基于小波包分解和PSO-BPNN的滚动轴承故障诊断

针对现有煤矿旋转机械滚动轴承故障诊断方法存在信号有效特征提取不完全、故障诊断精度不高及效率低等问题,提出了一种基于小波包分解和粒子群优化BP神经网络的滚动轴承故障诊断方法。该方法包括信号特征提取和故障类型识别两部分:在信号特征提取部分,对采集的滚动轴承振动信号进行小波包分解,得到各子频带能量及信号总能量,经归一化处理后获得表征滚动轴承状态的特征向量;在故障类型识别部分,通过粒子群优化算法优化BP神经网络的初始权值和阈值,以加速网络收敛速度,避免陷入局部极小值。实验结果表明,该方法提高了滚动轴承故障诊断效率和准确率。     

滚动轴承故障诊断的仿真研究

研究滚动轴承故障诊断问题,故障振动信号具有非平稳性、突变性。由于运行中噪声影响识别故障信号,传统傅立叶变换或单一小波分析难以对特征信号进行准确提取,导致滚动轴承故障诊断正确率较低。为了提高了滚动轴承故障诊断正确率,提出一种小波分析和Hilbert变换的滚动轴承故障诊断方法。首先采用小波分析对采集滚动轴承信号进行分解,消除噪声信息,然后采用Hilbert变换对信号进行进一步精细分解。利用MATLAB软件对滚动轴承故障进行仿真,仿真结果表明,改进算法提高了滚动轴承故障诊断正确率,很适合处理滚动轴承的故障信号。     

滚动轴承故障诊断优化仿真研究

研究提高滚动轴承故障诊断准确率问题,滚动轴承故障振动信号具有非平稳,造成系统不稳定,针对传统方法难以提取故障信息的不足,提出一种小波包和最小二乘支持向量机的滚动轴承故障诊断方法(WP-LSSVM)。首先采用小波包对滚动轴承振动信号进行降噪处理,消除背景和噪声信息,然后小波包对去噪后振动信号分解并计算能量特征值,最后采用最小二乘支持向量机对能量特征值进行学习,建立滚动轴承故障诊断模型。仿真结果表明,滚动轴承故障诊断训练和测试时间减少,且故障诊断准确率得到提高。     

基于Peakvue技术的滚动轴承故障诊断

滚动轴承作为旋转机械的关键部件,其运行状态决定设备以及整个系统的性能.滚动轴承出现故障时会产生高频的应力波信号,而Peakvue技术能够有效的检测应力波,运用一种基于Peakvue技术的滚动轴承故障诊断方法.该方法采用加速度传感器采集滚动轴承振动信号,利用高通滤波器滤除加速度传感器输出信号中不必要的低频部分,按照一定的时间间隔对高频信号和应力波信号进行峰值提取,并对提取的峰值信号进行包络检波处理分析故障类型.应用西储大学轴承数据集进行验证,结果表明该方法能准确有效地检测出滚动轴承的故障类型.     

基于SVM的旋转机械故障诊断方法

提取时域与频域共20个特征参数作为数据样本,选择适合旋转机械振动信号的径向基函数及相关参数,基于一对多法构造支持向量机(SVM)多类分类器,实现旋转机械滚动轴承的故障诊断。通过对振动信号特征进行训练与测试,并与BP神经网络进行对比结果表明,该SVM多类分类器可较好地解决小样本问题,在训练时间和识别正确率上均优于BP神经网络。     

变工况滚动轴承故障诊断方法综述

智能制造背景下,旋转机械工况更加复杂,运行条件更加严峻,设备的运行状态监测与故障诊断更加重要。变工况条件下,轴承振动信号存在幅值变、脉动冲击间隔、采样相位不恒定和信号噪声污染等特点,传统滚动轴承故障诊断方法的应用受到了限制。针对变工况条件下的轴承故障诊断技术,发展了以阶次跟踪、时频分析、随机振动以及混沌理论等人工提取特征的信号解调与分析方法、以卷积神经网络、自编码器与深度置信网络为代表的深度学习方法以及迁移学习方法。回顾近五年变工况轴承故障诊断领域的进展,从算法原理、算法优化以及算法实际应用等角度,详细介绍几种当前主流的变工况故障诊断方法,讨论各类算法的优势不足及适用场景,为后续的研究指明方向。     

