改进集成深层自编码器在轴承故障诊断中的应用

针对滚动轴承振动信号故障特征难以自动提取和故障类别难以自动准确识别的问题,提出一种改进集成深层自编码器(IEDAE)方法.首先,改进自编码器的损失函数并设计3种小波卷积自编码器;其次,利用区分自编码器、小波卷积自编码器等5种自编码器构造相应的深层自编码器,并设计“跨层”连接以缓解深层网络的梯度消失现象,实现对轴承振动信号的无监督预训练和有监督微调;最后,通过加权平均法输出识别结果,以保证诊断结果的准确性和稳定性.实验结果表明,改进集成深层自编码器方法能有效地对滚动轴承进行多种工况和多种故障程度的识别,较好地摆脱了对人工特征提取的依赖,特征提取能力和识别能力优于现有其他方法.     

基于压缩感知和深度小波网络的列车故障识别

针对列车走行部故障振动数据无监督特征学习的难点，提出了一种基于压缩感知和深度小波神经网络（CS-DWNN）的列车故障识别方法。首先，对采集得到的列车走行部振动信号利用高斯随机矩阵进行压缩采样；其次，构建以改进小波自编码器（WAE）为基础的深层小波网络，将压缩后的信号直接输入网络进行自动逐层特征提取；最后，用DWNN学习到的多层特征分别训练多个深度支持向量机（DSVM）和深度森林（DF）分类器，并将识别结果进行集成。该方法利用深层小波网络从压缩信号中自动挖掘隐藏的故障信息，受先验知识和主观影响较小，并且避免了复杂的人工特征提取过程。实验结果表明，CS-DWNN方法取得了99.16%的平均诊断正确率，能够有效识别列车走行部的3种常见故障，识别能力优于传统的人工神经网络（ANN）、支持向量机（SVM）等方法和深度信念网络（DBN）、堆栈降噪自编码器（SDAE）等深度学习模型。     

基于深度迁移自编码器的变工况下滚动轴承故障诊断方法

在实际工业场景下的轴承故障诊断,存在轴承故障样本不足,训练样本与实际信号样本存在分布差异的问题;文章提出一种新的基于深度迁移自编码器的故障诊断方法FS-DTAE,应用于不同工况下的轴承故障诊断;该方法首先采用小波包变换进行信号处理与特征提取;其次,采用提出的基于朴素贝叶斯与域间差异的特征选取(FSBD)方法对统计特征进行评估,选取更有利于跨域故障诊断和迁移学习的特征;然后,利用源域特征数据训练深度自编码器,将训练得到的模型参数迁移至目标域,再利用目标域正常状态样本对深度迁移自编码器模型进行微调,微调后的模型用于目标域无标签特征数据的故障分类;最后,基于CWRU轴承故障数据开展不同工况下故障诊断实验,结果表明,所提出的FS-DTAE方法能够有效提高不同工况下的故障诊断准确率。     

基于深度小波去噪自动编码器的轴承智能故障诊断方法

针对原始振动数据无监督特征学习问题,提出一种深度小波去噪自动编码器与鲁棒极限学习机相结合的滚动轴承的智能故障诊断方法。利用小波函数作为非线性激活函数设计小波去噪自动编码器,从而有效地捕获信号特征;利用多个小波去噪自动编码器构造一个深度小波去噪自动编码器来增强无监督特征学习能力;采用鲁棒极限学习机作为分类器,对不同的轴承故障进行分类识别。对实验所得的轴承振动信号进行对比分析,结果验证了该方法在原始振动数据无监督特征学习的条件下优于传统方法和标准深度学习方法。     

基于栈式稀疏自编码器的矿用变压器故障诊断

鉴于将深度学习应用于变压器故障诊断具有良好的故障诊断效果,提出了一种基于栈式稀疏自编码器的矿用变压器故障诊断方法。通过在自编码器隐含层引入稀疏项限制构成稀疏自编码器,再将多个稀疏自编码器进行堆叠形成栈式稀疏自编码器,并以Softmax分类器作为输出层,建立矿用变压器故障诊断模型;利用大量无标签样本对模型进行无监督预训练,并通过有监督微调优化模型参数。算例分析结果表明,与栈式自编码器相比,栈式稀疏自编码器应用于矿用变压器故障诊断具有更高的准确率。     

基于动态卷积多层域自适应的轴承故障诊断

现有基于深度学习的轴承故障诊断方法对数据具有一定的依赖性,要求训练数据与测试数据具有相同的分布。在变工况的条件下,网络模型的故障诊断精度会因数据分布发生变化而下降。为保证网络模型能够在变工况条件下对轴承的健康状态进行准确识别,基于无监督域自适应理论,提出一种新颖的智能故障诊断网络模型——动态卷积多层域自适应网络。该网络一方面充分利用动态卷积强有力的特征提取能力,提取更多有效的故障特征;另一方面采用相关对齐实施非线性变换,同时对齐多层故障特征分布的二阶统计量,促进源域的诊断知识向目标域迁移,提高了模型在目标域无故障标签条件下的故障识别准确率。最后,在两个数据集共14个迁移任务下进行实验,实验结果表明,动态卷积多层域自适应网络能够实现较高的故障诊断识别精度。     

