Fault diagnosis method of link control system for gravitational wave detection

To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Different from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling nonlinearity.By constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault parameters.The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.

Morphology Similarity Distance for Bearing Fault Diagnosis Based on Multi-Scale Permutation Entropy

Bearings are crucial components in rotating machines,which have direct effects on industrial productivity and safety.To fast and accurately identify the operating condition of bearings,a novel method based on multi⁃scale permutation entropy(MPE)and morphology similarity distance(MSD)is proposed in this paper.Firstly,the MPE values of the original signals were calculated to characterize the complexity in different scales and they constructed feature vectors after normalization.Then,the MSD was employed to measure the distance among test samples from different fault types and the reference samples,and achieved classification with the minimum MSD.Finally,the proposed method was verified with two experiments concerning artificially seeded damage bearings and run⁃to⁃failure bearings,respectively.Different categories were considered for the two experiments and high classification accuracies were obtained.The experimental results indicate that the proposed method is effective and feasible in bearing fault diagnosis.

Gearbox Fault Diagnosis using Adaptive Zero Phase Time-varying Filter Based on Multi-scale Chirplet Sparse Signal Decomposition

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

Fault diagnosis model based on multi-manifold learning and PSO-SVM for machinery

Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine(PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.

SIMULATION OF CRACK DIAGNOSIS OF ROTOR BASED ON MULTI-SCALE SINGUUR-SPECTRUM ANALYSIS

In the diagnosis of rotor crack based on wavelet analysis, it is a painful task to find out an adaptive mother wavelet as many of them can be chosen and the analytic results of different mother wavelets are yet not the same. For this limitation of wavelet analysis, a novel diagnostic approach of rotor crack based on multi-scale singular-spectrum analysis （MS-SSA） is proposed. Firstly, a Jeffcott model of a cracked rotor is developed and the forth-order Runge-Kutta method is used to solve the motion equations of this rotor to obtain its time response （signals）. Secondly, a comparatively simple approach of MS-SSA is presented and the empirical orthogonal functions of different orders in various scales are regarded as analyzing functions. At last, the signals of the cracked rotor and an uncracked rotor are analyzed using the proposed approach of MS-SSA, and the simulative results are compared. The results show that, the data-adaptive analyzing functions can capture many features of signals and the rotor crack can be identified and diagnosed effectively by comparing the analytic results of signals of the cracked rotor with those of the uncracked rotor using the analyzing functions of different orders.

A bearing fault diagnosis method based on sparse decomposition theory

The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.

基于GMPE和GWO-MKELM算法的往复压缩机轴承故障诊断

针对往复压缩机内部结构复杂,轴承间隙故障特征提取困难和识别准确率不高等问题,提出了多尺度排列熵和多核极限学习机混合算法的智能诊断新方法。首先,针对多尺度排列熵在多尺度过程中,利用均值粗粒化的方式在一定程度上“中和”了原始信号的动力学突变行为,降低了熵值分析的准确性,提出了一种广义多尺度排列熵算法;然后,为解决核极限学习机处理复杂数据样本分类存在的局限性,将高斯核函数、多项式核函数和感知器核函数进行线性叠加,构建混合核函数,提出了多核极限学习机模型。仿真实验结果表明,该故障诊断方法识别准确率高达98%,高效地实现了轴承不同种类故障的智能诊断。

基于HMFDE和t-SNE的旋转机械故障诊断方法

针对旋转机械的故障特征提取较难,以及单一类型的特征无法全面反映故障特性的问题,提出了一种基于混合多尺度波动散布熵(HMFDE)、t分布-随机邻域嵌入(t-SNE)和郊狼优化算法(COA)优化极限学习机(ELM)的旋转机械故障诊断方法。首先,采用特征加权提出了混合多尺度波动散布熵方法,并将其用于提取旋转机械振动信号的故障特征;随后,采用t-SNE方法对混合故障特征进行了特征降维,挑选出了最能够反映故障特性的特征子集,构建了敏感特征样本;最后,采用郊狼优化算法对极限学习机的输入权重和隐含层阈值进行了优化,完成了旋转机械的故障识别和分类;以齿轮箱和滚动轴承故障数据集为对象,对基于HMFDE、t-SNE和COA-ELM的故障诊断方法进行了实验,验证了方法的有效性。研究结果表明:采用HMFDE-t-SNE-CAO-ELM故障诊断方法可以取得100%的故障识别准确率,该方法能够有效地诊断旋转机械的不同故障类型和损伤;相较于基于单一类型特征的故障诊断方法,其准确率分别可以提高0.68%、22.42%、29.18%(齿轮箱)和1.43%、8.23%、23.67%(滚动轴承),虽然牺牲了一定的计算效率,但准确率得到了明显的提高;相较于其他常规故障分类器,COA-ELM的故障识别准确率具有明显的优势。