基于振动和声发射技术的滚动轴承故障分析

对声发射和振动两种方法在滚动轴承裂纹故障诊断中的应用进行了研究,并比较了这两种方法在早期故障诊断中的优越性。采用电火花技术在滚动轴承内圈加工宽度为0.5 mm和0.8 mm的两种裂纹,模拟在实际情况下的滚动轴承内圈裂纹故障。在低速(10 r/min)和高速(1 000 r/min)两种情况下,通过试验分别测得滚动轴承两种裂纹故障状态下的声发射信号和振动信号。分析比较了不同转速、不同裂纹故障状态下的轴承振动和声发射信号的时频特征。对信号数据进行波形和包络谱分析。试验结果表明:声发射信号与调制信号无关。对声发射信号进行包络分析,可以有效分析信号的故障特征频率。在故障微弱情况下,声发射信号比振动信号更具敏感性;当故障强度较大时,采用振动方法诊断更为准确,即声发射技术更有利于故障的早期检测。声发射技术可作为振动分析技术的补充,用于故障检测。     

基于双树复小波包和改进SVM的轴承故障诊断

为了提高滚动轴承内圈、滚动体、外圈等故障诊断效率,提出了将双树复小波包和支持向量机（Support Vector Machine,SVM）结合的故障诊断方法。采用双树复小波包对轴承振动信号分解和重构,提取重构信号中的故障能量特征并构造特征样本作为支持向量机诊断模型的输入。针对支持向量机的参数选取没有固定方法而导致故障诊断的准确性降低的问题,采用人工鱼群算法对支持向量机的惩罚系数和核参数进行寻优。用寻优得到的参数建立支持向量机诊断模型对特征样本进行故障诊断。仿真结果表明提出的方法不仅可以提高降噪效果从而得到滚动轴承故障振动的特征信号,而且能实现更高精度的故障诊断。     

基于多特征融合与GSA-SVM的滚动轴承故障诊断

滚动轴承在故障诊断过程中，存在着单一特征诊断准确率较低且无法充分表征故障信号所包含信息的问题。提出一种基于局部线性嵌入算法(Locally Linear Embedding, LLE)结合熵权法(the Entropy Weight Method, EWM)的多特征融合方法，结合引力搜索算法(Gravitational Search Algorithm, GSA)改进支持向量机(Support Vector Machine, SVM)实现滚动轴承的故障诊断。首先采用LLE-EWM对提取到的48维故障特征进行筛选融合，然后结合GSA-SVM模型对提取到的融合特征进行诊断，从而实现对滚动轴承变负载条件下的故障诊断。通过凯斯西储大学滚动轴承实测振动信号，对所提故障融合诊断方法的有效性进行验证。在特征筛选阈值设定为60%时，滚动轴承故障诊断的准确率达到99.7%。对比不同模型，所提方法具有最高的诊断准确率。试验结果表明，所提方法能够实现对故障信号特征信息的深度提取及提高故障诊断精度。     

小波阈值函数旋转机械振动信号去噪方法研究

从强背景噪声中提取出微弱的旋转机械振动故障特征信号一直是技术性难题。针对传统全局阈值函数去噪在阈值处不连续和存在恒定偏差的问题,提出一种改进的小波阈值函数分层去噪方法。首先对旋转机械故障信号去噪中的小波参数进行了筛选,然后采用改进的阈值函数,利用最优小波参数对振动信号进行分层阈值降噪处理。理论仿真和实测结果表明,对比传统阈值去噪方法,该方法能有效去除背景噪声,保留振动信号原貌特征信息,提高信噪比和减小均方根误差,适合非平稳振动信号去噪,为旋转机械故障诊断奠定了信号预处理的基础。     

A deep feature enhanced reinforcement learning method for rolling bearing fault diagnosis

Fault diagnosis of rolling bearing is crucial for safety of large rotating machinery. However, in practical engineering, the fault modes of rolling bearings are usually compound faults and contain a large amount of noise, which increases the difficulty of fault diagnosis. Therefore, a deep feature enhanced reinforcement learning method is proposed for the fault diagnosis of rolling bearing. Firstly, to improve robustness, the neural network is modified by the Elu activation function. Secondly, attention model is used to improve the feature enhanced ability and acquire essential global information. Finally, deep Q network is established to accurately diagnosis the fault modes. Sufficient experiments are conducted on the rolling bearing dataset. Test result shows that the proposed method is superior to other intelligent diagnosis methods.     

Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and bi-LSTM

With the development of industry and technology, mechanical systems’ safety has strong relations with the diagnosis of bearing faults. Accurate fault diagnosis is essential for the safe and stable operation of rotating machinery. Most former research depends too much on the fault signal specificity and learning model’s choices. To overcome the disadvantages of lacking intrinsic mode function (IMF) modal aliasing, low degree of discrimination between data of different fault types, high computational complexity. This paper proposes a method that combines multi-scale weighted entropy morphological filtering (MWEMF) signal processing and bidirectional long-short term memory neural networks (Bi-LSTM). The developed rolling bearing fault diagnosis strategy is then implemented to different databases and potential models to demonstrate the greatly improved system’s ability to reconstruct the time-to-frequency domain characteristics of fault signature signals and reduce learning cost. After verification, the classification accuracy of the proposed model reaches 99%.

     

基于BO-DKELM的滚动轴承故障诊断

滚动轴承作为旋转机械中的必需元件,其任何故障都可能导致机器乃至整个系统发生故障,从而导致巨大的经济损失和时间的浪费,因此必须要及时准确地诊断滚动轴承故障。针对传统极限学习机中模型参数对滚动轴承故障诊断精度影响较大的问题,提出了一种基于贝叶斯优化的深度核极限学习机的滚动轴承故障诊断方法。首先,将自动编码器与核极限学习机相结合,构建了深度核极限学习机(Deep kernel extreme learning machine, DKELM)模型。其次,利用贝叶斯优化(Bayesian optimization, BO)算法对DKELM中的超参数进行寻优,使得训练数据集和验证数据集在DKELM模型中的分类错误率之和最低。然后,将测试数据集输入到训练好的BO-DKELM中进行故障诊断。最后,采用凯斯西储大学轴承故障数据集对所提方法进行验证,最终故障诊断精度为99.6%,与深度置信网络和卷积神经网络等传统智能算法进行对比,所提方法具有更高的故障诊断精度。     

邻域知识图算法在旋转机械设备故障诊断中的应用

旋转机械应用过程中极易出现内环故障、外环故障、滚动体故障的情况,而这也直接影响机械部件的使用寿命。为准确诊断设备元件的故障行为,达到延长旋转机械设备寿命水平的目的,针对邻域知识图算法在旋转机械设备故障诊断中的应用展开研究。求解邻域知识图算法的函数表达式,并以此为基础,完成对故障数据的推荐,再通过预处理的方式,实现对旋转机械设备故障数据的深度挖掘。融合关键故障数据,并对其进行降维处理,根据核特征定义条件,完善具体的故障诊断流程,完成基于邻域知识图算法的旋转机械设备故障诊断算法的设计。实验结果表明,上述方法的应用,可以准确诊断出内环故障、外环故障、滚动体故障三种故障表现行为,通过适当方法对所诊断出故障行为加以处理,可以达到延长旋转机械设备使用寿命的目的。     

基于深度学习的旋转机械故障诊断研究综述

在现代化生产中,旋转机械的精密性和重要性越来越高,朝着大型、高速和自动化方向发展,以至传统故障诊断方法不足以处理海量、多源、高维的测量数据,不能满足安全性和可靠性的要求;因此,首先简要介绍几种典型的深度学习模型,并结合深度学习强大的特征提取能力和聚类分析的优势,对其近些年来在转子系统、齿轮箱和滚动轴承故障诊断的应用情况进行了对比分析;最后总结深度学习模型的优缺点,并从工程实际出发对旋转机械的故障诊断方法进行总结与展望。     

An intelligent fault diagnosis method based on wavelet packer analysis and hybrid support vector machines

In this paper, a new intelligent method for the fault diagnosis of the rotating machinery is proposed based on wavelet packet analysis (WPA) and hybrid support machine (hybrid SVM). In fault diagnosis for mechanical systems, information about stability and mutability can be further acquired through WPA from original signal. The faulty vibration signals obtained from a rotating machinery are decomposed by WPA via Dmeyer wavelet. A new multi-class fault diagnosis algorithm based on 1-v-r SVM approach is proposed and applied to rotating machinery. The extracted features are applied to hybrid SVM for estimating fault type. Compared to conventional back-propagation network (BPN), the superiority of the hybrid SVM method is shown in the success of fault diagnosis. The test results of hybrid SVM demonstrate that the applying of energy criterion to vibration signals after WPA is a very powerful and reliable method and hence estimating fault type on rotating machinery accurately and quickly.     

