACGAN)用于轴承故障诊断研究。首先,通过快速傅里叶变换将轴承振动信号转为2维频域特征灰度图,设计卷积网络作为模型主体结构,添加批量归一化和LeakyReLU激活函数缓解梯度消失问题;其次,引入自注意力机制(self-attention mechanism,SA),将数据中相距较远的特征相互关联建立新的SA-ACGAN模型,实现多分类场景下对原始数据分布特征的有效学习;最后,将模型应用于电机轴承进行对比验证,结果表明所提方法故障诊断准确率高达99.7％,并具有良好的鲁棒性和泛化性。     

基于Hilbert-Huang变换的特征能量法及其在滚动轴承故障诊断中的应用

Hilbert-Huang变换是一种新的自适应信号处理方法,非常适用于非线性和非平稳过程。该文在介绍Hilbert-Huang变换的基础上,针对滚动轴承故障振动信号的非平稳特征,提出了一种基于Hilbert-Huang变换的特征能量法。该方法在Hilbert-Huang变换的基础上定义滚动轴承振动信号在固有频率段的能量为特征能量,以此作为滚动轴承的故障特征向量,并通过建立M-距离判别函数来识别滚动轴承的故障类型。对滚动轴承外圈和内圈故障信号的分析结果表明,基于Hilbert-Huang变换的特征能量法可以有效地提取滚动轴承振动信号的故障特征。     

基于蝙蝠算法优化相关向量机的轴承故障诊断方法

由于轴承振动信号具有复杂性和非线性,难以有效提取故障特征,影响故障诊断的准确率.为了提高故障诊断准确率,提出一种蝙蝠算法(BA)优化相关向量机(RVM)的轴承故障诊断方法.首先结合变分模态分解和多尺度熵从轴承振动信号中提取出故障特征,作为相关向量机的输入向量;接着采用蝙蝠算法优化相关向量机的核函数参数;然后训练相关向量...     

基于改进深度卷积神经网络的轴承故障诊断

轴承为风电机组的重要且故障频发部件,传统基于轴承振动数据的图像转换的卷积神经网络(CNN)的故障诊断技术存在一定局限性。提出了一种基于改进深度卷积神经网络(IDCNN)的直接时间序列特征提取方法,依据采样频率将原始振动数据划分为单个样本,构建诊断模型训练数据集。设计了一种新型的深度卷积神经网络(IDCNN),自动提取复杂样本数据的故障特征,提高DCNN的鲁棒性和泛化性,并将IDCNN提取的高维故障特征输入到分类器中,从而实现轴承故障的智能诊断。对比实验结果表明本方法有效提升了故障诊断精度。