基于交互式多头注意力的机械故障诊断方法

针对旋转机械振动信号复杂且难以提取有效故障特征,多头注意力故障诊断方法计算复杂度高的情况,提出一种交互式多头注意力的机械故障诊断方法。通过对输入数据的特征图进行窗口分割,降低了注意力的计算复杂度。使用特征图滚动技术,在各自注意力之间和各窗口之间建立数据的联系,在降低计算复杂度的同时保证分类精度。将输入数据的位置信息融入注意力权重矩阵,增强神经网络对数据位置信息的辨别能力。在实验部分,为了测试算法的性能,将其应用到ZHS-2型多功能电机柔性转子试验台进行验证。实验结果表明,与其他数据驱动的故障诊断方法相比,该方法能更有效识别各种故障特征,实现故障诊断。

不同工况及类别下热力系统故障诊断的多源域自适应方法

针对不同负荷工况下,热工参数数据分布差异大且故障类别不一致的问题,提出了一种基于多源样本加权域对抗网络的热力系统故障诊断方法。首先,构建领域共享的一维卷积神经网络以提取多个源域和目标域的深度判别特征;其次,引入加权机制和域一致性损失度量样本,以降低仅存在于源域的故障类别的负迁移影响;然后,通过多域判别器的对抗学习实现每对源域和目标域的特征差异对齐;最后,构建多分类器对齐模块以提高预测的一致性,从而实现多源域不同工况下热力系统故障的准确诊断。借助某600MW超临界机组全范围仿真系统进行故障仿真实验,结果验证了所提方法的鲁棒性和优越性。

基于多领域耦合建模的轴向柱塞泵故障诊断方法

针对轴向柱塞泵传统单一领域建模方法存在的建模困难、仿真精度低以及故障诊断所需故障样本不足的问题,开展基于多领域耦合建模的轴向柱塞泵故障诊断方法。利用Simscape构建轴向柱塞泵多领域耦合模型,并对柱塞泄漏、主轴轴承磨损以及组合故障3种常见的故障进行模拟,再通过故障注入技术和MATLAB快速重启功能获取多种工况、不同故障程度下的压力和流量数据;随后从时域和频域对故障数据进行特征提取,同时利用单因素方差分析对故障特征进行选择;最后利用得到的特征对K邻近、朴素贝叶斯、决策树、神经网络、支持向量机等5种故障诊断算法进行训练,得到故障诊断准确率最高的算法,其平均诊断准确率为98.5%。该方法提高了轴向柱塞泵多领域耦合建模的精确性,实现了对轴向柱塞泵的有效故障诊断。

基于改进多尺度卷积循环神经网络的滚动轴承故障研究

针对传统卷积神经网络故障诊断方法提取特征不丰富,容易丢失故障敏感信息,且在单一尺度处理方法限制实际复杂工况下故障特性的深度挖掘问题,提出了注意力机制的多尺度卷积神经网络和双向长短期记忆(bi-directional long short-term memory,BiLSTM)网络融合的迁移学习故障诊断方法。该方法首先应用不同尺寸池化层和卷积核捕获振动信号的多尺度特征;然后引入多头自注意力机制自动地给予特征序列中的不同部分不同的权重,进一步加强特征表示的能力;其次利用BiLSTM结构引入双向性质提取特征前后之间的内部关系实现信息的逐层传递;最后利用多核最大均值差异减小源域和目标域在预训练模型中各层上的概率分布差异并利用少量标记的目标域数据再对模型进行训练。试验结果表明,所提方法在江南大学(JNU)、德国帕德博恩大学(PU)公开轴承数据集上平均准确率分别为98.43%和97.66%,该方法在重庆长江轴承股份有限公司自制的轴承故障数据集上也表现出了极高的准确率和较快的收敛速度,为有效诊断振动旋转部件故障提供了实际依据。