Fault diagnosis and condition surveillance for plant rotating machinery using partially-linearized neural network

Fault diagnosis and condition surveillance of rotating machinery in a plant is very important for guaranteeing production efficiency and plant safety. In a large plant, with an enormous number of rotating machines, condition surveillance and fault diagnosis for all rotating machines is not only time consuming and labor intensive, but the accuracy of condition judgment cannot be ensured. These difficulties may cause serious machine accidents and consequently great production losses. In order to improve the efficiency of condition surveillance and detect faults at an early stage, this paper proposes a method of condition surveillance and fault discrimination for rotating plant machinery using non-dimensional symptom parameters in a time domain and “Partially-linearized Neural Network” (PLNN), from which the state of a rotating machine can be discriminated automatically. The verification results of precise diagnosis for rolling bearings show that the PLNN can effectively distinguish bearing faults. The verification results for condition surveillance of rotating machinery in a real plant show that the PLNN correctly judges the machine state of the inspected rotating machine as normal or abnormal.     

基于BP神经网络的故障诊断模型研究

滚动轴承是旋转机械中最常用的部件之一。滚动轴承很容易损坏，而它的工作条件通常比较复杂，很难对其故障进行准确判断。为了提高滚动轴承故障诊断的有效性，构建了一种新的基于改进量子蜂群算法和BP神经网络的滚动轴承故障诊断模型（IQABC-BP）。首先针对量子蜂群算法在种群初始化和进化过程中存在的问题，提出了一种改进量子蜂群算法，然后利用改进量子蜂群算法对BP神经网络的初始权值、阈值和隐含层单元数进行优化，建立了一种具有超并行超高速的基于改进量子蜂群算法的BP神经网络模型，并应用于滚动轴承的故障诊断中。实验结果表明，IQABC-BP模型收敛速度更快，故障诊断效果更好，具有很好的应用价值。     

基于广义形态滤波和MRSVD的故障诊断方法研究

为了从复杂工况下获取滚动轴承故障信息,提出了一种基于广义形态滤波和多分辨奇异值分解（Multi-Resolution Singular Value Decomposition,MRSVD）相结合的方法。首先利用广义形态学滤波方法对振动信号进行降噪预处理;然后利用MRSVD对降噪后的振动信号进行分解;最后通过峭度准则选取故障特征最丰富的细节信号,并对其进行Hilbert包络谱分析。将提出的方法应用于滚动轴承的故障检测,实验结果表明该方法能清晰地提取故障特征信息。     

Application of an intelligent classification method to mechanical fault diagnosis

A new method for intelligent fault diagnosis of rotating machinery based on wavelet packet transform (WPT), empirical mode decomposition (EMD), dimensionless parameters, a distance evaluation technique and radial basis function (RBF) network is proposed in this paper. In this method, WPT and EMD are, respectively, used to preprocess vibration signals to mine fault characteristic information more accurately. Then, dimensionless parameters in time domain are extracted from each of the original vibration signals and preprocessed signals to form a combined feature set. Moreover, the distance evaluation technique is utilised to calculate evaluation factors of the combined feature set. Finally, according to the evaluation factors, the corresponding sensitive features are selected and input into the RBF network to automatically identify different machine operation conditions. An experiment of rolling element bearings is carried out to test the performance of the proposed method. The experimental result demonstrates that the method combining WPT, EMD, the distance evaluation technique and the RBF network may accurately extract fault information and select sensitive features, and therefore it may correctly diagnose the different fault categories occurring in the bearings. Furthermore, this method is applied to slight rub fault diagnosis of a heavy oil catalytic cracking unit, the actual result shows the method may be applied to fault diagnosis of rotating machinery effectively.