基于深度特征聚类和RNN的电网故障诊断

针对传统方法难以利用大量时序数据和无标签数据对电网进行故障诊断的问题,提出了基于深度特征聚类和循环神经网络(RNN)的电网智能故障诊断方法.该方法首先利用卷积神经网络搭建起特征提取器来提取时序数据的高层特征,然后对提取的特征进行半监督聚类,为无标签样本获得对应的标签,从而可以确定无标签样本所属的故障类别并加以利用;然后...     

基于深度神经网络的提升机轴承故障诊断研究

为了对矿井提升机的轴承故障进行精确诊断,提出一种基于深度神经网络的双层次故障诊断系统,精准识别提升机轴承的故障类型及故障程度。该系统首先利用滑动窗口重叠采样技术进行数据增强,随后结合自编码器减少噪声影响,通过反向传播算法训练深度神经网络双层分类器识别出故障模式及故障程度,最后用集成学习投票法进一步提高识别准确率。实验结果表明,该系统诊断准确率高于SVM与BPNN算法,可以完成提升机轴承的故障诊断任务。     

应用深度自编码网络的网络安全态势评估

针对BP神经网络类方法对标签数据的依赖性缺陷,提出了一种基于深度自动编码网络的态势评估方法。模型应用深度自动编码器作为基本单元构建深度自编码网络,结合专家经验和层次化评估的方法训练深度自编码网络。利用无标签数据采用无监督逐层算法对网络进行预训练,确定网络各层参数及权值的范围空间。在此基础上,采用有监督算法使用有标签样本对网络进行微调,对各层参数及权值进行优化,最终形成具有对输入态势数据进行准确评估能力的模型。多种样本数量条件下的对比实验表明,相对于BP神经网络类方法,基于深度自动编码网络模型受标签的影响较小,明显减少了对专家经验的依赖,并且具有整体上较高的评估精度。     

基于边际Fisher深度自编码器的电台指纹特征提取*

针对在少量有标签样本条件下传统方法训练不充分而且难以准确提取通信电台指纹特征的问题,文中提出基于边际Fisher深度自编码器的电台指纹特征提取算法.以深度自编码器为基础,训练过程分为无监督预训练、基于边际Fisher映射的有监督训练两部分.首先挖掘海量无标签样本中包含的电台个体类别信息,用于深度自编码器最优参数训练.然后在有标签样本的辅助下对训练参数进行基于边际Fisher映射的有监督精校,提高指纹特征对同类型电台个体的鉴别能力.在多个通信电台数据集上进行的分类识别实验表明,文中算法能在小样本训练条件下有效表达同类型通信电台个体之间的差异.     

Transfer reinforcement learning method with multi-label learning for compound fault recognition

In complex working site, bearings used as the important part of machine, could simultaneously have faults on several positions. Consequently, multi-label learning approach considering fully the correlation between different faulted positions of bearings becomes the popular learning pattern. Deep reinforcement learning (DRL) combining the perception ability of deep learning and the decision-making ability of reinforcement learning, could be adapted to the compound fault diagnosis while having a strong ability extracting the fault feature from the raw data. However, DRL is difficult to converge and easily falls into the unstable training problem. Therefore, this paper integrates the feature extraction ability of DRL and the knowledge transfer ability of transfer learning (TL), and proposes the multi-label transfer reinforcement learning (ML-TRL). In detail, the proposed method utilizes the improved trust region policy optimization (TRPO) as the basic DRL framework and pre-trains the fixed convolutional networks of ML-TRL using the multi-label convolutional neural network method. In compound fault experiment, the final results demonstrate powerfully that the proposed method could have the higher accuracy than other multi-label learning methods. Hence, the proposed method is a remarkable alternative when recognizing the compound fault of bearings.     

Intelligent fault diagnosis in microprocessor systems for vibration analysis in roller bearings in whirlpool turbine generators real time processor applications