基于SDP和MCNN-LSTM的齿轮箱故障诊断方法

齿轮箱在长期使用过程中,不可避免地会产生齿轮故障和轴承故障,严重影响传动精度和设备运行安全。基于此,针对齿轮箱常见故障类型,研究多通道对称点图案(symmetrized dot pattern, SDP)数据处理方法,并利用最小能量误差法实现SDP关键参数的选取。结合多尺度卷积神经网络(multi-scale convolutional neural network, MCNN)的空间处理优势、长短时记忆网络(long short term memory, LSTM)的时间处理优势及其良好的抗噪性和鲁棒性,提出了一种基于SDP和MCNN-LSTM的齿轮箱故障诊断模型。同时利用东南大学齿轮箱数据集,验证了基于SDP和MCNN-LSTM的齿轮箱故障诊断方法对齿轮和轴承常见故障类型特征提取的有效性,并与现有其他故障诊断方法进行对比,结果表明了所提方法具有更高的精度。

IMIBSE与ISOMAP在旋转机械故障诊断中的应用

针对基本熵的区域划分标准不理想,导致无法有效测量振动信号的复杂度,使故障诊断的准确率不佳这一问题,提出了一种基于改进多尺度改进基本熵(IMIBSE)、等距特征映射(ISOMAP)和随机森林(RF)的旋转机械故障诊断方法。首先,采用基于方差的区域划分准则对基本熵进行了改进,结合改进的粗粒化处理,提出了IMIBSE,并将其用于提取故障特征;随后,利用ISOMAP对原始故障特征进行了特征降维,选择了对分类贡献最大的一组特征作为故障敏感特征;最后,基于RF建立了多故障分类器,将故障敏感特征输入至RF模型进行了训练和测试,实现了旋转机械的故障识别,利用齿轮箱和离心泵两种故障数据集将IMIBSE方法与复合多尺度基本熵、多尺度改进基本熵、多尺度基本熵进行了比较和分析。研究结果表明:IMIBSE不仅具有最佳的可视化效果,而且取得的识别准确率最高,二者均达到了100%,而二者的平均分类准确率分别为100%和99.8%;相较于其他故障诊断方法,IMIBSE方法的准确率更高,而且适用于小样本的故障识别问题。

基于局部描述子的小样本轴承故障诊断方法

深度学习由于其在特征表示方面的优势,近年来已被应用于很多领域,但是在故障诊断领域很难获取大量的故障样本来训练模型。针对这一问题,提出了一种基于局部描述子的小样本轴承故障诊断方法,利用改进的深度最近邻神经网络(Deep Nearest Neighbor Neural Network,DN4)进行故障诊断。首先通过短时傅里叶变换将振动信号转换为二维时频图像,特征提取主干网络采用ResNet-12网络,引入高效多尺度注意力机制(Efficient Multi-scale Attention,EMA)来更好地提取故障特征,得到轴承故障的局部描述子,最后利用K近邻算法来得到故障类别。为验证所提方法的有效性,进行了不同样本数量、跨工况条件下的轴承故障诊断实验,实验结果表明,文章所提方法在小样本条件下具有较好的故障识别效果和泛化性能,具有一定的工程应用价值。

基于空洞卷积和增强型多尺度特征自适应融合的滚动轴承故障诊断

传统卷积神经网络(CNN)在识别故障类型时存在从原始振动信号中提取特征不足以及提取特征过程中需要更大的感受野以充分捕获信号的时间相关性的局限.针对轴承振动信号固有的多尺度特征,提出基于空洞卷积和增强型多尺度自适应特征融合的模型(DC-MAFFM).利用空洞卷积的大感受野提取信号特征,同时引入残差连接来减少卷积层上的信息损失,从而有效过滤信号中的噪声;设计改进的多尺度特征提取模块,在不同尺度上捕获互补的诊断特征,同时在各层都进行不同尺度特征融合,充分学习信号的高频和低频特征;利用提出的特征自适应融合模块对不同尺度的特征自适应赋予权重,增强判别特征学习的能力.在2个轴承数据集上进行验证,结果表明所提模型在噪声和变工况下有较强的诊断能力.在强噪声情况下,故障诊断准确率分别达到88.08%和75.56%,与其他方法相比有显著优势.