Large steam turbines used for electrical power generation demand governing systems of very high integrity (safety) and availability. The latest generation of electronic governors uses microprocessors in a distributed, two level architecture to achieve the required integrity and availability and in addition provides greater configuration flexibilities and wider facilities than earlier governors. Rolling element bearings are one of the major machinery components used in industries like power plants, chemical plants and automotive industries that require precise and efficient performance. Vibration monitoring and analysis is useful tool in the field of predictive maintenance in small hydro electric power plants. Health of rolling element bearings can be easily identified using vibration monitoring because vibration signature reveals important information about the fault development within them. Numbers of vibration analysis techniques are being used to diagnosis of rolling element bearings faults. This paper proposes a new signal feature extraction and fault diagnosis method for fault diagnosis of low-speed machinery. Initially, the proposed work explores the Continuous Wavelet Transform (CWT) to adaptively remove the exact noises from vibration analysis and then feature extraction is performed by exploiting the noise removed pre-processed data. Statistic filter (SF) and Hilbert transform (HT) are combined with moving-peak-hold method (M-PH) to extract features of a fault signal, and Special bearing diagnostic symptom parameters (SSPs) in a frequency domain that are sensitive to bearing fault diagnosis are defined to recognize fault types. The SF is first used to adaptively cancel noises, and then fault detection is performed by exploiting the optimum symptom parameters in a time domain to identify a normal or fault state. For precise diagnosis, the SSPs are calculated after the signals are processed by M-PH and HT.     

基于迁移QCNN的孪生网络轴承故障诊断方法

轴承故障诊断对于降低旋转机械的损坏风险,进一步提高经济效益具有重要意义。深度学习在轴承故障诊断中应用广泛,但是深度学习模型在训练与测试时容易受到噪声的干扰导致性能下降。并且轴承的工况变化频繁,不同工况下的数据采集困难。对此,提出了一种基于迁移QCNN的孪生网络轴承故障诊断方法,先预训练QCNN获取具有较强判别性的模型参数,将预训练的参数迁移到QCNN作为子网络的孪生网络中,然后正常训练孪生网络获取模型,最后将测试数据与故障数据组成数据对输入模型,即可得到测试数据的故障类型。该方法将QCNN与孪生网络相结合,QCNN中的Quadratic神经元具有强大的特征提取能力,孪生网络共享权重和相对关系的训练方式,使得模型可以缓解噪声和工况数据不平衡问题的影响。实验结果显示,相较与传统机器学习模型和QCNN等模型,所提出方法在面对噪声和工况数据不平衡问题表现更好。     

改进CNN-LSTM模型在滚动轴承故障诊断中的应用

滚动轴承的运行状态对整机工作状态影响重大,但目前其故障诊断方法存在依赖手工特征提取、鲁棒性不高等问题.因此,本文提出了一种基于改进的一维卷积神经网络(1D-CNN)和长短期记忆网络(LSTM)集成的滚动轴承故障诊断方法 (1D-CNN-LSTM).首先,利用改进的1D-CNN-LSTM模型对滚动轴承6种不同的工作状态进行了分类识别实验,实验结果表明提出的分类模型能够以较快的速度识别出滚动轴承的不同状态,平均识别准确率达99.83%;其次,将提出的模型与部分传统算法模型进行对比实验,结果表明所提方法在测试精度方面有较大优势;最后,引入迁移学习测试模型的鲁棒性和泛化能力,实验结果表明提出的改进模型在不同工况下有较好的适应性和高效性,模型有较强的泛化能力,具备工程应用的可行性.     

基于小波包-AR谱和深度学习的轴承故障诊断研究

针对轴承故障信号的非平稳性和非线性特点,本文采用小波包分解和自回归(auto-regressive,AR)谱估计相结合的方法提取振动信号有效特征值,为了提高诊断结果的精度,提出用深度信念网络(deep believe network,DBN)进行诊断模型训练。首先通过对轴承振动信号进行小波包分解和自回归谱估计,累加不同频段的能量实现轴承故障特征提取,然后将提取到的特征值作为深度信念网络的输入向量,进行模型训练,最后用训练好的模型进行轴承故障诊断。为验证本文所提方法的有效性,采用美国凯斯西储大学提供的旋转轴承数据集进行实验。在输入相同数据集的前提下,对比了DBN和灰色关联分析(grey relational analysis,GRA)、支持向量机(support vector machine,SVM)、反向传播神经网络(back propagation neural network,BPNN)这四种方法的识别准确率。结果表明DBN识别效果较好。从而验证了本文所提方法的可行性和有效性。     

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.     

Alternative multi-label imitation learning framework monitoring tool wear and bearing fault under different working conditions

Bearings and tools are the important parts of the machine tool. And monitoring automatically the fault of bearings and the wear of tools under different working conditions is the necessary performance of the intelligent manufacturing system. In this paper, a multi-label imitation learning (MLIL) framework is proposed to monitor the tool wear and bearing fault under different working conditions. Specially, the multi-label samples with multiple sublabels are transformed into the imitation objects, and the MLIL develops a discriminator and a deep reinforcement learning (DRL) to imitate the feature from imitation objects. In detail, the DRL is implemented without setting the reward function to enhance the feature extraction ability of deep neural networks, and meanwhile the discriminator is used to discriminate the generations of DRL and imitation objects. As a result, the MLIL framework can not only deal with the correlation between multiple working conditions including different speeds and loads, but also distinguish the compound fault composed of coinstantaneous bearing fault and tool wear. Two cases demonstrate jointly the imitation ability of the MLIL framework on monitoring tool wear and bearing fault under different working conditions.