基于多尺度特征互补和聚合约束的肺结节分类方法

肺结节分类问题是早期肺癌检测与诊断的重要问题之一,针对现有的肺结节分类方法存在多尺度特征融合的信息冗余和缺乏判别性特征表示等问题,提出了一个基于多尺度特征互补与聚合约束(Multi-scale Feature Complementation and Aggregate Constraint, MFCAC)的肺结节分类方法,并提出了多尺度特征互补模块用于学习相邻尺度特征的差异信息,从而避免特征融合过程中的信息冗余;同时在网络特征层引入了聚合约束损失,实现对同类特征的聚集,提高网络判别性特征表示能力;将两个模块融入在编码器-解码器架构中形成MFCAC,共同作用实现高效分类。本文在LIDC-IDRI数据集上进行了对比实验,并通过消融实验分析了该方法中各组成部分的贡献和影响,结果表明,相较于对比算法,MFCAC在肺结节分类上具有更优的性能。

基于双注意力机制的MSCN-BiGRU的滚动轴承故障诊断方法

针对滚动轴承故障诊断模型在变工况和环境噪声干扰下诊断精度降低的问题,提出一种基于双注意力机制的多尺度卷积网络(dual attention and multi-scale convolutional networks,DAMSCN)与改进的双向门控循环单元(bidirectional gated recurrent unit,BiGRU)组成的故障诊断模型DAMSCN-BiGRU。首先,多尺度特征融合模块使用不同大小的卷积核,获得多种感受野,从而提取到轴承原始振动信号的多尺度特征信息,并根据重要性对其进行自适应融合,然后利用通道注意力和空间注意力组成的双注意力模块(dual attention module,DAM)对多尺度特征进行重新标定,分配注意力权重,削弱融合特征中的冗余特征;然后,增加注意力层和利用分段激活改进BiGRU进而挖掘信号的时域特征,以提高轴承故障诊断的性能;最后,通过Softmax层完成对不同故障的分类。试验结果表明,与其他智能诊断模型相比,DAMSCN-BiGRU在变工况环境下,平均诊断精度达到98.2%,在强噪声背景下仍然有着85.3%的准确率,且在不同程度的噪声强度下效果均优于其他常用模型,有利于促进滚动轴承的智能故障诊断研究和实际应用。

液压泵和滚动轴承多种样本量的改进多任务故障诊断

基于充足样本的多个设备元件导致多任务学习网络规模庞大,轻微和严重的跨元件零样本问题难度大。在多种样本量(充足样本和零样本)下,针对基于充足故障样本的多元件诊断网络规模过于庞大问题,引入MicroNet方法对多任务学习网络进行轻量化处理,然后利用热重启余弦退火算法优化上述网络,提出一种多任务轻量化学习网络模型,改善多任务学习网络的准确率和效率。针对更高难度的跨元件零样本问题,引入元学习方法进一步改进上述MT-MN-CA,进而提出一种改进多任务轻量化学习网络模型,解决轻微和严重的跨元件零样本问题。通过实测液压泵和滚动轴承故障验证所提两个网络模型的有效性和优越性,试验结果表明所提网络具有很高的实时性和准确率。

基于多尺度特征匹配的矿井排水系统故障诊断算法

矿井排水系统的高效运行对于煤矿生产具有重要意义,然而,故障的发生会导致排水系统运行异常,进而影响生产安全和效率。提出了一种基于多尺度特征匹配的矿井排水系统故障诊断算法。该算法首先通过传感器获取到实时的矿井排水系统数据,并对其进行预处理和特征提取。然后,采用多尺度特征匹配方法,将数据特征映射到不同的尺度空间中。在每个尺度空间中,通过构建特征匹配模型,将当前数据与已知的正常运行数据进行比对,以判断是否存在故障。为了提高故障诊断的准确性和鲁棒性,引入集成学习方法,通过集成多个尺度空间的特征匹配结果,并利用投票机制进行综合决策,最终确定矿井排水系统的故障类型和位置。研究表明:所提算法在矿井排水系统故障诊断方面具有良好性能,与传统方法相比,该算法能够高效地检测故障,有助于及时准确地发现和解决矿井排水系统问题,对于确保矿井安全生产具有一定的意义。